BNUS ANNUAL REPORT-2020

BANGLADESH
NETWORK OFFICE FORURBAN SAFETY
BUET, DHAKA, BANGLADESH
Edited By:

Mehedi Ahmed Ansary

April 2021
1

CONTENTS
PART-I: CYCLONE AMPHAN, 20TH MAY 2020
3
PART-II: CPT AND SPT TESTS IN ASSESSING LIQUEFACTIONPOTENTIAL
6
PART-III: WEBINAR ON EXPERT CONSULTATION ON BUILDING
EARTHQUAKES RESILIENT COMMUNITIES AND SOCIETIES FORBANGLADESH: SHARING EXPERIENCES AND LESSON LEARNED FROM
EARTHQUAKES AROUND THE WORLD
23
PART-IV: STRONG GROUND MOTION IN BANGLADESH AND NORTHEAST INDIAN REGION FROM 2005 TO 2017 AND ITS PREDICTION OF
ATTENUATION DATA DURING FUTURE EARTHQUAKES
31
PART-V: PEOPLE’S AWARENESS, KNOWLEDGE AND PERCEPTIONINFLUENCING EARTHQUAKE VULNERABILITY OF A COMMUNITY: A
STUDY ON WARD NO. 14, MYMENSINGH MUNICIPALITY,BANGLADESH
58
PART-VI: ANALYSIS AND SEISMIC RETROFITTING PROCEDURE OF
UNREINFORCED MASONRY BUILDING-A CASE STUDY93
PART-VII: INTEGRATION OF EARTHQUAKE RISK SENSITIVITY IN
LANDUSE PLANNING: AN APPROACH FOR AN AREA UNDER
117
DEVELOPMENT AT LOCAL LEVEL
PART-VIII: ASSESSMENT OF RIVER BANK EROSION AND CHANNELSHIFTING OF PADMA RIVER USING MULTITEMPORAL SATELLITE
156
DATA AND GIS TECHNIQUE
PART-IX: GIS-BASED LANDSLIDE SUSCEPTIBILITY MAPPING USINGANALYTICAL HIERARCHY PROCESS (AHP) IN BAGHAICHHARIUPAZILA, RANGAMATI
177

2

PART-ICYCLONE AMPHAN, 20th MAY, 2020

BANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Maruf Billah
Mehedi Ahmed Ansary3A powerful cyclone Amphan that tore through India’s eastern state of West Bengal and Bangladesh
this week causing a damage of $14 billion) to infrastructure and crops, state officials said. The twocountries have lost at least 102 people in the cyclone, the most powerful in over a decade, mostly
because of house collapses and electrocution. More than 3 million people were evacuated before
Amphan made landfall, preventing a large number of deaths. The cyclone has affected more than 13
million people - some losing houses, crops and lands - and over 1.5 million houses have beendamaged.
The cyclone also destroyed farmland in Bangladesh’s low-lying coastal areas, damage that will likely
endanger livelihoods,

With a wind speed of up to 245kmph, Amphan (Thai word meaning ambition) was located 940km
off the coast at 9:00pm on May 19. It was formed in the Bay of Bengal on May 17. GoB has 4,071
permanent shelters, where around 51 lakh people were accommodated. It took seven to eight hours toevacuate 20 lakh people from their homes and take them to the shelters.
According to the Met office, the coastal districts of Satkhira, Khulna, Bagerhat, Jhalakathi, Pirojpur,
Barguna, Patuakhali, Bhola, Barishal, Laxmipur, Chandpur, Noakhali, Feni, Chattogram and theirshore islands and chars have suffered the worst of the storm,

4

5

PART-II

CPT AND SPT TESTS IN ASSESSING
LIQUEFACTION POTENTIAL

BANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Ripon Hore
Sudipta Chakraborty
Mehedi Ahmed Ansary6

1.

INTRODUCTION

Liquefaction problem has become important when it started to affect human and social activities by
disturbing the function of facilities and also after rapid urbanization by expanding the cities inreclaimed areas. Ground failures generated by liquefaction has been a major cause of damage during
past earthquakes e.g., 1964 Niigata, Japan and 1964 Alaska, USA, 1971 San Fernando, 1989 Loma
Prieta, 1995 Kobe, Japan and 2004 Chuetsu, Japan earthquakes. Liquefaction affects buildings,
bridges, buried pipelines and lifeline facilities etc. in many ways.
The historical seismicity data and recent seismic activities in Bangladesh and adjoining areas
indicate that Bangladesh is at high seismic risk. As Bangladesh is the world’s most densely
populated area, any future earthquake shall affect more people per unit area than other seismically
active regions of the world. Bangladesh including capital city Dhaka is largely an alluvial plain
consisting of fine sand and silt deposits with shallow ground water table in most places. Although theolder alluvium is less susceptible to liquefaction, the deposits along the river flood plains may
liquefy during a severe earthquake. Human made soil deposits also deserve attention. Loose fills,
such as those placed without compaction, are very likely to be susceptible to liquefaction.
Over the past 30~40 years Dhaka city has experienced a rapid growth of urban population and it willcontinue in the future due to several unavoidable reasons. This high population increase demands
rapid expansion of the city. Unfortunately, most parts of Dhaka city has already been occupied. As aresult, new areas have been reclaimed by both government and private agencies in and around Dhaka
city. In many cases, the practice for developing such new areas is just to fill lowlands of the depth
3~12m with dredged material consisting of silty sand. This causes liquefaction susceptibility for suchareas.
Seed and Idriss (1971) have presented test procedures for measuring soil liquefaction characteristics
as summarized in Table 1. Their proposed basic simplified method to evaluate liquefaction potential
has been modified and improved by Seed et al. (1983); Youd and Idriss (2001); Boulanger and Idriss(2004); Cetin et al. (2004) as described in Table 1.
Seed et al. (1983) has evaluated liquefaction potential using field test data. They used simplified
procedure for evaluating the liquefaction potential of sand deposits using data obtained fromStandard Penetration Test (SPT). Field data for sites which have been known to be liquefied or notliquefied during earthquakes in the United States, Japan, China, Guatemala, Argentina and othercountries have been presented to establish a criterion for evaluating the liquefaction potential ofsands in Magnitude 7.5 earthquakes. Then the results of this study have been extended to othermagnitude earthquakes using a combination of laboratory and field tests data.

7

Gratchev et al. (2006) have conducted research to investigate the liquefaction potential of clayeysoils under cyclic loading. This research seeks to investigate the liquefaction of clayey soils, aphenomenon that has been the trigger for many natural disasters in the last few decades, including
landslides. Research has been conducted on artificial clay-sand mixtures and natural clayey soils
collected from the sliding surfaces of earthquake-induced landslides.
Robertson (2009) has developed cone penetration test (CPT) based relationships to evaluate thesusceptibility to strength loss and liquefied shear strength for a wide range of soils based on casehistories as stated in Table 1. The case histories has indicated that very young, very loose, nonplastic
or low-plastic soils tend to be more susceptible to significant and rapid strength loss than older,denser, and/or more plastic soils. The CPT is a useful in situ test that can provide continuous
estimates of the potential for flow liquefaction. For low risk projects, the CPT-based method isappropriate when combined with selective samples to confirm the soil type as well as conservative
estimates of soil response. For moderate risk projects, the CPT-based method should be combinedwith appropriate additional in situ testing, as well as selected undisturbed sampling and laboratory
testing, to confirm soil response. For high risk projects, the CPT-based method should be used as an
initial screening to identify the extent and nature of potential problems has been followed byadditional in situ testing and appropriate laboratory testing on high-quality samples. An advanced
numerical modeling is appropriate for high risk projects where initial screening indicates a need.
The SPT based liquefaction charts are commonly been used for determining liquefaction potential.
Simplified method given by Seed, Tokimatsu and Yoshimi (Y-T) and Idriss and Boulanger (2008)
methods of liquefaction assessment have been analyzed by Kumar et al. (2012) as presented in Table
1. Computational methods like artificial neural network (ANN) and neuro-fuzzy technique (NF) has
been also discussed as capable for liquefaction assessment using database either from SPT or CPT
results. Some pertinent soil properties along with seismic characteristics may help in modeling andanalyzing liquefaction potential of prone sites. The major advantage of computational methods is theability to associate both SPT and CPT indicator properties for better engineering judgment toevaluate site dependent liquefaction.
Sesov et al., (2012) has presented (Table 1) that the investigations and results on the evaluation of
the potential of liquefiable soil layers at location where new industrial complex has been planned to
be built in southern part of Republic of Macedonia. Investigations combine the results from different
in-situ methods, site response analysis and laboratory cyclic triaxial undrained tests. For the purposeof the new industrial complex which has been planned to be built at, first preliminary evaluation of
the liquefaction potential has been done with SPT and CPT methods. Results have indicated that forthe soil layers where cyclic resistance ratio is lower than the expected cyclic stress ratio might8

behave as liquefiable. Soil samples have been taken from the boreholes of the site and cyclic
undrained triaxial tests have been done.
Guettaya et al. (2013) has presented a case study of liquefaction potential assessment for thefoundation of an earth dam in Tunisia. An emphasis has been made on the exploration of
geotechnical conditions and the interpretation of field tests (SPT and CPT) and the results have beencollected before and after soil densification using the vibro-compaction technique. The SPT
resistance values has been increased on average from 12 to 25 blow counts/0.3m, and the CPT
resistance has been increased on average from 8MPa to 14MPa. Before vibro-compaction, the factorof safety (FS) against liquefaction fell below 1.0, which means that the soil is susceptible forliquefaction. After vibro-compaction the values of FS exceed the unit which justified the liquefaction
mitigation efforts in dam foundation.
Ecemis and Karaman (2014) has performed (Table 1) a set of four high-quality field tests at 20
different locations on the Northern coast of the Izmir Gulf: (1) piezocone penetration test (CPTu), (2)pore pressure dissipation test (PPDT), (3) direct push permeability test (DPPT), and (4) standard
penetration test (SPT). The total sounding depth for each test is about 15 m. Uncertainties prevail atthe current liquefaction screening method based on the cone penetration test (CPT) as to whether the
existence of fines increases liquefaction resistance or decrease cone penetration resistance. Fieldbased data have been used to evaluate the effects of non-/low plastic fines on liquefaction resistanceat the current CPT-based liquefaction assessment method. The liquefaction resistance of sands andsilty sands has been reinterpreted from the current CPT-based liquefaction assessment method. The
trend, which presents the change of liquefaction resistance with fines content at the same relative
density, has been compared with the available laboratory-based data in the literature.
Ndoj et al., (2015) have evaluated the liquefaction, based on the data collected by Piezocone Test
(CPTU) and Standard Penetration Test (SPT) as shown in Table 1. These data have been used toevaluate the liquefaction for a case study, in a coastal area of Albania, located in from Lalezi Bay toHamallaj area, near Durresi city, where several residential buildings and resorts useful during thesummer season are foreseen to be built. The site investigation has been carried out with 8 CPTU tests
until a maximal depth of 20 m. According to this study, in this area sands, gravelly sands, silty sandsand clays are present. The water level is located very close to ground surface, at around 0.5 - 2.0 m
under the ground surface. The highest moment magnitude registered in this area is 6.2 and it belongs
to Durresi city, for the earthquake happened in December 1926. CPT based method show that all thesoils have been classified as “like sands”, below the water level where the liquefaction may occur.
Also the SPT based method show that the liquefaction may occur in these soils, but the thickness ofliquefiable layers of soils is small.
9

Konni (2015) has been taken a live project having offshore artificial island of 84 km offshore and an
attempt has been made to assess compaction levels achieved for offshore artificial islands.
Subsequently, liquefaction potential has been evaluated based on SPT, N and CPT data assummarized in Table 1.k). According to the test result it has been found that CPT appears to be better
suited to liquefaction assessment than SPT because it is more standardized, reproducible, and costeffective and yields a continuous penetration record with depth. CPT can be quickly andeconomically identify the thick and thin liquefiable soil, which is cost-prohibitive in SPT test.
During the recent devastating earthquakes in Christchurch, many residential houses have beendamaged due to widespread liquefaction of the ground. In-situ testing has widely been used as aconvenient method for evaluating liquefaction potential of soils. Cone penetration test (CPT) andstandard penetration test (SPT) are the two popular in situ tests which are widely used in New
Zealand for site characterization. Mirjafari et al., (2016) has conducted the Screw Driving Sounding(SDS) test in Christchurch, a correlation has been developed between tip resistance of CPT test andSDS parameters for layers consisting of different fines contents. As SDS method is simpler, fasterand more economical test than CPT and SPT, it can be a reliable alternative in-situ test for soil
characterization, especially in residential house constructions as shown in Table 1.
Huang and Yu (2017) have evaluated three procedures for liquefaction potential based on in situ.
Procedure one is the assessment of “triggering” (initiation) of soil liquefaction. Soil type is very
important for assessment of this initiation. Clay content, liquid limit, and water content has been used
to evaluate the potential initiation of soil liquefaction. Procedure two is the assessment ofliquefaction resistance based on in situ tests. The methods that has been used to evaluate liquefaction
are the standard penetration test (SPT), cone penetration test (CPT), dynamic cone penetration test
(DPT), Becker penetration test (BPT), and shear wave velocity (VS) test. The liquefaction resistancehas been obtained by calculating the penetration resistance in empirical equations. Procedure three isassessment of the site liquefaction index and deformation of liquefiable sites. In this procedure, thedepth and thickness of the liquefiable soil layer has been considered. Finally, the site liquefaction
potential has been calculated by integrating all test points. Assessment of soil seismic deformation
also been introduced.
Hoque et al., (2017) has presented (Table 1.m) evaluation and comparative analysis of liquefaction
potential from Standard Penetration Test (SPT) and Cone Penetration Test (CPT) based deterministic
relationships. Both methods have significant relative advantages, and can often be optimal whenused in combinations. In this research, four pairs of SPT and CPT tests have been carried out at theriver bank of Jamuna, Bangladesh and each pair of test has been conducted as close as possible.

10The goal of this paper is to estimate the earthquake induced liquefaction potential of selected
reclaimed areas of Dhaka city and develop liquefaction potential (LPI) map based on CPT and SPT

2.

EVALUATION OF LIQUEFACTION SUSCEPTIBILITY

2.1

Evaluation Based on SPT N-Value

The most common index properties for estimating liquefaction strength is the N-value obtained fromthe standard penetration test. The standard penetration test (SPT) consists of driving a thick-walledsampler into the granular soil deposit. The measured SPT N-value (blows per foot) is defined as thepenetration resistance of the soil, which equals the sum of the number of bows required to drive the
SPT sampler over the depth interval of 15 to 45 cm (6 to 18 in).
SPT test is probably the most widely used field test in the Bangladesh as well as in the world. This isbecause it is relatively easy to use, the test is economical compared to other types of field testing, andthe SPT equipment can be quickly adapted and included as part of almost any type of drilling rig.
The most comprehensive liquefaction data catalogs are based on Standard Penetration test (SPT)
blow counts (SPT N). Starting in the 1970’s, Seed and his colleagues worked to develop a reliable
method for assessing liquefaction potential based on SPT data. Their framework for SPT-based
assessments of liquefaction potential has been developed in a series of papers that includes Seed andIdriss (1971); Seed and Idriss (1982); Seed et al. (1983)significant contributions have been also madeby Tokimatsu and Yoshimi (1983) as summarized in Table 1. This research culminated in theliquefaction criteria published by Seed et al., (1985) as described in Table 1.
The empirical chart published by Seed et al. (1983) is based on a standardized SPT blow count,(N1)60 and the cyclic stress ratio (CSR).To get (N1)60, the measured NSPT corrected for the energy
delivered by different hammer systems and normalized with respect to overburden stress. Boundary
curves separating nonliquefied from liquefied soils, in terms of CSR and (N1)60have beenconservatively drawn to nearly all observed cases of liquefaction in the data catalog. Three separate
boundary curves have been presented for clean to silty sands. To consider the effects of earthquake
magnitude on the duration of strong shaking, magnitude scaling factors has been specified. Over thelast few decades, the empirical method given by Seed et al. (1983), sometimes referred to as the“simplified procedure”, has been widely used for evaluating soil liquefaction potential in all aroundthe world.
2.2

Evaluation Based on CPT

For estimating liquefaction potential by in-situ test other than the SPT, the most advanced ones arethose using cone penetration resistances. The idea for the cone penetration test is similar to the SPT
11except that instead of driving a thick-walled sampler into the soil, a steel cone is pushed into the soil.
The force required to move the cone into the extended position divided by the horizontally projectedarea of the cone is defined as the cone resistance, qc. For liquefaction analysis, the cone penetration
test value qc is corrected for the vertical effective stress. A major advantage of the cone penetration
test is that by using the electric cone, a continuous subsurface record of the cone resistance qc can beobtained. This is in contrast to the SPT, which obtains data at intervals in the soil deposit.
Disadvantage of the CPT are that soil samples cannot be recovered and special equipment is required
to produce a steady and slow penetration of the cone.
The Cone Penetration test (CPT) yields a continuous profile of penetration resistance and is thus
well-equipped for detecting thin, liquefiable layers within a larger, stable soil deposit.

3.

SELECTED AREAS FOR THE RESEARCH

Total ten areas of the Dhaka city have been selected for this research. The main targeted areas havebeen reclaimed lands since some of these lands have been found to be susceptible to liquefaction(Hore, 2013). The reclaimed areas are Bramangaon, Ashian City, Badda, Banasree, Gabtoli, Kawran
Bazar, Purbachal, United city, Uttara and Kamrangirchar. Figure 1 shows the selected study areas.
The study area is located between the latitude 23°35΄N-23°54΄N and the longitudes 90°19΄ E90°30΄E. the expansion of the city is restricted by the Buriganga River in the south, Turag River inthe west and Balu River in the east. The city lies on the lower reaches of the Ganges Delta (Rahman
et al., 2011) as described in Table 1.
3.1

Geology of Dhaka

Geological evolution of the Bengal Basin starting from Upper Paleozoic time when Gondwana land
break up and collision the Indian plate with the Asian plate and sedimentary cover of the basin witha maximum thickness of 20 km includes three major lithostratigraphic units separated by three major
unconformities. Most of the authors have the opinion about the Madhupur tract is the tectonic
uplifted surface. Fergussion (1963) believes that the Madhupur tract is the recent uplifted surface dueto the 1762 earthquake. Dhaka city is situated in the southern half of the Madhupur Tract andFloodplain area with southern river system.

12

No.
a.

Reference
Seed and Idriss(1971)b.

Seed et al. (1983)c.

Youd and Idriss(2001)

d.

Cetin et al., (2004)e.

Robertson (2009)f.

Idrissand Boulanger(2008)g

Kumar et al.,(2012)h.

Sesov et al. (2012)i.

Ecemis andKaraman(2014)

j.

Ndoj et al. (2015)

k.

Konni (2015)l.

Mirjafari et al. (2016)m.

Hoque et al. (2017)n.

Seed and Idriss(1982)o.

Tokimatsu andYoshimi (1983)p.

Seed et al., (1985)q.

Rahman et al., (2011)r.

WASA, (1991)s.

Morganand McIntire(1959)

t.

Robertson(1990)

Table 1 Summary of previous works
Summarya) Significant factors affecting the liquefaction potential of sands during earthquake b) variable field data
concerning the liquefaction or non-liquefaction behaviour of sands during earthquakes and compared withevaluations of performance using the simplified procedure.
a) Evaluating the liquefaction potential of silty sand deposits (Field data for liquefied or not liquefied
during earthquakes in the United States, Japan, China, Guatemala, Argentina, and other countries) using
data obtained from standard penetration tests has been reviewed. b) Evaluated the possible magnitude ofpore water pressure generation during earthquake shaking. c) evaluating the liquefaction resistance of soils
by static cone penetrometer, shear wave velocity, and electrical measurementsa)In 1996 a workshop sponsored by the National Center for Earthquake Engineering Research (NCEER)
was convened by Professors T. L. Youd and I. M. Idriss with 20 experts to review developments over theprevious 10 years. b) The following topics have been reviewed and recommendations developed: (1)criteria based on SPT tests; (2) criteria based on CPT tests; (3) criteria based on shear-wave velocity
measurements; (4) use of the Becker penetration test for gravelly soil; (4) magnitude scaling factors; (5)correction factors for overburden pressures and sloping ground; and (6) input values for earthquake
magnitude and peak acceleration.
a)New correlations for soil liquefaction, b) new correlations are-accumulation of an expanded database(field performance case histories); factors affecting interpretation of SPT data; factors affecting sitespecific earthquake ground motions; d) improved methods for in situ cyclic shear stress ratio c)this paper
helps to develop magnitude-correlated duration weighting factors, adjustments for fines content, andcorrections for overburden stress.
a) Analyzing some case-histories of cone penetration test-based relationships to evaluate the susceptibility
to strength loss and liquefied shear strength for a wide range of soils.
a)Fundamentals of liquefaction behaviour for the development and limitations of various engineering
analytical procedures; b) methods for liquefaction analysis and use of factors of safety in engineering
practice, c) mitigation strategies, and methods for ground improvement; d) Cyclic softening of saturated
clays for potential performance of cohesive fine-grained soils.
a) Assessment of liquefaction of soils both in conventional and computational (ANN, NF) methods, using
database either from SPT or CPT results.
a)At southern part of Republic of Macedonia the evaluation of the potential of liquefiable soil layers has
been done where new industrial complex has been planned to be built, b) assessment of liquefaction hazard
at the location with medium to high seismicity and heterogeneous soil condition, c) potential ofliquefaction in complex geological condition with high degree of soil heterogeneity.
a)Evaluate the effects of non/low plastic fines on liquefaction resistance at the current CPT liquefaction
method. b) Examines the effects of the coefficient of consolidation or drainage characteristics of fine soils(cone penetration resistance)c) coefficient of consolidation depends on fines content and the relative
density of the soil d) investigates the contribution of fines content (< 30% by weight) on the liquefaction
resistance of soils at different relative densities e) Fines content (>30% by weight) and/or high plasticity offines can cause additional complications.
a)CPTU tests (8) at a depth of 20 m, b) soil profiles contain sands, gravelly sands, silty sands and clays b)
evaluation of liquefaction in a coastal area of Albania based on the data collected by Piezocone Test
(CPTU) and SPT.
a)Liquefaction potential has been evaluated based on SPT, N and CPT data in a live project (having 84km
offshore artificial island), b) studied the reliability of the data for the liquefaction assessment.
a)Correlation developed between tip resistance of CPT test and SDS parameters for soil of different fines
contents at Christchurch (New Zealand), b) a chart was proposed which relates the cyclic resistance ratio tothe appropriate SDS parameter.
a)Evaluation and comparative analysis of liquefaction potential from SPT and CPT (four pairs) based
deterministic relationships which carried out along the river bank of the Jamuna river, Bangladesh (About
85 km from Dhaka city).
In this paper, the following areas have been covered: a) Liquefaction analysis for the methods forevaluating the potential for under ground motion during earthquake. b) Liquefaction behaviour for acommon understanding of the development and limitations of various engineering analytical procedures
under ground motion.
a) Field performance of sandy soil deposits during past earthquakes has been conducted with special
emphasis being placed on SPT N-values and fines content, b) field relationship between adjusted dynamic
shear stress ratio and normalized SPT N-values together with laboratory tests on undisturbed sands indicatethat (1) sands containing more than 10% fines has much greater resistance to liquefaction than clean sands
having the same SPT N-values, (2) extensive damage would not occur for clean sands with SPT N1-values
greater than 25, silty sands containing more than 10% fines with SPT N1-values greater than 20, or sandy
silts with more than 20% clay, and (3) sands containing gravel particles seem to have less resistance toliquefaction than clean sands without gravel having the same SPT N-values. c) an improved empirical chart
separating liquefiable and non-liquefiable conditions is presented in terms of dynamic shear stress ratio,
SPT N-values, fines content, and shear strain amplitude.
a)To clarify the meaning of the values of standard penetration resistance used in correlations of fieldobservations of soil liquefaction with values of N1 measured in SPT tests b) Liquefaction resistance curves
for sands with different (N1)60 values and with different fines contents have been proposed.
Dhaka city, the soul of Bangladesh is highly vulnerable to the earthquake disaster (caused liquefaction) due
to high density of population, unplanned infrastructure and close proximity with India and Myanmar’s
active seismic area, poor economic condition, poor emergency preparation and recovery capability.
a) The changing trend of wetlands makes the drainage system of Dhaka City vulnerable and creating water
logging problems and b) Land filling and encroachment is the main reasons for shrinking of the wetlands inthe city.
Geological condition of Bengal Basin, East Pakistan and India has been investigated in this research paper.
13 the Bengal basin on the east and west and flank Tertiary and older
Two areas of Pleistocene terrace borderhills of India. Two large inliers of Pleistocene sediments within the basin are surrounded by Recent floodplain deposits of the Ganges and Brahmaputra rivers and their combined deltaic plain.
a) A new system has been proposed based on normalized CPT data, b) new charts are based on extensive
data (from a 300 m deep borehole with wire-line CPT) available from published and unpublishedi
ld idRegional elevation of the area gradually declines towards Buriganga River on the south and theelevation ranges from 10m to 17m but is generally around 14m above mean sea level. Geologically
Dhaka city belongs to Bengal Foredeep and is situated in the Pleistocene uplifted block (Madhupur
Tract) within the passive margin surrounded by subsiding floodplains bounded on the west by aseries of NW-SE trending en-echelon faults including the Dhamrai, Maijail and Kaliakoir ones
(WASA, 1991; Morgan and McIntire,1959) as summarized in Table 1.r) and s) . Land surface iscovered by gray floodplain and non-Cretaceous floodplain soils. Stratigraphically, Old Dhaka ischaracterized by hundreds of meters thick unconsolidated sequence of fluvio-deltaic deposits many
composed of gravels, sands, silts and clays of Plio-Pleistocene age. In this paper, subsoil
characteristics of two sites (namely Bramangaon and Ashian City have been elaborated.
Soil Parameters
0

2

4

6

Soil Parameters
8

10

0

2.5

2.5

5

5

7.5

7.5
Depth (m)0Depth (m)

0

10

50

75

100

125

f (kPa)
N60

10

12.5

12.5
15

25

15
qt (Mpa)
Friction Ratio

17.5

17.5

20

20

22.5

22.5

25

25

Figure 2 Depth (m) vs. Cone Resistance qt (MPa), Friction(kPa), N60 and Depth vs. friction ration (kPa) at Bramangaon

Figure 1: Geological Map showing study areas of Dhaka city.

3.2

Sub- Soil characteristics of Bramangaon

This site has been situated in southeast part of Dhaka city. It is a private land development project
where main filing has been done by dredged river sand. The depth of filling of fine sand is 5.0 m
from existing ground level. The clayey silt layer exists from 5.0 m to 6.5 m from EGL. After that 1.5
m is sandy silt layer. Then 12.0 m is clayey silt. The uncorrected SPT N value of filling fine sandvaries from 4 to 5. The SPT N value of clayey silt layers varies from 5 to 8. The maximum value of
SPT N is 8. The minimum value of SPT N is 4. From the CPT test, the cone resistance varies from
0.51 to 16.78 MPa. The average value of cone resistance is 3.88 MPa. Friction varies from 0 to 120.2
14

kPa. The average value of Friction is 30.79 kPa. Friction ratio varies from 0 to 4.13. The averagevalue of Friction ratio is 1.11.The SPT N values of the boreholes have been shown in the Table 2.
Figure 2 shows Depth (m) vs. Cone Resistance qt (MPa), Friction (kPa), N60 and Depth vs. friction
ration (kPa) at Bramangaon. From the CPT test, the cone resistance varies from 0.51 to 16.78 MPa.
The maximum value of cone resistance is 16.78 MPa. The minimum value of cone resistance is 0.51
MPa. The average value of cone resistance is 3.88 MPa. Friction varies from 0 to 120.2 kPa. The
average value of friction is 30.79 kPa. Friction ratio varies from 0 to 4.13 kPa. The average value ofFriction ratio is 1.11 kPa. Results from grain size analysis of the soil samples have been presented inTable 2. The mean grain size (d50), fine content (Fc) of filling sand varies 0.16 to 0.17 mm, 20 to
21% respectively. The mean grain size (d50), fine content (Fc) of silt is 0.046 mm, 71% respectively.
Table 3 shows Probable soil classification using CPT data (Robertson, 1990) (as shown in Table 1)
at Bramangaon.
Table 2 SPT N value and grain size analysis at Bramangaon
Depth(m)
1.5
3
4.5
6
7.5
9
10.5
12
13.5
15
16.5
18
19.5

Descriptionof Soil
Filling Sand
Clayey Silt
Sandy silt

Clayey Silt

SPT N
Value
4
5
5
7
8
5
5
5
4
5
6
7
8

Fc (%)

d50 (mm)

20

0.17

71
51

0.046
0.076

71

Table 3 Probable soil classification using CPT data (Robertson,1990) at Bramangaon

Depth Range(m)

IC Range

Probable Soil
Classification

0‐3

0.82‐1.52

Sand

3‐6

1.22‐1.92

Sand

6‐9

1.77‐2.82

Sand/Silt

9‐12

2.34‐3.24

0.046

3.3 Sub-Soil Characteristics of Ashian City
This site has been situated in Northern part of Dhaka city. The depth of filling of fine sand is 3.5 mfrom existing ground level. The silty clay layer exists from 3.5 m to 12.5 m from EGL. After that 4.5
m is fine sand layer. Then 3.0 m is silty clay. The uncorrected SPT N value of filling fine sand varies
from 4 to 5. The SPT N value of silty clay layers varies from 3 to 7. The maximum value of SPT N is
42. The minimum value of SPT N is 3. Figure 3 shows Depth (m) vs. Cone Resistance qt (MPa),
Friction (kPa), N60 and Depth vs. friction ration (kPa) at Asian City. From the CPT test, the cone
resistance varies from 0.195 to 7.805 MPa. The average value of cone resistance is 1.78 MPa.
Friction varies from 0 to 233.9 kPa.
The average value of Friction is 31.21 kPa. Friction ratio varies from 0 to 6.35 kPa. The averagevalue of Friction ratio is 1.29 kPa.
15

SPT has been conducted in the area following procedure described in ASTM D1586. The SPT N
values of the boreholes have been shown in the Table 4. The graph between depth vs N value has
been shown in Figure 3.
The graph between depth vs friction and cone resistance have been shown in Figure 3. From the CPT
test, the cone resistance varies from 0.195 to 7.805 MPa. The maximum value of cone resistance is7.805 MPa. The minimum value of cone resistance is 0.195 MPa.
Results from grain size analysis of the soil samples have been presented in Table 4. The mean grain
size (d50), fine content (Fc) of filling sand varies 0.16 to 0.17 mm, 20 to 21% respectively. The
mean grain size (d50), fine content (Fc) of clay varies 0.002 to 0.003 mm, 94 to 96% respectively.
The mean grain size (d50), fine content (Fc) of sand is 0.16 mm, 21% respectively. Table 5 shows
the probable soil classification using CPT data (Robertson, 1990) at Ashian City.

4. LIQUEFACTION POTENTIAL ANALYSIS BASED ON SPT AND CPT
The main objective of this study is to present the liquefaction potential of the selected reclaimed
areas. Soil characteristics of the selected reclaimed areas have been determined by field andlaboratory tests. Test results have been presented earlier. Liquefaction potential has been estimatedusing two methods based on CPT (Robertson, 2009) and SPT (Seed et al; 1983) data. The results ofthese estimations have been presented.

5. GROUND MOTION AND EARTHQUAKE IN AROUND DHAKA CITY
In this research, the value of amax has been taken as 0.15g as Dhaka city exist in the zone 2 ofseismic zonation map of Bangladesh (BNBC, 1993). Other researchers (Ansary and Rashid, 2000)
also used similar values of amax for the similar purpose as given in Table 1. Though at present thevalue of amax is being update by various researchers and agencies from 0.15 to 0.2. But it has notbeen taken to the consideration for this study since it has not been incorporated to BNBC yet.
Earthquake ground motion has been influenced by a number of factors. Most important factors are
moment magnitude, epicenter distances, local soil conditions, earthquake sources, etc. In Seed-Idriss
simplified procedure moment magnitude (Mw) input parameter is also important correction factor.
From Table 6, it is seen that ranges of Mw at nearby faults from Dhaka varies 7.5~8.5. However, thisvalue cannot be considered directly for Dhaka since those faults are at quite distant places from
Dhaka. Due to non-availability of attenuation law and suitable correlations between distance andground motion characteristics for Dhaka, the design moment magnitude has been taken 7.5 for thisstudy, which is the lowest value in Table 6.

16

Bangladesh covers one of the largest deltas and one of the thickest sedimentary basins in the world.
According to the report on time predictable fault modeling (CDMP, 2009), earthquake and tsunami
preparedness component of CDMP have identified five tectonic fault zones which may produce
damaging earthquakes in Bangladesh. These are Madhupur fault zone, Dauki fault zone, plateboundary fault zone -1, plate boundary fault zone -2, and plate boundary fault zone -3. Among these,
Madhupur fault zone has been considered as a source of damaging earthquake near Dhaka in thisstudy.

6. LIQUEFACTION POTENTIAL INDEX (LPI) ANALYSIS
Liquefaction potential based on CPT (Robertson, 2009) and SPT (Seed et al; 1983) data have been
estimated. Liquefiable zone is where FS<1, on the other hand non liquefiable zone is where FS>1.
The liquefaction analyses results for 10 different locations as described earlier have been presented
in Table 7.
Liquefaction potential index (LPI) has been estimated using Iwasaki et al. (1982) based on CPT dataand is shown in Figure 4. The lowest Liquefaction Potential Index (LPI) value for CPT has beenfound at Kawran Bazar which is 3 and highest value at Bramangaon which is 21. The Liquefaction
Potential Index (LPI) ofother sites like Badda, Gabtoli, United City, Ashian city, Uttara,
Kamrangirchar, Banasree and Purbachal are 10, 10, 11, 13, 16, 16, 18 and 18 respectively. Based on
Liquefaction Potential Index (LPI) for CPT test results, the Dhaka city has been divided in to threezones according to Iwasaki et al. (1982) named low, high and very high as presented in Table 1). The
low liquefaction zone has been found at the center position of the study area and spread along thenorth south direction. The high liquefaction zone has been found in the outer periphery of the study
area.
Liquefaction potential index (LPI) has also been estimated using Iwasaki et al. (1982) based on SPT
data and is shown in Figure 5. The lowest liquefaction value for SPT has been found at Kawran
Bazar which is 1 and highest value at Purbachal which is 28. The Liquefaction Potential Index ofother sites like Badda, Gabtoli, United City, Ashian city, Uttara, Kamrangirchar, Banasree andBramangaon are 10,16, 7, 9, 9, 18,14 and13 respectively. Based on Liquefaction Potential Index for
SPT test results, the Dhaka city has been divided in to three zones according to Iwasaki et al. (1982)
named low, high and very high. The low liquefaction zone has been found at the center position of
the study area and spread along the north south direction. The high liquefaction zone has been foundin the outer periphery of the study area.
These results are supported by the Geological Map presented in Figure 1 and past study by Ansary
and Rahman (2013) where microtremor survey has also been carried out as described in Table 1.
17

The similarity of liquefaction potential index map based on CPT (Figure 4) and SPT (Figure 5) datais that for both the cases, low liquefaction zone has been found at the center position of the study
area and spread along the north south direction. The very high liquefaction zone has been found in
the outer periphery of the study area. CPT based method also show very high liquefaction zone inNorthern and North-Western part of Dhaka. High liquefaction zone area is relatively larger for SPT
based map.

7

CONCLUSIONSIn this research liquefaction potential have been evaluated using procedures based on both SPT and
CPT. SPT based evaluation has been carried out by Seed-Idriss Simplified Procedure. Cone
Penetration Test (CPT) based evaluation has been carried out by the methods proposed byRobertson. Results of Liquefaction Potential slightly vary in the two methods. One of the reasons of
this variation may be due to the process of data collection. SPT N value has been obtained at each
1.5 m interval. On the other hand CPT value has been collected at each 0.01 m interval. This yields a
more reliable and continuous CPT data than SPT. Some certain zone of liquefaction which cannot berecognized by SPT clearly has been identified by CPT test results. According to the zonation map of
Dhaka city based on Liquefaction Potential Index (LPI),the low potential zone has been found along
the center and north south alignment. The high and very high liquefaction zone has been found in theouter periphery of the study area.

18

Figure 5: Liquefaction potential index Map based on SPT data

Figure 4: Liquefaction potential index Map based on CPT data

19

8

REFERENCES

Ansary, M. A.& Rashid, M. (2000). Generation of liquefaction potential map for Dhaka, Bangladesh.
Paper presented at the 8th ASCE Specialty Conference on Probabilistic Mechanics and Structural
Reliability PMC2000-061, Paris: University of Notre Dame.
Ansary, M. A. & Rahman, S. (2013). Site amplification investigation in Dhaka, Bangladesh, using
H/V ratio of microtremor, Environmental Earth Sciences, 70 (2), 559-574.
BNBC (1993), Bangladesh National Building Code, HBRI-BSTI.
Boulanger, R. W., & Idriss, I. M. (2004). Evaluating the potential for liquefaction or cyclic failure ofsilts and clays: Center for Geotechnical Modeling.
CDMP (2009), Report on time predictable fault modeling.
Cetin et al., (2004).Standard Penetration Test-Based Probabilistic and Deterministic Assessment ofSeismic Soil Liquefaction Potential.J.Geotech.Geoenviron. Eng., 130(12), 1314–1340.
Ecemis, N., &Karaman, M. (2014). Influence of non-/low plastic fines on cone penetration andliquefaction resistance. Engineering Geology, 181, 48-57.
Fergusson, J., 1963, Delta of the Ganges; Quaternary Journal of the Geological Society of London,
vol. 19, p. 321-323.
Gratehev et al. (2006).Investigation the liquefaction potential of clayey soils under cyclic loading.
Guettaya, I., El Ouni, M. R., & Moss, R. E. S. (2013). Verifying liquefaction remediation beneath anearth dam using SPT and CPT based methods. Soil Dynamics and Earthquake Engineering, 53, 130144.
Hoque, M. M., Ansary, M. A. and Siddique, A. (2017). Evaluation of Liquefaction Potential from
SPT and CPT: a Comparative Analysis. Proceedings of the 19th International Conference on Soil
Mechanics and Geotechnical Engineering, Seoul, pp. 1-5.
Huang, Y., & Yu, M. (2017). Liquefaction Potential Evaluation Based on In Situ Testing. In Hazard
Analysis of Seismic Soil Liquefaction, Springer Singapore. Chapter-3, pp. 35-59.
Hore, R. (2013). Liquefaction potential of selected reclaimed areas of Dhaka city Based on SPT and
CPT. (M.Sc Eng.), Bangladesh University of Engineering and Technology (BUET), Dhaka,Bangladesh.
Idriss, I.M. and Boulanger, R.W. (2008) Soil Liquefaction during Earthquake. EERI Publication,
Monograph MNO-12, Earthquake Engineering Research Institute, Oakland.
Iwasaki, T., Tokida, K., Tatsuoka, F., Watanabe, S., Yasuda, S., and Sato, H.(1982) Microzonation
for soil liquefaction potential using simplified methods, Proceedings of 2nd International Conferenceon Microzonation, Seattle, 1319–1330,

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Kumar, V., Venkatesh, K., and Kumar, Y. (2012) Approaches for Estimating Liquefaction Potentialof Soils. International Journal of Structural and Civil Engineering, ISSN, 2277-7032. Volume 1 Issue
2, 35-53.
Konni, G. R. (2015). Compaction Assessment and Evaluation of Liquefaction Potential of Offshore
Artificial Island Based On ‘SPT’and ‘CPT’ Data. In SPE Kuwait Oil and Gas Show andConference.Society of Petroleum Engineers, pp.1-12.
Morgan, J.P., and McIntire, W.G., 1959, Quaternary Geology of the Bengal Basin, East Pakistan andIndia; Geological Society of America, Bull. 70, p. 319-320.
Mirjafari, Y., Orense, R. P., &Suemasa, N. (2016).Evaluation of liquefaction susceptibility of soils
using Screw Driving Sounding method. Japanese Geotechnical Society Special Publication, 2(32),1160-1164.
Ndoj, A., Shkodrani, N., and Hajdari, V. (2015). Evaluation of Liquefaction Potential of Soil using
CPT and SPT. Proceedings of International Conference on Architecture, Structure and Civil
Engineering (ICASCE 15), Antalya (Turkey) Sept. 7-8, ISBN 978-93-84422-6-1, PP.1-7
Rahman, M. M., S K Paul and K Biswas, (2011), EARTHQUAKE AND DHAKA CITY-AN
APPROACH TO MANAGE THE IMPACT. J. Sci. Foundation, 9(1&2): 65-75, June-December
ISSN 1728-7855.
Robertson, P.K.(1990). Soil Classification Using the Cone Penetration Test.Canadian Geotechnical
Journal, Vol. 27, No. 1, pp. 151–158.
Robertson, P. K. (2009). Evaluation of flow liquefaction and liquefied strength using the cone
penetration test. Journal of Geotechnical and Geoenvironmental Engineering 136.6: 842-853.
Seed, H. B., & Idriss, I. M. (1971). Simplified procedure for evaluating soil liquefaction potential.
Journal of Soil Mechanics & Foundations Div, 97(8371), 1249-1273.
Seed, H. B., & Idriss, I. M. (1982). Ground motions and soil liquefaction during earthquakes (Vol.
5): Earthquake Engineering Research Institute.
Seed, H. B., Idriss, I. M. & Ignacio, A. (1983). Evaluation of liquefaction potential using field
performance data. Journal of Geotechnical Engineering, 109(3), 458-482.
Seed, H., B., Tokimatsu, K., Harder, L., & Chung, R. M. (1985). Influence of SPT procedures in soilliquefaction resistance evaluations. Journal of Geotechnical Engineering, 111(12), 1425-1445.
Sesov, V., Edip, K., &Cvetanovska, J. (2012).Evaluation of the liquefaction potential by in-situ tests
and laboratory experiments in complex geological conditions.In Proceedings 15th world conference
earthquake engineering Lisbon, Portugal, 1-7.
Tokimatsu, K., & Yoshimi, Y. (1983). Empirical correlation of soil liquefaction based on SPT Nvalue and fines content. Soils and Foundations, 23(4), 56-74.
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WASA, 1991, Dhaka Region Groundwater and Subsidence Model; Dhaka Water and Sewerage
Authority.
Youd, T., & Idriss, I. (2001). Liquefaction resistance of soils: summary report from the 1996
NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. Journalof Geotechnical and Geoenvironmental Engineering, 127(4), 297-313.

22

PART-III

WEBINAR ON EXPERT CONSULTATION ON
BUILDING EARTHQUAKES RESILIENTCOMMUNITIES AND SOCIETIES FORBANGLADESH: SHARING EXPERIENCES ANDLESSON LEARNED FROM EARTHQUAKES
AROUND THE WORLDA Celebration of the United Nations International Day on Disaster Reduction
Tuesday, 13 October 2020

BANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By:

Mehedi Ahmed Ansary23

On the day of International Day on Disaster Reduction, Centre on Integrated Rural Development for
Asia and the Pacific (CIRDAP) in collaboration with Bangladesh University of Engineering andTechnology (BUET), organized a webinar on Tuesday 13th October, 2020. The webinar is entitled“Expert Consultation on Building Earthquakes Resilient Communities and Societies for
Bangladesh: Sharing Experiences and Lesson Learned from Earthquakes around the World”.
Experts from Bangladesh, Thailand and U.S.A. joined this webinar to discuss this highly burning
issue – Earthquake risks – and it’s after effects on the planet and the general people on the world.
Introduction
1. Director General (DG) CIRDAP Dr. Cherdsak Virapat welcomed all participants. He
informed that the date of 13 October is an important day for Thailand as the countrycommemorates the passing of the late King Bhumibol Adulyadej, the Father of the Nation in
2016. . For honoring the King, all the participants were requested to stand up for the Royal
Anthem.
2. Dr. Virapat then proceeded by giving opening address of the webinar and provided his
message for the International Day for Disaster Risk Reduction. He called for immediate
global attention on disaster risk reduction by mean of hazard vulnerability assessment,
disaster early warning, mitigation, preparedness and response. He expected that the meeting
will identify strategic actions, measures and pilot implementation of end-to-end earthquake
risk reduction systems for Bangladesh. Each participant was asked to introduce oneself.
Then, Dr. Virapat invited Prof. Mehedi Ansary, BUET to provide background and aims of themeeting.
3. Prof. Ansary acknowledged CIRDAP for collaboration with BUET in organization of thiswebinar. He briefly addressed the past earthquakes occurred in Bangladesh as follows.
Professor Mehedi Ansary
4. Prof. Ansary showed some graphs on the earthquakes of Bangladesh as this is a moderately
seismic country in the world. Several past earthquakes have occurred in Bangladesh andsurrounding region in the last several hundred years. Among these earthquakes, the 1762
Arakan, 1869 Cachar, 1885 Bengal, 1897 Indian, 1918 Srimangal, 1930 Dhubri, 1934 Nepal,
2015 Nepal Earthquakes are well known in Bangladesh. The 1897 Indian Earthquake locatedin Assam, has caused massive destruction to structures in Dhaka City killing 1542 people in
the region (Oldham 1899). He also showed some old photographs regarding the damage of1897 great Indian (AKE M. 8.1) and 1918 Srimangal earthquake (7.6), which showed
destruction of the buildings and land.

24

5. Prof. Ansary pointed out that at that time only 90,000 people lived in the Dhaka city out ofthem 1542 were killed. Today, almost 20 million people live in Dhaka city, and also there arearound 2 million buildings here so that it can be imagined that what will be the casualty if thesame earthquake happens today. Prof. Ansary also shared that no large earthquake has
occurred here for the last few decades, so the people have become complacent.
Dr. Walter D. Mooney
6. Dr. Mooney gave his speech on Indonesian Palu and Sulawesi earthquakes which occurred on
September 28, 2018. This earthquake caused Tsunami and also ground shaking created lots ofdestruction on the areas. Dr. Mooney showed the 5 –year period of Indonesian seismicity(2015-2020). Not only Tsunami but also the Palu earthquake generated ground shaking,landside and liquefaction which was un-anticipated. Most of the emergency warning centres
were also destroyed due to this earthquake and also the ground shaking stayed for a longtime.
7. Due to the earthquake, the death toll was high with the confirmed death of 3,400 people. Dr.
Mooney described the enigmatic source of Tsunami and quantifying damage facilitates
informed decisions on seismic building design. He said it will not matter how strong abuilding is built if the foundation is weak. The key points of the Palu earthquake is that
Tsunami waves was there for several minutes after the main shock. And another point is thatthe maximum tsunami inundation was 469 m.
8. Dr. Mooney also presented some of the images of the Palu earthquakes in the city regarding
mass destructive buildings, roads, houses and bridges with intensity and severity levels.
There is severe liquefaction in three areas – Balaroa, Petobo and Jonooge-Sidera.
9. In the end of his presentation, Dr. Mooney shared the recovery plan
Recovery Plan 1:‐

Not allowed to build any settlements within 100 m of the coast.

‐

Areas with high risk pf liquefaction to be converted to public places.

‐

Local living in “high risk” zones are being rehoused to “low risk” areas.

Recovery Plan 2:‐

1,100 new houses built in “safe zones”‐

Mangrove and sea wall being assessed for coastal protection‐

BMKG working collaboratively with the USGS to improve efficiency of Tsunami
warning system.

Prof. Ansary discussed and gave his opinion on the presentation of Dr. Mooney that the Palu
city is not densely populated as Dhaka city given the fact that death toll and causes. Dr.
25

Mooney responded that this city is much higher than the Switzerland in population but
obviously lower than Dhaka city. He also said that at present they changed their land use
policies because of the earthquake. But it’s very difficult to relocate or gave up the land in theoffshore because it the matter of people’s land ownership and livelihoods.
Prof. Tavida Kamolvej
10. Prof. Tavida Kamolvej, Dean of Political Science, Thammasat University, Thailand presented
on lesson learned from disasters.
11. Prof. Kamolvei emphasized more to lesson learned, awareness and education training about
the earthquake more than the technicality of it. How are we doing about the safety of thepeople? What measures did the governments take for this? Are people aware enough about
the consequences of earthquake? Do they have enough knowledge about post disaster risk
reductions measures and others?
12. She focused on the shared risk big data and communication amongst nations, and on the risk
governance.
‐

Degree of uncertainty and nonlinearity still there.

‐

There are compound hazards and trans border still.

‐

Structural and non-structural measures for disaster risk reductions cannot be same forevery sector of the people. Combination of both is important and need balance between
the two.

‐Transboundary and different level of capacity and literacy are required.

‐

Research and development on possible risks and measures are required.

‐Different vulnerabilities from capacity and competency.

‐

Political commitment and priority must be given.

‐

Public and private investment should be allocated in the disaster risk reduction. And 1%of the national budget must be allocated for the post disaster measures. Everyone always
talks about the pre-disaster measures not post-disaster risk reduction measures.

‐

Self-sustained emergency management with reverse risks and adaptive risk governance isrequired.

‐

Policy sandbox and institutional innovation are needed. Translations of institutional
design/measures to local mechanism are needed for the community people. It is difficult
to change the mindset of the people so that we need to focus more on for increasing
technical capacity.

‐

Balanced risk and economic loss in recovery is needed. (with a combination of predesigned measures, policy alternative simulation and institutional changes).
26

‐After Dr. Tavida’s presentation, Prof. Ansary commented that it is very difficult tochange the political will and institutional changes are needed. He pointed out thatknowledge of people is power.

‐

Dr. Tavida stressed that communication at the community level helped make risk
reduction measures more practical than the national level measures. Participatory action
research is required for this. Academics and local people – this combined team of these
two groups – can be useful through pilot project on the disaster risk reductions measuresor post – disaster phases.

13. Prof. Ansary presented some of the work slides of Bangladesh Earthquakes and Risk
Assessment of Dhaka, Bangladesh on behalf of Dr. Fouad Bendimerad, EMI, U.S.A. This
presentation showed about the risk assessment of building collapse due to distanceearthquake outside the Dhaka city.
‐

Recent incidents at Shakharinazar in 2004, Collapse of 9 storied building Rana Plaza in
April, 2013 are described. In every building collapse tragedy, 80% of the people isrescued by local community people rather than fire brigades and other defense people.

‐

One of the world bank funded assessment study on the proposed earthquake scenario in
Dhaka which is developed by Earthquakes & Megacities Initiatives (EMI), U.S.A showedthat because of the earthquake 6 billion dollar will be direct loss. Nearly 200 thousandpeople will die and nearly 300 thousand people will be injured. More than 5.3 million
people will be displaced person in need of access to basic social services.

Open discussions
At the open discussion, Dr. Virapat gave a Mock scenario questions to everyone about what arelessons learned from earthquakes happen in different countries, what are local capacity requirements
on end-to-end early warning and mitigation systems. What will be practical measures and action planin response to an incoming earthquake for Bangladesh.
Dr. Tavida Kamolvej: In reply to these questions, Dr. Tavida responded that to communicate withearthquake is very much difficult than other disasters. If an autonomy is established, she would wantto build an earthquake resistant building and in the risk prone area instead of trying to change
political thoughts which is very difficult. Also a learning center with data regarding earthquake canbe built closer to the epicenter kind of area to educate the community and people. This center will
have warning of aftershock and record or predict about the destructions. It is not possible to help
people by forecasting about the earthquake beforehand. However, this learning and education willteach the community what to do after the earthquake. With this learning center, government can also
be aware or learnt about the investment, what kind of balance between structure and non – structured
27

measurement, Community capacity, etc. And this building can be used as a shelter for a short periodof time. Basically, this building will be an example of safety for the community people beforehand of
the earthquake. Those places which are already being destroyed and have destruction, tocommunicate with the people of those areas to prepare for the next one will be less difficult. But
those areas which are not suffered or observed the earthquake yet will be difficult. Because most of
the people will not understand the future risks of the earthquake that you are forecasting.
Dr. Ansary discussed that by showing and experiencing the incidents especially with the fireincidents then people can easily relate to that. This is due to the fact that during the last few years
several fire incidences in Dhaka killed several hundred people as well as damaged several multistoried buildings. If fire incidents and earthquake can work together, people will understand. But
creating awareness to the people only about the earthquake, it will be very difficult to work. People
need some example – like since there is no earthquake in the last decades but there are lots of fireincidents – people can relate because the fire incidents are still burning in their minds.
Dr. Tavida then asked the question regarding the recent event of Indonesian tsunami to Dr. Mooney.
She asked that is it possible to forth sight the different kind of source of risks or have pre-design ofevery possible risks from disasters based on the geo data and other techniques. And relating to the
Dr. Tavida’s query, Mr. Tomasi asked about the indigenous people of Indonesia and their knowledge
whether it exists or not?
Dr. Walter Mooney: There are too many earthquakes with the faults around the world. The main
issue is the more responsibility needs to be pushed down to the local level for all these agencies. The
field staff/people always depends or waits for the instruction from the headquarters – the local people
are identifying the vulnerable structure and also not sending the recommendations back to theheadquarters. There is a lack of empowerment in the local level for example in Indonesia there are 10
warning centers/bureaus at the local level in different places. But every bureaus always look at the
Jakarta and waits for the instruction but it should be other way around, these local bureaus should tell
the headquarter about what they need? It is a big problem of a human organization for reducing therisks and also saving the lives.
Prof. Ansary suggested to start communicating with Director General (DG) of Department ofDisaster Management (DDM), Bangladesh. Or the secretary of the disaster management about the
EMI CVMP produced the risk of map of earthquake of Dhaka city.
Prof. Helal Uddin of Director Research:
Prof. Helal pointed out on the earthquake impacts on the country economy. Since the capital stock ofBangladesh is more than trillion dollar and Dhaka city contributes around 40% of our GDP which is28

a huge amount. So, Dhaka is being the most popular city and exposed to the earthquake, economic
development and growth will be tremendously affected by indirect loss incurred by any earthquake.
indirect cost. It will be seriously damaging effect to the Bangladesh’s economy. This figures andstatistics is needed to place before government.
In Bangladesh, the earthquake is in nowhere in the priority of the government. Agreeing with Dr.
Tavida that one tsunami change the public sector priority in Indonesia, the common and general
people should be aware of more about the earthquake rather than constantly going back to thegovernment in Bangladesh – make general people aware about the severity of earthquake – Once
general people makes the earthquake as a popular demand, then government will give priority. Afterthat this future risks can be calculated or for casted about the disaster. Through some projects, thisawareness amongst people about earthquake can be possible – they can learn and apply themeasurements and at the same time with this government will also be attracted.
Mr. Tinnakorn Tatong: Earthquake cannot be predicted but still the good data base is needed forkind of predicting about where it is going to happen. Which magnitude it is going to be? Then we
can go to the government – pursuing government is clearly a management issue. Government always
have to make priority about each sectors such as COVID 19 is a big issue. So, government willprioritize this pandemic more than the earthquake. For every projects, the big data of Earthquake ismore needed.
Closing Remarks
With the open discussions amongst the participants the webinar ended on 12 p.m. by the closing
remarks of DG CIRDAP Dr. Virapat. On behalf of CIRDAP, he thanked all the experts andparticipants for sharing their views on the issue of earthquake. The ideas that we are generating that
how we are going to mitigate the risks and problems due to the earthquake which may happen in
Bangladesh or anywhere in the world. CIRDAP and Prof. Ansary have already submitted a project
proposal on the earthquake and natural disaster resilience in Chittagong city to European Union
Horizon 2020 fund and hoping that next year EU will approve the fund for this project. Again,
thanking all the participants for celebrating the international day of disaster risk reductions, DG
CIRDAP closed the webinar.

29

Experts and Partcipants of the Webinar:
Experts
1. Prof. Mehedi Ansary, Department of Civil Engineering, BUET
2. Dr. Walter Mooney, United States Geological Survey (USGS), U.S.A.
3. Prof. Tavida Kamolvej, Dean, Faculty of Political Science, Thammasat University, Thailand
4. Mr. Tinnakorn Tatong, Department of Mineral Resources, Ministry of Natural Resources andEnvironment, Thailand
CIRDAP
1. Dr. Cherdsak Virapat, DG, CIRDAP
2. Prof. Helal Uddin, Centre on Integrated Rural Development for Asia and the Pacific (CIRDAP)
3. Mr. Tomasi V. Raiyawa, Strategic Planning Officer, CIRDAP
4. Mr. George C. Babu, Programme Officer, CIRDAP
5. Dr. Usharani Boruah, Head of Librarian, CIRDAP
6. Ms. Hurain Jannat, Communication Officer, CIRDAP

30

PART-IV

STRONG GROUND MOTION IN BANGLADESHAND NORTH-EAST INDIAN REGION FROM
2005 TO 2017 AND ITS PREDICTION OF
ATTENUATION DATA DURING FUTURE
EARTHQUAKESBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By:

Tanzila Tabassum
Mehedi Ahmed Ansary31

1 Introduction
Over the last few decades, the world has experienced numerous devastating earthquakes (Sieh et al.,2008). As a result of these devastating earthquakes, severe structural damages are occurring indensely populated areas and increasing loss of human life (Doǧangün, 2004). Mechanisms ofearthquakes and seismic ground motions are continually advancing (Bolt, 2004). To understand andinterpret earthquakes and earth structures, more observation stations need to be established formonitoring high quality seismic data. When enough effective earthquake data are available,
statistical analysis of these data could be carried out to better understand the wave propagation.
Earthquake magnification due to the local site condition and distance from the focus, play animperative role in predicting seismic hazards of structures (Idriss, 1991; Singh, 1998; Liao, 2013).
Ground-motion prediction equations (GMPEs) are function of magnitude, distance, site class,scaling of ground-motions etc.(Bradley, 2010; Atkinson and Adams, 2013). The amplitudes of strong
ground motions decay (attenuation) with increasing distances from the source of earthquakes(Minzheng, 1984; Boore and Atkinson, 2008). Previous literatures relied on earthquake intensity inpredicting models (Murphy and O'brien, 1977; Malone and Bor, 1979; Ambraseys, 1985). However,Cornell et al. (1979) proved that, earthquake intensity had poor correlations with acceleration.
Consecutively, Hasegawa et al. (1981) converted earthquake intensity data into equivalent strong
motion data e.g. acceleration and velocity. They also described the potential importance of thesestrong ground motion parameters for developing response spectra. Boore and Atkinson (2008)
predicted ground motion equations for spectral periods between 0.01s and 10.0s. They have analyzed
peak ground acceleration (PGA), peak ground velocity (PGV) for the prediction analysis. However,they have not incorporated peak ground displacement (PGD) for analysis as PGD is too sensitive tothe low-cut filters. According to their research, PGD might influence the data processing system.
Their study recommended to use response spectra (acceleration or velocity) at long periods instead of
PGD. Moreover, Dino Bindi et al. (2014) investigated ground-motion prediction equations for the
average horizontal component of PGA, PGV, and 5%-damped pseudo-spectral acceleration (PSA) at
spectral periods up to 3.0 s. This research only considered the spectral ordinates within the usable
frequency band and have the function of the low-cut frequency. Joyner and Boore (1981) analyzed
strong-motion data to derive new attenuation relations for peak horizontal acceleration and velocity
for the earthquake of close distances. Whereas, Bommer and Akkar (2012) predicted ground motion
prediction equations considering source-to-site distance metrics and used regression analysis tocalibrate the equations.
32

Sabetta and Pugliese (1996), Abrahamson and Silva (1997) also established different
predictive attenuation relationships for different regions which were functions of magnitude, distanceand soil sites. Defining these functions and using them in a consistent manner is very important forprediction of attenuation models (Chen and Faccioli, 2008). In this research, the predicted peakground parameters are expressed as a function of moment magnitude, distance (e.g. hypocentral
distance) and site category (rock and firm soil). Early studies often measure the distance decay withrespect to epicentral or hypocentral distance (Dino Bindi et al., 2014). These distance matrices canseverely affect the prediction models (Bommer and Akkar, 2012). Some researchers also considered
the coupled effect of these parameters in their studies (Qiwen et al., 1986; Fukushima and Tanaka,
1990; Hwang and Huo, 1997).
This study includes 1608 records with three-component digital seismograms recordings bothfrom the North-East Indian and Bangladesh region. In this research, the data collected from IMD has
been extracted by Seismosignal software and multi stage regression method has been used tocalibrate the ground motion prediction equations proposed by Joyner and Boore (1981) and Bommer
and Akkar (2012) model. The advantages of these two models are the motivation of behind opting
them. These two studies had the benefit of analyzing earthquake ground motion without any bias ofconsidered parameters. Predicted models from Joyner and Boore (1981) and Bommer and Akkar(2012) are based on geometrical spreading and anelastic attenuation as well as can retain magnitudeindependent shape curves. Joyner and Boore (1981) model had the benefit that it decouples distancedependence of the acquired data from the magnitude dependence. Whereas Bommer and Akkar(2012) model show the relationship associated with magnitude and distance for available groundmotion data. This research includes two GMPEs derived from the same dataset to demonstrate andobserve the potential difference and effect on the prediction equation for different soil sites.
In this study, the predicted ground shaking amplitudes, e.g. acceleration; velocity, and acceleration
response spectrum (ARS) and velocity response spectrum (VRS) for 0.3s, 1.0s and 2.0s are
expressed as a function of magnitude, hypocentral distance and site category. Similar studies havebeen performed for different dataset by some authors (Joyner and Boore, 1982; Ansary and
Yamazaki, 1998). Ground-motion prediction equations (GMPEs) based on peak ground motions,spectral values and attenuation relationships has been used for this research work. (Seed et al., 1976;
Ansary and Yamazaki, 1998). Residual values (model errors) for predicted equations have alsoestablished in this research (Anderson and Lei, 1994; Boore and Atkinson, 2008). Predicted models
have been also compared with the previous work done by other authors (Minzheng, 1984;
Fukushima and Tanaka, 1990; Cui et al., 2006).

33

2 Recorded Database
The database consists of total 160 earthquakes with around 1608 three-component (North-South,(NS), East-West (EW) and Vertical) seismic recordings from North-East Indian Meteorological
Department (IMD) network of instruments. Figure 1 shows the locations of the instruments
established by IMD. This database contains acceleration values within 1000 km of the fault fromearthquakes. The amplification of ground motions due to the local site condition plays an importantrole in seismic damage (Mohraz, 1976; Bommer and Akkar, 2012). The stiffness of soil site isexpressed by the time average shear wave velocity in the upper 30 m of soil deposits and was
initiated in the early 1990s (Nakamura, 1989; Borcherdt, 1994; Boore and Atkinson, 2008) byassigning a range of VS30 to different site categories e.g., NEHRP categories (Crouse and McGuire,1996). Soils having shear wave velocity between 700 m/s to 1620 m/s are characterized as rock sites(soil type A). Furthermore, the range of shear wave velocity between 375 m/s to 700 m/s and 200
m/s to 375 m/s are categorized as soft rock (soil type B) and firm soil (soil type C), respectively.
Table 1 shows the summary of the data used in this study. This can be observed that, bulk of the data
are from soil type A and C, which range from rock to firm soil; very few data were from soil type B(soft rock).
Table 1 Summary of data used for the attenuation study
No. of earthquakes

160

No. of records

536

No. of total three1608

component data
No. of recording stations

163

Magnitude range

2.3 – 7.8

Minimum intensity(larger of two horizontal

PGA ≥ 0.438 cm/s2

components)
Soil Type A (Rock)700< Vs30 <1620 m/s
Soil Type B (Soft Rock)375 < Vs30 <700 m/s
Soil Type C (Firm)
200 < Vs30 < 375 m/s
34

273 records
63 records
200 records

Fig. 1 Map showing the locations of the instruments covered by the IMD

3 Attenuation Model and Predictive Equations
Peak ground motion attenuation models have been usually presented by logarithmic type of
empirical equations (Trifunac, 1976; Joyner and Boore, 1981; Sabetta and Pugliese, 1996;
Abrahamson and Silva, 1997; Boore et al., 1997; Bommer and Akkar, 2012). All the attenuation
theories and research achievements are mainly based the observations and data recorded by thestations during earthquakes. Therefore, the number of data available is very important to develop theground motion attenuation relationships.
The predictive relationships are usually expressed as ground motion parameters, e.g. PGA,
PGV, ARS, VRS in a functional form (Boore and Atkinson, 2008; Dino Bindi et al., 2014). The
functional forms of the predictive relationships are usually selected to reflect the mechanicalproperties of the ground motions as close as possible minimizing the number of empiricalcoefficients and allowing greater confidence in application of the predictive relationships to seismicparameters, e.g. magnitude, distance, site conditions etc. (K. W. Campbell, 1985).
The general functional form of attenuation relationship for any type of soil can be presented
by the expression:
log

(1)

Where, Y = Ground Motion (acceleration, velocity, response spectrum)a = Regression Constant
M = Moment Magnitude
35

= Hypocentral Distance (km) =σ = Standard Error
Final functional form for modelling the ground motion attenuation equations for any type ofsoil can be represented as following:
log

log

log

(Joyner and Boore, 1981)

(2)

log

(3)

(Bommer and Akkar, 2012)

In order to determine the coefficients and standard deviation in equation (2) and (3), multiple
regression method is used with variable soil type (Joyner and Boore, 1981, 1982; Graizer andKalkan, 2007; Idriss, 2008; Bommer and Akkar, 2012). Attenuation model is essential for groundmotion attenuation relationships. The standard deviation of the regression analysis result of a properand suitable attenuation model should be as small as possible. For making the predictive models
acceptable and reliable, resulting equations should be consistent with data as much as possible(Somerville et al., 1997; Graizer and Kalkan, 2007).
3.1 Regression coefficients
GMPEs based on peak ground motions and spectral values are presented in this paper IMD strong
ground motion (SGM) data recorded from 2005 to 2017. To determine the coefficients of attenuation
equations, two attenuation models proposed by Joyner and Boore (1981); equation (2) and Bommer
and Akkar (2012); equation (3) are used in this study to fit the SGM data. This study predict PGA,
PGV, ARS and VRS spectral ordinates at response periods for 0.3s, 1.0s, and 2.0s. as a function of
moment magnitude (M), hypocentral distance and site class. Usually, response spectrum periods lessthan 0.3s are indicated as short periods. Periods between 0.3s to 2.0s are specified as intermediate
and greater than 2.0s are expressed as long periods (Joyner and Boore, 1982). Joyner and Boore(1982) explain that, spectral values for period less than 0.3s can generate slight de-amplification at
the soil sites. Intermediate periods e.g. between 0.3s to 2.0s can avoid the errors associated with theamplifications of soils and scaling effects. Several earlier studies also explain that, response
spectrum equations predicted for periods between 0.3s to 2.0s, can result in a consistent manner(Joyner and Boore, 1982; Bozorgnia and Niazi, 1993; Wilson and Lam, 2003).
Tables 2 and 3 show coefficients determined by multistage regression analysis for rock andfirm soil sites respectively for Joyner and Boore (1981) GMPEs whereas, Tables 4 and 5 showcoefficients determined by multistage regression analysis for rock and firm soil sites for Bommer andAkkar (2012) GMPEs. Here, b1, b2, b3, b4 are the coefficients for the independent variables, σ is36standard deviation (lesser value indicating good model) and correlation factor R2 (higher value
indicating good correlation between the parameters). As there are only 63 records for soft rock sites(Table 1), the analysis of soft rock soil sites has been excluded in this research.
Table 2 Regression coefficients for peak ground motions and spectral values using Joyner and Boore(1981) GMPEs for rock sites (Soil Type A)
Y

b1

b2

b3

b4

σ

R2

PGAmax,h0.80532

0.00030

0.36134-0.92616

0.37057

0.35008

PGAmax,v

0.707520.00033

0.38629-1.06121

0.40904

0.36024

PGVmax,h0.23332

0.000250.28093-0.64941

1.26125

0.02363

PGVmax,v

0.297100.000370.24754-0.75369

1.25565

0.02544

PGAres,h0.88098

0.000280.36346-0.91552

0.37306

0.34561

PGVres,h0.30098

0.00025

0.28609-0.64690

1.262130.02390

ARSh (0.3s)-1.97543

0.00032-0.13774

0.573420.526540.18665

ARSh (1.0s)-2.36232

0.00006-0.10470

0.69627

0.54808

0.19524

ARSh (2.0s)-2.13429

0.00033-0.14379

0.64215

0.53334

0.21353

VRSh (0.3s)-2.32579

0.000120.063160.18016

1.35363

0.01386

VRSh (1.0s)-1.60392

0.00004

0.057020.01944

1.34169

0.00253

VRSh (2.0s)-1.43951-0.00011

0.08796-0.02869

1.346310.00275

ARSv (0.3s)-2.48720

0.00000-0.08348

0.61952

0.66552

0.12431

ARSv (1.0s)-2.22717

0.00002-0.05987

0.43130

0.646450.07373

ARSv (2.0s)-2.39968-0.00012-0.10277

0.663170.606470.13431

VRSv (0.3s)-2.19324

0.00024-0.00951

0.13308

1.31757

0.00906

VRSv (1.0s)-1.55553

0.00025

0.02355-0.09257

1.34334

0.00234

VRSv (2.0s)-1.21549

0.00041

0.09624-0.36059

1.36968

0.00598

Table 3 Regression coefficients for peak ground motions and spectral values using Joyner and Boore(1981) GMPEs for firm soil (Soil Type C)
Y

b1

b2

b3

b4

σ

R2

PGAmax,h0.63296-0.00062

0.43518-0.84747

0.30658

0.49519

PGAmax,v

0.56545-0.00067

0.41255-0.85580

0.345360.44610

PGVmax,h0.559350.00012-0.23624

0.51760

1.309310.01283

PGVmax,v

0.603810.00045-0.30737

0.459381.30335

0.02197

37

PGAres,h0.72201-0.00064

0.43682-0.83985

0.29974

0.50658

PGVres,h0.62698

0.00011-0.22759

0.49924

1.308020.01225

ARSh (0.3s)-3.13749-0.00052

0.07768

0.705770.594770.17871

ARSh (1.0s)-3.01049-0.00062

0.096200.62204

0.577140.15254

ARSh (2.0s)-2.81744-0.00039

0.06788

0.58105

0.593390.13916

VRSh (0.3s)-1.57219

0.00059-0.62886

1.46261

1.38130

0.09570

VRSh (1.0s)-0.68733

0.00076-0.63600

1.19174

1.372770.08472

VRSh (2.0s)-0.25837

0.00061-0.59675

0.99466

1.365630.06784

ARSv (0.3s)-2.98277-0.00054

0.09208

0.55851

0.67368

0.10135

ARSv (1.0s)-3.57852-0.00132

0.13989

0.82195

0.639220.16144

ARSv (2.0s)-3.47533-0.00105

0.07258

0.89599

0.648650.15442

VRSv (0.3s)-2.56279

0.00005-0.64273

1.84534

1.38835

0.10536

VRSv (1.0s)-1.25212

0.00092-0.55499

1.06735

1.37557

0.07851

VRSv (2.0s)-0.78463

0.00073-0.58880

1.05993

1.37838

0.07146

Table 4 Regression coefficients for peak ground motions and spectral values using Bommer andAkkar (2012) GMPEs for rock site (Soil Type A)
Y

b1

b2

b3

b4

b5

σ

R2

PGAmax,h0.01572

0.123280.078700.36183-0.24825

0.367440.36340

PGAmax,v

0.015490.12000

0.07856

0.18590-0.23509

0.408930.36297

PGVmax,h0.60494-0.35371

0.11282

0.39446-0.20777

1.262280.02566

PGVmax,v

1.44156-0.67949

0.134720.16065-0.16841

1.25704

0.02692

PGAres,h0.10633

0.10592

0.08287

0.40495-0.25719

0.368800.36286

PGVres,h0.68015-0.35456

0.113510.40195-0.20816

1.263210.02587

ARSh (0.3s)-0.77177-0.34989-0.01495-0.23223

0.185420.522220.20325

ARSh (1.0s)-1.78714-0.06227-0.03513

0.010480.145370.547090.20153

ARSh (2.0s)-1.21176

0.11363-0.10098-0.97367

0.357190.523870.24439

VRSh (0.3s)

0.30639-0.50732-0.00638-1.18072

0.30220

1.34715

0.02690

VRSh (1.0s)-0.03879-0.23158-0.01388-0.901280.20141

1.34069

0.00773

VRSh (2.0s)

0.31509-0.25365-0.00460-0.98732

0.190111.345410.00778

ARSv (0.3s)-0.40104-0.29914-0.05029-0.96401

0.333730.650310.16776

ARSv (1.0s)-1.00245-0.14135-0.04011-0.62087

0.224410.64084

0.09389

ARSv (2.0s)-1.44359

0.17819-0.10198-0.91591

0.329130.59674

0.16555

38

VRSv (0.3s)

0.45172-0.92082

0.06362-0.47733

0.14840

1.313410.01897

VRSv (1.0s)

0.62426-0.90209

0.08907-0.20249

0.04036

1.34194

0.00811

VRSv (2.0s)

0.86310-0.67402

0.05193-0.76169

0.12701

1.370290.00878

Table 5 Regression coefficients for peak ground motions and spectral values using Bommer andAkkar (2012) GMPEs for firm soil (Soil Type C)
Y

b1

b2

b3

b4

b5

σ

R2

PGAmax,h-0.21224

0.61269

0.01595-0.25977-0.16759

0.312160.47932

PGAmax,v-0.42053

0.75202-0.00915-0.49652-0.12559

0.34930

0.43628

PGVmax,h-1.39360-1.20635

0.26292

4.58270-0.79111

1.297210.03593

PGVmax,v-0.85807-1.24603

0.22804

3.89521-0.63607

1.29756

0.03559

PGAres,h-0.14321

0.61198

0.01749-0.22437-0.17393

0.30550

0.49004

PGVres,h-1.34040-1.18048

0.26038

4.55703-0.78734

1.296070.03516

ARSh (0.3s)-2.79298

0.18648-0.01987

0.283400.037700.60092

0.16592

ARSh (1.0s)-2.60069

0.12248-0.00349

0.35801-0.00206

0.58558

0.13203

ARSh (2.0s)-2.42870

0.18662-0.02730

0.053130.06976

0.59730

0.13223

VRSh (0.3s)-1.80672-1.50317

0.16374

3.53395-0.35805

1.38191

0.09952

VRSh (1.0s)-0.79252-1.62982

0.17719

3.36354-0.36335

1.37372

0.08813

VRSh (2.0s)-0.89564-1.62035

0.20816

3.79156-0.49874

1.363470.07553

ARSv (0.3s)-2.93939

0.58434-0.08220-0.45564

0.15258

0.67651

0.09846

ARSv (1.0s)-5.14879

0.89017-0.04817

0.97874-0.14065

0.65152

0.13334

ARSv (2.0s)-4.54408

0.436790.00066

1.39851-0.18783

0.659940.12928

VRSv (0.3s)-3.60777-1.16205

0.14115

4.03399-0.42556

1.387700.11076

VRSv (1.0s)-1.51042-0.91665

0.05468

1.87134-0.07897

1.38462

0.07110

VRSv (2.0s)-1.40231-0.981780.080312.33904-0.18845

1.38474

0.06766

Here, subscript ‘h’ and ‘v’ denotes values for horizontal and vertical component and ‘res’ denotes forresidual value.
3.2 Attenuation Models
By multiple regression analysis for all earthquake data (536 records) the predicted attenuation model
correspond to authors are given below:
log Amax,h = 0.45612 - 0.00027*r + 0.39353*M – 0.73977*log r ± 0.3547 σ
1981)

(4)
39

(Joyner and Boore,log Amax,h = 0.12628 + 0.44816*M + 0.00922*M2 + (– 0.47679 – 0.07301*M) log r ± 0.3552 σ(Bommer and Akkar, 2012)(5)

The standard deviation for equation (4) is ± 0.3547 and for equation (5) is ±0.3552; which aresatisfactory. Whereas, the correlation factors are 0.3461 and 0.3456, respectively. From the standard
deviation and correlation values it is evident that, both models are satisfactory for all earthquake data
records (536 records). Both Fig. 2 and 3, solid line represents the mean value of equation (4) and (5)respectively where the value of σ is zero.
3.2.1 Peak ground acceleration with respect to hypocentral distance
The attenuation relationship developed by K. Campbell (1994) and Boore and Joyner (1982) arerecommended for predicting acceleration in horizontal direction (AH ). The coefficients in thisrelationship were determined from regression analysis. The distribution of the recordings withrespect to magnitude and hypocentral distance were plotted in Fig. 2 and 3 according to local site
conditions.
Inspection of Fig. 2 and 3 reveal that there is a correlation between magnitude and distance,
which may induce bias to the magnitude and hypocentral coefficients b2 and b3 in equation (2) for
Joyner and Boore (1981) and coefficients b2, b3 and b5 in equation (3) for Bommer and Akkar(2012). However, because there are many earthquakes with only a few records in our data set,instead of grouping together the data belonging to the same earthquake, the data have been divided
into three soil classes e.g. soil type A, B and C (Table 1); based on the shear wave velocity at 30 m
depth of the related event. Herak et al. (2001) also predicted earthquake attenuation equation fordifferent soil sites. Figures 2 and 3 demonstrate strong correlation between magnitude andhypocentral distance with a coefficient of 0.4999, and 0.6617 respectively for rock site (soil type A)
and firm soil (soil type C). Soil type A includes total 273 sets and type C includes 200 sets of data.
Predicted equations for horizontal and vertical component of the earthquake ground motions
after regression method according to soil sites and corresponding authors are given below:
Rock sites (Soil Type A)
Joyner and Boore (1981)
log Amax,h = 0.80532 + 0.00029*r + 0.36134*M – 0.92616*log r ± 0.3705 σ(6)log Amax,v = 0.70752+ 0.00033*r + 0.38629*M – 1.06121*log r ± 0.4090 σ(7)Bommer and Akkar (2012)log Amax,h = 0.01572 + 0.12328*M + 0.07870*M2 + (0.36183 – 0.24825*M) log r ± 0.3674 σ(8)40log Amax,v = 0.01549 + 0.12000*M + 0.07856*M2 + (0.18590 – 0.23509*M) log r ± 0.4089 σ(9)Firm soil (Soil Type C)
Joyner and Boore (1981)
log Amax,h = 0.63296 - 0.00062*r + 0.43518*M – 0.84747*log r ± 0.3066 σ(10)log Amax,v = 0.56545 - 0.00067–r - 0.41255*M – 0.85580*log r ± 0.3454 σ(11)Bommer and Akkar (2012)log Amax,h = -0.21224 + 0.61269*M-0.01595*M2 + (-0.25977–0.16759*M) log r ± 0.3122 σ(12)log Amax,v = -0.42053 +0.75202*M -0.00915*M2 + (-0.49652 – 0.12559*M) log r ± 0.3493 σ
(13)

8

8

Soil Type A(273 Records)

R = 0.4999

6

6

5

5

4

4

3

3

2

1

10

100

Soil Type C(200 Records)

7

M

M

7

2

1000

2

10000

1

R2 = 0.6617

10

100

1000

10000

Hypocentral Distnace (km)Hypocentral Distance (km)Fig. 2 Distribution of recordings in the PGA

Fig. 3 Distribution of recordings in the PGA

database as a function of magnitude, distance for

database as a function of magnitude, distance forrock site: soil type A

firm soil: soil type C

Figures 4 and 5 describe the attenuation curves according to equations (6-7) and (10-11) for
rock and firm soil respectively that correspond to Joyner and Boore (1981) model. First row
indicates the magnitude interval 4.5 ≤ M ≤ 5.5, where the mean magnitude is 5. Second row shows
magnitude interval of 5.6 ≤ M ≤ 6.5 and the mean magnitude is 6. The bottom row denotes themagnitude interval of 6.5 ≤ M ≤ 7.8 where the mean magnitude is 7. Left column shows the peakhorizontal acceleration (maximum one of the two horizontal component) and right column shows thevertical component. The mean magnitude is expressed as a solid bold line. Short and long dashedlines bound ±lσ and ±2σ intervals, respectively. The multiple correlation coefficient of equation (6)41and (7) is high: 0.35 for horizontal component and 0.36 for vertical component, respectively.
Standard error σ is 0.3705 for horizontal component and 0.4090 for vertical component. For equation(10) and (11), the multiple correlation coefficient is also very high: 0.50 for horizontal component
and 0.45 for vertical component, respectively. Standard error σ is 0.3066 for horizontal component
and 0.3454 for vertical component. Similar results have also presented by (Herak et al., 2001).

(a)

(b)

(c)
Fig. 4 Comparison of peak horizontal acceleration with predicted ones for earthquakes for soil type A42(a)5.6 ≤ M ≤ 6.5(b)
6.5 ≤ M ≤ 7.8(c)Fig. 5 Comparison of peak horizontal acceleration with predicted ones for earthquakes for soil type C

From Fig. 4 (a), it can be perceived that for magnitude range 4.5 to 5.5, most of the points lie
between the ±lσ range, which demonstrates that the predicted results are consistent with theobservations. However, for Fig. 4 (b), the data set are little bit biased in ±1σ for vertical component
43

of peak acceleration whereas for the peak horizontal acceleration it shows most of the points lie in+lσ range. For magnitude range 6.5 to 7.8 in Fig. 4 (c) it is evident that, the data set is biased in -1σ
to -2σ range for both acceleration component e.g. horizontal and vertical. This is because lack of thedata set in the magnitude range of 6.5 to 7.8.
From the result of Fig. 5 (a), it can be seen that for magnitude range 4.5 to 5.5, most of thepoints lie between the ±lσ range for horizontal component of peak acceleration, but massively biased
to +2σ and more for vertical component. Which demonstrates that the predicted results are consistent
with the observations for horizontal component but not for the vertical one. Since Fig. 5 (b), the data
set are within the range of ±2σ for both component of peak acceleration, which demonstrates that thepredicted results are consistent with the observations. For magnitude range 6.5 to 7.8 in Fig. 5 (c) it
is evident that, the data set is within ±1σ range for both acceleration component e.g. horizontal andvertical. It expresses a good correlation between the predicted and observed values for attenuation
models.
This research includes total 216 records of magnitude from 4.5 to 5.5, 112 records ofmagnitude from 5.6 to 6.5 and only 33 records of magnitude from 6.5 to 7.8.
3.3 Decay of Predicted Acceleration
As the hypocentral distance of the earthquake increases, the predicted peak horizontal and verticalacceleration also decreases (Anderson and Lei, 1994). Thus, the predicted maximum acceleration is afunction of hypocentral distance. From Fig. 6, it is perceived that for a fixed earthquake magnitude
e.g. M = 6; the peak horizontal acceleration is dominant for Bommer and Akkar (2012) attenuation
model than Joyner and Boore (1981) model and the peak value of acceleration is more for firm soil
and less for rock site. Thus, the predicted values for firm soil sites are always lager than those fromrock sites for given conditions. This is due to amplification effect of local site condition. The shapesof the curves do not depend on the magnitude, indicating the magnitude independence of the distancedecay. Anderson and Lei (1994) also had similar results. Anderson and Lei (1994) described thatpeak horizontal and vertical acceleration had very weak tendency to decrease with magnitude. It isevident from Fig. 6 that the decay curves are steeper for Bommer and Akkar (2012) model indicating
faster decay of the model. This decay is related to the higher frequency content in the groundmotions. However, the reason needs further investigation.
Similarly, Fig. 7 shows that, for a fixed earthquake magnitude e.g. M = 6; the peak verticalacceleration is larger for Bommer and Akkar (2012) attenuation model than the other one and the44

peak value of acceleration is more for firm soil and less for rock site. This scenario is acute forsmaller hypocentral distance.
3.4 Predictive Equations with Respect to Residuals, Distance and Magnitude
The residuals represent the deviations of the predicted values from the observed values. It can be
defined as:
residual =

)

(14)

Where, Yobserved is the observed value recorded, e.g. peak ground motion acceleration, while Ypredicted
represents the value predicted by the empirical attenuation equations accordingly. The residual can
describe the extent to which the predicted values are consistent with the recorded values,
representing the reliability and applicability of the empirical predictive equations. The plots of theresiduals with respect to magnitude or distance can show the description of the results. If no trend isapparent in the residual points, the predictive equations are satisfactory, vice versa. The residual
plots could be seen hereafter.
The ratios of observed and predicted model (residuals) for rock and firm soil sites according
to equation (6) (Joyner and Boore, 1981) and equation (8) (Bommer and Akkar, 2012) are shown in
Fig. 8 and 9 respectively, as a function of hypocentral distance (a) and magnitude (b). These results
show no bias or singularity with distance and magnitude. These indicate that, residuals of the peakhorizontal acceleration are independent of hypocentral distance and magnitude. Along with, Fig. 8-9
show that most of the data are within its one standard deviation of ±0.3705 and ±0.3674 respectively,
indicating good correlations of residuals with magnitude of the earthquakes and distance. Though,for hypocentral distance, the data are seen scattered whereas for magnitude showing regulartendency.
Residuals distribution of firm soil sites by equation by (Joyner and Boore, 1981) and equation (12)(Bommer and Akkar, 2012) are shown in Fig. 10 and 11 as a function of hypocentral distance (a) andmagnitude (b). The results show no distance dependence of residuals for any of the component, evenfor large distances. Residuals are also not magnitude dependent. Nevertheless, most of the data arewithin its one standard deviation ±0.3066 (Fig. 10) and ±0.3122 (Fig. 11) indicating goodcorrelations of residuals with magnitude of the earthquakes and distance. These results are also
supported by previous studies (K. W. Campbell, 1981; Herak et al., 2001; D Bindi et al., 2006).

45

(a)

(b)

Fig. 6 Comparison of peak horizontal acceleration with local site conditions and distancepredicted by the attenuation relationship recommended by Joyner and Boore (1981) (a);Bommer and Akkar (2012) (b)

(a)

(b)

Fig. 7 Peak vertical acceleration with local site conditions and distance predicted by theattenuation relationship recommended by Joyner and Boore (1981) (a); Bommer and Akkar(2012) (b)

46

(b)

(a)

Fig. 8 Peak horizontal acceleration ratios between observed values and predicted ones for rocksite by equation (6) Joyner and Boore (1981) as function of hypocentral distance (a) andmagnitude (b)1.5Soil Type A(Bommer and Akkar)

1

Residuals

0.5
0
-0.5
-1
-1.5
2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8

M

(a)

(b)

Fig. 9 Peak horizontal acceleration ratios between observed values and predicted ones for rocksite by equation (8) Bommer and Akkar (2012) as function of hypocentral distance (a) andmagnitude (b)
1.5

1.2

Soil Type C(Joyner and Boore)

1
0.5

0.4
Residuals

Residuals

Soil Type C(Joyner and Boore)

0.8

0

-0.5
-1

0
-0.4
-0.8

-1.5

-1.2
1

10

100
1000
Hypocentral Distance (km)

10000

2

2.5

3

3.5

4

4.5

5
M

5.5

6

6.5

7

7.5

8

(b)

(a)

Fig. 10 Peak horizontal acceleration ratios between observed values and predicted ones forfirm soil by equation (10) Joyner and Boore (1981) as function of hypocentral distance (a) andmagnitude (b)
47

1.5

1.2

Soil Type C(Bommer and Akkar)

1

0.8

0.5

0.4
Residuals

Residuals

Soil Type C(Bommer and Akkar)

0

-0.5

0

-0.4

-1

-0.8
-1.2

-1.5
1

10

100
1000
Hypocentral Distance (km)

10000

2

2.5

3

3.5

4

(a)

4.5

5
M

5.5

6

6.5

7

7.5

8

(b)

Fig. 11 Peak horizontal acceleration ratios between observed values and predicted ones forfirm soil by equation (12) Bommer and Akkar (2012) as function of hypocentral distance (a)and magnitude (b)3.5 Predictive Equations with Respect to Residuals and Log-Normal Plot
The residuals of horizontal as well as vertical component are distributed approximately log-normallyfor different soil types. For the horizontal components of acceleration, firstly, the peak of the twohorizontal components (East-West and North-South) has been taken for the analysis and then thesame analysis is performed for the resultant of the two components. Similar study has also been
conducted by some authors (Boore and Joyner, 1982; Anderson and Lei, 1994; Herak et al., 2001).
Figures 12-13 show the distribution of residuals according to equations (6) and (8)respectively. They are distributed approximately log normally with slight asymmetry showing longer
tails on the positive side for Joyner and Boore (1981) model as well as in negative side for Bommer
and Akkar (2012) model. This is most probably caused by site amplification at some stations andbalanced by a larger than expected number of residuals in the first negative and positive class,respectively. Peak acceleration residuals with log-normal distribution are shown in Fig. 14-15
according to equations (10) and (12). Similar slight asymmetry is observed with positive andnegative tails respectively for Fig. 14-15. These have been balanced by a larger than expected
number of residuals in the first negative class for Fig. 14 and larger residuals in the first positiveclass for Fig. 15.
3.6 Comparisons of Peak Ground Acceleration with Other Studies
A comparison of Joyner and Boore (1981) and Bommer and Akkar (2012) prediction model has beenshown in Fig. 16 for rock and firm soil sites. These two models function quite different for same data48

set and condition. Figure 16 (a) compare the dissimilarity of equations (6) and (8) that is for rock site
explaining that, the deviation of ground acceleration increases with the increased magnitude. There isno difference between the two models for small magnitude of earthquake e.g. M = 5. The largest
deviation between the curves corresponds to a factor of 10 which is greater than standard deviationof the data about the regression line. As there are similarity of the absolute predictions, there are no
fundamental differences in the shape of the curves. Thus, shape of both models is constrained to be
independent of magnitude. Figure 16 (b) shows the comparison of attenuation curves of equations(10) and (12) for firm soil site. The deviation of ground acceleration increases with the increased
magnitude which was seen same for rock site. However, the largest deviation between the curves
exceeds 33 percent in logarithm scale. The reason may be Bommer and Akkar (2012) model has
much dependency on magnitude parameter.
Attenuation predictive equations obtained in this study have been compared with other
regression equations. According to the study of Cui et al. (2006), the predictive equations for
Yunnan area could be expressed as:log (Ah) = 3.5549 + 0.2881*M + (-2.7317 + 0.0889*M) log (D + 13) ± 0.5314 σ(15)Where, Ah represents the horizontal predictive accelerations in cm/s2, D is epicentral distance inkilometers, and M is magnitude.
70
60

60
50

40

Observations

Observations

50

70
Soil Type A(Joyner and Boore)(Amax,h)30
20

Soil Type A(Bommer and Akkar)(Amax,h)

40
30
20

10

10

0

0

Residuals

Residuals

Fig. 12 Distribution of residuals of the logarithm Fig. 13 Distribution of residuals of the logarithm
of

peak

acceleration

with

log-normal of

peakacceleration

with

log-normal

distributions for horizontal component for rock distributions for horizontal component for rocksites (soil type A) according to equation (6)sites (soil type A) according to equation (8)

49

60
50

60
Soil Type C(Joyner and Boore)(Amax,h)50
40
Observations

Observations

40

Soil Type C(Bommer and Akkar)(Amax,h)

30
20

30
20

10

10

0

0

Residuals

Residuals

Fig. 14 Distribution of residuals of the logarithm Fig. 15 Distribution of residuals of the logarithm
of

peak

acceleration

with

log-normal of

peakacceleration

with

log-normal

distributions for horizontal component for firm distributions for horizontal component for firm
soil site (soil type C) according to equation (10)soil site (soil type C) according to equation (12)According to Fukushima and Tanaka (1990), the resulting attenuation relation in Japan is,log A = 0.41*M-log (R+0.032*100.41M) - 0.0034*R+1.30(16)Where, A is the mean of the peak acceleration from two horizontal components at each site in cm/s2,
R is the shortest distance between site and rupture surface in kilometer and M is the surface wave
magnitude.
The following equation is proposed by Hu and Zhang (1984) and widely used in attenuation researchin China,log (amax) = 1.71 + 0.657*M − 2.18 log (Dep + 30) ± 0.47 σ(17)

Where, amax is the maximum acceleration from two horizontal components at each site in cm/s2, Dep
is the epicentral distance and M is the magnitude.
Figure 17 illustrates the comparison between the predicted horizontal component values fromequations (15-17) and this study equation (6) for Joyner and Boore (1981) model. This comparison is
performed assuming the magnitude of M = 6.0. Following conclusions can be made from thissection:(i) All attenuation curves are very close and have the consistent decay trend. The results
obtained from other authors are lower than this study at larger distance. It is because other studies
may have simulated results. Simulative recordings need digitalization and different kinds ofcorrection, but the digitalization may introduce long period error which will control the groundmotion values at the long period. In the correction of simulative recording the most important step isfiltering to remove the long period error. However, filtering will inevitably influence the groundmotion at long period, as a result, will underestimate the PGA. The data sets used in this study are50

direct acceleration recordings (extracted by Seismosignal software), without filtering, which are very
proper for study on PGA.
(ii) Within small distances, at 1 km, the predicted values from Cui et al. (2006), Fukushima
and Tanaka (1990), Hu and Zhang (1984) are lower than this study. But as the distance increases
from 4 km to 70 km, the predicted value from this study decreases than the other research.
(iii) At distances such that 10 km to 100 km, the predicted values from Hu and Zhang (1984)
and this study are approximately close.
(iv) If a site is relatively far away (more than 50 km) from the source, predictive peakacceleration from this research is smaller. The design may not be much conservative.

(a)

(b)

Fig. 16 Comparison of attenuation curves for peak horizontal acceleration by Joyner andBoore (1981) (solid lines) with the 50 percentile curves by Bommer and Akkar (2012) (dashedlines) for rock site (a) and for firm soil site (b)
10000
M =6

Peak Horizontal Acceleration (cm/s 2 )Soil Type A

This Study
Fukushima & Tanaka

1000

Cui. Li
Hu

100

10

1
1

10
100
Hypocentral Distance (km)1000

Fig. 17 Comparison of predicted horizontal PGA attenuation curves with respect to distance, M=6.0 for rock site

51

(a)

(b)

Fig. 18 Comparison of predicted PGA values for a M = 6.0 earthquakes between Joyner andBoore (1981) and Bommer and Akkar (2012) for rock (a) and firm soil sites (b)Figure 18 shows comparison of predicted PGA values for magnitude 6 earthquake using
Joyner and Boore (1981) (equations 6 and 10) and Bommer and Akkar (2012) (equations 8 and 12)
GMPEs at various distances for rock as well as firm soil sites. From this Fig. 18, it can be stated thatrock site data fits relatively well with Bommer and Akkar (2012) GMPE whereas firm soil site data
fits well with Joyner and Boore (1981) GMPE.

4 Conclusions
This study deals with the data collected from North-East Indian region and Bangladesh to derive theground motion attenuation relationships. The predicted equations are expressed in terms of PGA,
PGV, ARS and VRS for both horizontal and vertical direction. In this study, predicted attenuation
models are represented with respect to hypocentral distance and magnitude. These results are
consistent with the data from other authors having relatively low standard deviation. This study
states that, the predictive attenuation values for firm soil sites are larger than those of rock sites under
the same conditions for a given earthquake event. Rock sites data fit relatively well with Bommer
and Akkar (2012) model whereas firm soil sites data fit satisfactorily with Joyner and Boore (1981)
model. The amplification effect of soil is demonstrated in this research. Attenuation curves are
magnitude-independent, indicating the magnitude will not change the shapes of the curves. Adding
to it, residuals are distributed approximately log-normally. There is no distance and magnitudedependence of residuals for rock or firm soil sites for both models. Furthermore, the curves forpredicted peak acceleration, are decaying with distance almost linearly in the proper coordinate
scales. However, the predictive equations are sometimes governed by the hypocentral distance. This
study considers hypocentral distance instead of the station-to-fault distance. This might be the a52

shortcoming inducing scattered data points in the models. Another limitation of this research is, itdoes not incorporate any physical model and thus not permit extrapolation beyond the range ofobtained dataset. However, when the strong ground motion data over the entire observed area
becomes plentiful, this limitation will become less critical. For better interpretation of earthquakes
data more observation stations need to be established for obtaining high quality seismic data. The
comparison of this study with other literatures done by authors’ shows consistency of the obtained
results.

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56

PART-V

PEOPLE’S AWARENESS, KNOWLEDGE AND
PERCEPTION INFLUENCING EARTHQUAKEVULNERABILITY OF A COMMUNITY: A
STUDY ON WARD NO. 14, MYMENSINGHMUNICIPALITY, BANGLADESHBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By:

Uttama Barua
Mehedi Ahmed Ansary57

1 Introduction
Earthquake is considered as one of the most deadly natural hazards (Choudhury et al. 2016; Jimee etal. 2012). In recent years, the world has witnessed devastating nature of earthquakes destroying
major cities and large-scale havoc in the surrounding environ- ment (Choudhury et al. 2016; Goda etal. 2015). Apart from casualties, it results in exten- sive damage of physical and social
infrastructures, disruption of life-sustaining facilities, destruction of economic structure, etc. (ADPC
2002; Shinozuka et al. 1998; Alam et al. 2008). Studies in different countries over the years conclude
that such impact of earthquake varies greatly in different countries and even in different localities ina country despite being exposed to similar intensity earthquake (Brouwer et al. 2007; Gallopín 2006;
Smit and Wandel 2006). The most significant reason behind such difference is the variation invulnerability of different community. Hence, it can be said that although inhabitants of hazard prone
areas are at higher risk due to their potential exposure, their vulnerability may vary greatly for theircomplex contexts (Cutter et al. 2003; Morrow 1999; Tapsell et al. 2010). So, for effective disasterrisk reduction and management as well as building longterm resilience against future shocks,

understanding of the root causes of vulnerability is vital (Calgaro and Lloyd 2008). The vulnerabilityof different areas is guided by disparity in rapid urbanization, population growth, migration andunplanned development (CDMP 2014; Flanagan et al. 2011; Singh et al. 2014). In addition to thesefactors, the complex contexts of local people are also considered very important, which varies
significantly from community to community (Morrow 1999; Murphy 2007; Paton and Johnston
2001). These complex contexts include but not limited to personal contexts (age, gender, educational qualification, economic condition, asset ownership status, race, ethnicity, etc.), social capital(household relation, social network, duration of stay in the locality, etc.), power structure, andpeople’s awareness, knowledge and perception about the risk (hazard and vulnerability), etc. (Dynes
2002; Mechanic and Tanner 2007; Morrow 1999; Moser 1998; Murphy 2007; Shi et al. 2018). All
these contexts as a whole give rise to vulnerability (Cutler et al. 2018; Flanagan et al. 2011; Gallopín
2006; Rufat et al. 2015; Singh et al. 2014; Tapsell et al. 2010).
At any extent of disaster, local people in the community are the one who suffer most and they are in
the frontline to respond the disastrous condition. Due to unpredict- able nature in the event of anearthquake, predicting and mitigating the exposure of the people to the hazard is beyond control. But,
the vulnerability of the community can be addressed through understanding their complex contexts
leading to the vulner- ability. Moreover, the community people can be integrated in the process of
disaster management. This should include the coping and survival strategies to face or respond to thecircumstances immediately after an earthquake before the appearance of addi- tional help (Habiba
and Shaw 2012; Habiba et al. 2013). Utilizing local resources, capabilities and knowledge also58

reduce the cost of disaster risk reduction dramatically (Maskrey 2011; Yodmani 2001). In theprocess of reaching consensus and achieving acceptability, their involvement is essential(Bendimerad 2003). Thus, at present, dis- aster risk reduction and management is inclining more andmore to community-based approaches (Allen 2006). Community-based disaster management(CBDM) is consid- ered a more effective tool for disaster management nowadays in this regard(Maskrey 2011; Rahman et al. 2018a, b, c; Yodmani 2001). CBDM is a bottom-up disaster management approach where community people play the central role to assess their own vulnerability tohazards, and develop strategies and resources necessary to manage the vulnerability accordingly(prevention, mitigation, adoption, coping, preparedness, response, rescue, relief and recovery)
utilizing their own physical and social resources (Pandey and Okazaki 2005; Shaw and Okazaki2004). Effective and efficient adoption and implementation of CBDM greatly depends on the active
participation of commu- nity people. Such participation again depends on the complex contexts oflocal people which again varies from community to community (Bendimerad 2003).
Among different complex contexts mentioned earlier, people’s awareness, knowl- edge andperception of risk is most significant because these conditions indicate their capacity to realize aswell as utilize their own social, physical and psychological assets, thereby leading to effective
participation in and implementation of CBDM (Bankoff 2013; Paton and Johnston 2001). Hence,
Ainuddin and Routray (2012) and Paton and Johnston (2001) concluded that vulnerability of acommunity depends on awareness, knowledge as well as perception. Though these factors areinterdependent, there is a distinction among the degree of awareness, knowledge and perception.
This distinction may give rise to their vulnerability. Again other complex contexts of local
community people like personal contexts and social capital have significant influence on hazard
awareness, knowledge and perception of people (Ainuddin and Routray 2012; Alam 2016;
Anderson-Berry 2003; O’Sullivan et al. 2013; Paton et al. 2001; Roder et al. 2016).
Several studies have been carried out on hazard awareness, knowledge and percep- tion of people.
Table 1 shows summary of the literature on hazard awareness, knowl- edge and perception withrespect to hazard(s) considered, country of study and major findings or focus. From the table, it canbe observed that these studies focused on either of the awareness, knowledge and perception, or on
combination of awareness and perception, awareness and knowledge, knowledge and perception, etc.
Again, these researches focused on influence of these factors on different aspects of vulnerability.
But none of these studies focused on understanding the distinction among people’s hazard
awareness, knowledge and perception, how other complex contexts of people may influence thesefactors, and how they influence on the vulnerability of a community as a whole.

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Bangladesh lies in a moderately seismic-prone region signifying potential exposure to earthquake(Akhter 2010; BNBC 1993; BNBC draft 2019). It is positioned at the junction of three plates which
results in the generation of active faults (Akhter 2010). Historical evidence points to major
earthquakes within or close to the country (Bilham et al. 2001; Islam et al. 2011; Sarker et al. 2010).
Several researches have been carried out in Bangladesh on earthquake. These studies mainly focus
on hazard assessment (Carlton et al. 2018; Islam et al. 2011; Sarker et al. 2010), vulnerability
assessment (Das et al. 2018; Jahan et al. 2011; Mazumder et al. 2018; Rahman et al. 2018a, b, c;Rahman et al. 2015), risk assessment (Boni et al. 2017), damage assessment (Ansary et al. 2000),management (Biswas et al. 2018; Rahman et al. 2018a, b, c), etc. Very few studies focused on hazard
awareness, knowledge and perception in Bangladesh (Table 1).
This study aims to explore different dimensions of people’s awareness, knowledge and perception
influencing vulnerability of a community to earthquake. In this regard, the objectives of this research
are firstly to understand the existing condition and dis- tinction among people’s earthquake
awareness, knowledge and their actualized percep- tion in relation to their personal contexts andsocial capital in a community in Bangla- desh and secondly to explore how such distinction
influences earthquake vulnerability of the community.
1.1

Vulnerability

Vulnerability has been defined in different studies differently. Birkmann et al. (2013) sum- marized
the definition based on review of different studies, where vulnerability is defined as “… thepropensity of exposed elements such as physical or capital assets, as well as human being and theirlivelihoods, to experience harm and suffer damage and loss when impacted by single or compound
hazard events…”. In this research, a framework has been proposed to frame vulnerability, risk andsocietal response called MOVE framework (Methods for the Improvement of Vulnerability
Assessment in Europe). Though the frame- work is developed based on European context, theconcept of vulnerability is universal and is applicable in other countries with consideration andadjustment on the basis of local con- texts. According to this framework, vulnerability depends on
hazard (potential occurrence of natural, socio-natural or anthropogenic event), exposure (elements atrisk), susceptibility or fragility (conditions of elements at risk leading to suffer harm), resilience or
response capacity (existing capacity) and adaptive capacity (capacity to improve in future). All thesecomponents lead to increase or reduce vulnerability of a community.

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1.2

Disaster awareness, knowledge and perception influencing vulnerability

There are many studies on people’s hazard awareness, knowledge and perception which aresummarized in Table 1. Nonetheless, none of these literature has compared these concepts. Table 2
shows the indicators which have been considered in different literature to under- stand people’s
hazard awareness, knowledge and perception accordingly. From the table, it can be observed that theindicators of awareness are somewhat overlapping either with knowledge or perception. Suchoverlapping indicates that the interdependence in the nature of the concepts of awareness, knowledge
and perception. But the fact is that these three concepts though are interdependent, do not reflect
same concept.
Hazard awareness indicates whether people are aware of the hazard and corresponding risk or not.
Among different awareness indicators, claim of the people about their hazard awareness is importantbecause they are considered to be more willing to become informed through actively seeking theknowledge (Anderson-Berry 2003; Samaddar et al. 2015). Awareness influences people to develop
adaptive and coping capacity utilizing their own physical and psychological assets and thereby leads
to community resilience at the local levels (Ainuddin and Routray 2012; Bankoff 2013; Habiba et al.
2012; Isa et al. 2018; Jül- ich 2018; O’Sullivan et al. 2013). It is a critical factor to efficiently
implement mitigation and disaster preparedness measures (Anderson-Berry 2003; Bendimerad 2003;
Cumiskey et al. 2018; Shaw et al. 2004; Teo et al. 2018). It maximizes empowerment and reduces
the potential for feelings of helplessness and vulnerability (Back et al. 2009; Bendime- rad 2003).
Thereby, awareness enhances appropriate response (Anderson-Berry 2003; O’Sullivan et al. 2013;
Teo et al. 2018). These facts lower the community vulnerability to a considerable level (Ainuddin
and Routray 2012; Isa et al. 2018).
Such awareness does not necessarily mean possession of scientific hazard knowledge (Kruse et al.
2018). People may be aware of hazard but may not have proper knowledge. It is evident from thestudy of (Anderson-Berry 2003) that “…Campaigns were effective in raising community awarenessof storm surge, although, it did not generally improve residents understanding of this hazard…”.
From Table 2, it can be observed that explor- ing individual’s knowledge about natural hazards has
been considered important because such knowledge increases the ability to assess risks (Soffer et al.
2011). In contrary, lay- person knowledge leads to poor judgments and decisions (Flint and Luloff
2005). Source of knowledge is important because if an individual trust the knowledge source, thenthe acceptability of risk reduction measures increases (Samaddar et al. 2018). Again, direct
experience of past disaster increases hazard knowledge (Alam 2016). For infrequent disas- ters likeearthquake, most people may not have direct experience in their lifetimes. There- fore, researches
considering this factor may have to rely on experience of small disasters or different disasters64

(Becker et al. 2017). Appropriate hazard knowledge motivates people to participate in different
training programs effectively (Becker et al. 2017; Roder et al. 2016). Thus, hazard knowledge
enhances people’s coping capacity as well as their willingness to take risk mitigation andpreparedness approaches (Alam 2016; Anderson-Berry 2003; Becker et al. 2017; Roder et al. 2016;
Soffer et al. 2011; Taghizadeh et al. 2012). Conse- quently, they are more likely to response
appropriately, take part in disaster management activities and thereby reduce casualty (Roder et al.
2016; Soffer et al. 2011).
Based on the awareness and hazard knowledge, people make their perception (Ainud- din andRoutray 2012; Bankoff 2013; Becker et al. 2017; Kruse et al. 2018; Pedoth et al. 2018; Roder et al.
2016). People having perfect understanding about the hazard and its potential impacts and risks at the
local and community levels are more likely to have clear perception (Ainuddin and Routray 2012).
People remembering past disaster experience are more likely to perceive an event more probable(Alam 2016; Paton et al. 2001; Roder et al. 2016). Such perception may or may not be based onreality. In contrary, having appro- priate awareness and adequate knowledge may not lead toappropriate adaptation, prepar- edness and response behavior, because such behavior depends on theperception people develop through processing and internalizing the knowledge (Anderson-Berry
2003; Bott 2016). According to (Anderson-Berry 2003) “…If perceived risk is a true reflection of theactual risk associated with a particular hazard, then mitigation strategies, warning com- pliance andresponse preparedness are likely to be appropriate and vulnerability can be minimized…”. Therefore,risk perception is considered important as well as a challenge to make the risk reduction initiatives
successful (Ainuddin and Routray 2012; Bendimerad 2003; Ho et al. 2008; Paton et al. 2001). In thisregard, several studies have considered different attributes to study perception of local people about
hazard, which are shown in Table 2.
Suitable risk perception leads to effective adoption of mitigation, coping, adaptation andpreparedness actions (Ainuddin and Routray 2012; Alam 2016; Anderson-Berry 2003; Bott 2016;
Habiba et al. 2012; Ho et al. 2008; Morrissey and Reser 2007; Paul and Bhui- yan 2010; Samaddar etal. 2018). Having appropriate risk perception may not always lead to appropriate response, because
appropriate response depends on participation in training, drill and practices (Goidel et al. 2019). But
training programs are more successful if peo- ple perceive the importance and need (Anderson-Berry2003). Thus, realistic perception improves actual behavior and thereby ensures appropriate response(Ainuddin and Routray 2012; Anderson-Berry 2003). Again, participation of local people in the local
disaster management process for effective and efficient adoption and implementation of CBDM
greatly depends on their perception (Bendimerad 2003). Community people can join in such process
as a member in formal local disaster management committee for decision making purpose at the65event of an emergency (Fernandez et al. 2006; Mathbor 2007; Wang and Wan Wart 2007). They canalso participate in various disaster management activities during pre- and post-disaster periods asvolunteers (Alexander 2010; Brennan et al. 2005; Fernandez et al. 2006; Mathbor 2007; Twigg andMosel 2017). It depends on their will- ingness and perception about the importance of suchparticipation (Fernandez et al. 2006;
Samaddar et al. 2015; Van Krieken et al. 2017; Wang and Wan Wart 2007). As a whole, appropriate
awareness, knowledge and perception about hazard leads to reduced vulner- ability and vice versa(Paton and Johnston 2001; Paton et al. 2001; Rufat et al. 2015).
1.3

Personal contexts and social capital influencing vulnerability

Personal contexts and social capital has significant influence on community vulnerability (Cutler et
al. 2018; Flanagan et al. 2011; Gallopín 2006; Rufat et al. 2015; Singh et al. 2014; Tapsell et al.
2010). Personal contexts of people in a community include gender, age, educational qualification,
occupation, economic condition, asset ownership status, race, ethnicity, etc. Women are more
vulnerable to disasters, because they cannot respond to disaster immediately and effectively due totheir responsibilities toward other members of the family (Rahman 1996; Teo et al. 2018). Children
and older people are most vulnerable because they face difficulties to response immediately after adisaster due to their mobility and health issues (Ainuddin and Routray 2012; Back et al. 2009;
Brunkard et al. 2008; Cut- ler et al. 2018; Doocy et al. 2013; Freeman et al. 2015; Isa et al. 2018;
Jonkman et al. 2009; McGuire et al. 2007; Rahman 1996; Taghizadeh et al. 2012; Tapsell et al. 2002;
Wood et al. 2010). Again, young people can greatly contribute to enhance community resilience andrisk management capacity (Back et al. 2009; Freeman et al. 2015; Haynes and Tan- ner2015). Sometraining programs are targeted for young people at present considering the fact that it is easier todisseminate knowledge to households through them (Bendime- rad 2003; Freeman et al. 2015;
Haynes and Tanner 2015). They also have wide scope to be engaged in disaster management
activities through proper training (Back et al. 2009; Bendimerad 2003; Freeman et al. 2015; Haynes
and Tanner 2015; Teo et al. 2018). But, decisions regarding children and young people are taken by
their parents who sometimes do not acknowledge their capacities as social actors to contribute tofamily decision making and to demonstrate empathy for others (Freeman et al. 2015). People withhigher educa- tion can search, obtain and understand the scientific complexities of hazards moreeasily (Anderson-Berry 2003; Ho et al. 2008). They also have more adaptive capacity and more tend
to be prepared (Isa et al. 2018; Taghizadeh et al. 2012). Thereby, educated people can be more easily
encouraged to participate in CBDM (Adhikari et al. 2016). Tenants stay in an area for temporary
basis and therefore lack the sense of responsibility, so they are less worried about vulnerability of the66

area compared to owners who would stay in the area lifelong. This also leads to increased
community vulnerability (Ainuddin and Routray 2012; Morrow 1999; Rahman 1996; Tapsell et al.
2002; Wood et al. 2010).
Social capital includes household relation, social interaction, duration of stay in the locality, etc.,
which influence capacity of individuals and community for disaster man- agement (Ainuddin andRoutray 2012; Cope et al. 2018; Dynes 2002; Mechanic and Tanner 2007; Morrow 1999; Moser
1998; Murphy 2007; Shi et al. 2018; Tapsell et al. 2010). People living in a locality for longer
duration (either in dwelling or in the area generally) are assumed to have strong sense of belonging
to the place and community bonding, as well as better knowledge about the area (Anderson-Berry
2003; Goidel et al. 2019; Roder et al. 2016; Rufat et al. 2015; Taghizadeh et al. 2012; Teo et al.
2018). Thereby, based on common interests and experiences, people in a community form
relationship and lead to increased interaction (Anderson-Berry 2003). Enhancement of social capitalby improving social interaction leads to increasing preparedness, adaptive and coping capacity, andresilience to hazards (Cope et al. 2018; Murphy 2007; Paton et al. 2001; Pedoth et al. 2018; Rufat etal. 2015). Through social interaction, people can share information and offer and receive support inwarning period or at the event of a disaster (Anderson-Berry 2003). Strong social bond absorbs thestress and thereby helps people to respond appropriately (Shaw et al. 2004; Smit and Wandel 2006).
Community having strong bond can also recover fast supported by collective decision making (Murphy 2007). Thus, social bond effectively reduces individual and household vulnerability (AndersonBerry 2003).
2 Methodology of the research
2.1 Study area selection
Mymensingh municipality is situated in Mymensingh district of Dhaka division on the west bank of Old
Brahmaputra River which lies in high earthquake prone zone in Bangla- desh (BBS 2011; BNBC 1993;
BNBC draft 2019; Sarker et al. 2010). From the history of earthquake damage in the study area, it is evident
that the city was significantly damaged by the Great Indian Earthquake of June 12, 1897, with 8.1 magnitude(Sarker et al. 2010). The earthquake also changed the main flow of the Brahmaputra River shifting about 62
miles (100 km) west which co-sided west of the greater Mymensingh region (Goswami and Das 2002).
Considering the potential exposure of Mymensingh municipality to earth- quake, this municipality has been
considered for this research. For the purpose of this study, one among the 21 wards of the municipality (wardis the smallest administrative unit in a municipality in Bangladesh) has been selected as the study area, which
is Ward no. 14. It has been considered as a case study in this research considering two points: Firstly, thisresearch aims to study at local level considering one smallest administrative unit at a time, and secondly, thestudy area will represent the other wards of Mymensingh municipality. Figure 1 shows location of the study
area.
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The study area covers an area of 0.54 square kilometer with 12,142 population and 22,485 persquare kilometer population density. The ratio of male and female population is 47.06 percent and52.94 percent, respectively. The total number of households in the area is 2194, and average family sizeis 4.5 (BBS 2011). The primary occupations of people in the study area are service (80.6%),
agriculture (11.7%) and industry (7.6%). Major land use of the study area includes residential, healthfacility and mixed use (BBS 2011). There are 1611 buildings consisting of 735 pucca structures (URM
and RCC buildings) and rest semi-pucca, kutcha and under-construction buildings (Field survey 2017).

2.2 Data collection
2.2.1 Secondary data collection

For the purpose of this study, the GIS database of Ward no. 14 of Mymensingh Municipal- ity wascollected from Urban Development Directorate (UDD) (UDD 2015). The GIS data- base includes areaboundary, land use, buildings and road network of the area. The Com- munity Series of Statistics ofthe study area (2011) was collected from Bangladesh Bureau of Statistics (BBS) (BBS 2011). These
data were collected to understand basic profile of the study area and to consider as the basis forprimary data collection.

Fig. 1 Location of the study area Ward no. 14, Mymensingh Municipality, Bangladesh. Source: Adapted from UDD(2015)

69

2.2.2 Primary data collection
It is already known that the study area is at risk of exposure to earthquake. But, the earthquake
vulnerability of an area may differ due difference in susceptibility (or fragil- ity) of its different
elements at risk. In case of earthquake, such variation may occur due to difference in soil conditionand different physical features of buildings in the area. To comprehend physical earthquake
vulnerability of the study area, geological data were collected through borehole test at twelve
locations and micro-tremor test at eight loca- tions to understand liquefaction susceptibility of thesoil in the study area which may happen due to an earthquake. To know about earthquake
vulnerability of the buildings in the study area, Rapid Visual Screening (RVS) Method was used tocollect data on condition of 735 buildings in the study area including all pucca (URM and RCC)
struc- tures (FEMA 154 2002).
For the purpose of the study, face-to-face household questionnaire survey was con- ducted in the
study area. The sample size for the questionnaire survey was 700 house- holds including owners(430 households) and tenants (270 households). Separate build- ings were selected for owners andtenants, so that 700 pucca buildings among 735 could be covered. Here, sample size for owners isabout 1.6 times of that of tenants. So while selecting sample, two buildings in a row wereconsidered for owner’s household questionnaire survey and then one for tenant’s household
questionnaire survey. Thus, repeating this process, 700 household samples were selected throughsystematic random sample selection method. For the purpose of sample selection in this method, theGIS database of buildings in the study area was utilized. This survey was conducted parallelto the building vulnerability assessment survey. The survey team consisted of three members—twofrom the locality and one from BUET to. Thus, consistency and reliabil- ity of the data was ensured
through integration of local people in the surveying process.
For household questionnaire survey, firstly a draft questionnaire was prepared based on review ofliterature on disaster awareness, knowledge and perception. The question- naire firstly included
questions to collect basic demographic and economic data of all the members of the households
surveyed. The variables for this purpose included gender, age, educational qualification, occupationand disability. For the purpose of this research, the questionnaire consisted of five sections:awareness, knowledge, perception, personal con- text and social capital. Table 3 shows the factors
considered under each section with corre- sponding questions and type of response collected. After
preparation of draft questionnaire, pilot survey of five households in the study area was conductedin April, 2017. Based on the pilot survey, the questionnaire was finalized. The final household
questionnaire survey was conducted in the May–June 2017. Thereby, basic demographic andeconomic data of all the members of 700 households were collected consisting of 2630 persons, and70

research related data were collected from the respondents considering that the 700 respondents represent 700 families, respectively.
2.3

Data preparation and analysis

To understand actual vulnerability of the study area, firstly the physical earthquake vulner- ability
profile was developed. It included assessment of liquefaction susceptibility in the study area using
the collected geological data through borehole and micro-tremor tests. Earthquake vulnerability of
all the buildings (735 pucca structures) in the study area was assessed based on seismic design
criteria through RVS method on the basis of collected building condition data. Moreover, road
network in the area was analyzed based on the col- lected GIS data (road shape file) to understandwhether the roads are suitable for disaster management after an earthquake. Here, the assumption isthat higher proportion of roads with sufficient road width (minimum 12 ft for movement of both
way traffic) is suitable for response, rescue and evacuation after an earthquake.
The database of all the collected household questionnaire survey data was prepared in statisticalanalysis software SPSS 21. After that, the data were analyzed using MS-Excel 2013 and SPSS 21.
Firstly, the social profile of the households in the study area was devel- oped based on collected
data of 700 sample households consisting of 2630 persons. The analysis for the purpose of theresearch was done based on response of 700 respondents considering that these respondents
represent their households. To understand relation among different variables, descriptive statistics
and contingency tables or cross-tabulation to display the bivariate frequency distribution ofvariables were analyzed. To analyze asso- ciation (strengths and weaknesses) of these relations,
Goodman–Kruskal’s gamma (G) coefficient was computed and interpreted accordingly.
In this research, impact on vulnerability was analyzed utilizing the MOVE framework proposed by
Birkmann et al. (2013), because this framework analyses vulnerability, risk as well as societal
response which is also the basis of analysis in this research. Accord- ing to this framework,
vulnerability depends on hazard (potential occurrence of natural, socio-natural or anthropogenic
event), exposure (elements at risk), susceptibility or fragil- ity (conditions of elements at risk
leading to suffer harm), resilience or response capacity (existing capacity) and adaptive capacity(capacity to improve in future). Here, the general hazard and social profile of the area portray the
exposure and susceptibility or fragility.
On the other hand, difference among awareness, knowledge and perception influenced by personal
contexts and social capital portray resilience or response capacity and adaptive capacity of local people.

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3

Results and discussion

3.1

Actual physical earthquake vulnerability profile of the study area

It is evident from the review of historical earthquake analysis of Mymensingh municipality that the area
is at high vulnerability for exposure to potential earthquake. However, from the study of geological datain the study area, it has been found that the soil in area is not susceptible to liquefaction. Again, from theearthquake vulnerability assessment of the buildings in the study area based on seismic design criteria
through RVS method, it has been found that about 37% buildings are vulnerable to earthquake. Thus,
these buildings are susceptible to damage if exposed to an earthquake. Among the total building stock inthe study area, about 58% buildings have been developed before the year 2000. It repre- sents that asignificant portion of the area is newly developed. Although road network cov- ers 21.8% of total landarea, most of the roads are very narrow in the study area. The width of the roads varies from 1.5 to 12 ft(Field survey 2017). Most of these roads are only accessible through walking, cycle, motorcycle or oneway rickshaw/van due to their width. Only a very few roads including the major thoroughfare (DhakaMymensingh Highway) are accessible for vehicles in both way directions. All these conditions represent
that the study area is highly vulnerable for potential earthquake.
3.2

Social profile of the households in study area

The 700 households surveyed in the study area consist of 2630 persons. Table 4 shows overall social
profile of these people. Gender distribution of male and female among the population is almost equal.
Moreover, about 14% of the people are children (younger than 10 years), about 6% are elderly people(elder than 60 years), and about 0.34% (10 out of 2630 people) are physically disabled. These people areconsidered as ‘vulnerable group’ at the occurrence of any disaster and would need assistance for
evacuation from the affected areas (Hemingway and Priestley 2006; Lord et al. 2016; McGuire et al.
2007). So special attention will be required for these persons in the study area after an earthquake. Majority of the people in the study are young and middle aged (54%). Most of them are liter- ate, with 29%
people having education level graduation or above which is much higher in Bangladesh context. Onethird of the people is student. These people can be utilized to ensure effective development of awareness,knowledge and perception about earthquake in the study area effectively. They can also implement anddirectly participate in the CBDM process in the study area successfully with appropriate training.
3.3

Awareness and knowledge about earthquake

Among 700 surveyed respondents, 85% claimed to be aware of earthquake. Table 5 shows the sourcesfrom which these respondents could learn about earthquake, the knowledge they possess about
earthquake and immediate response, and their past experi- ence of earthquake. From the table, it can be72

observed that majority of these respondents learned about earthquake from electronic media, such as
television, radio, etc. This find- ing is compliant with the findings from other researches (Becker et
al. 2017; Eisen- man et al. 2007; Hurnen and McClure 1997; Jimee et al. 2012; Pedoth et al. 2018;
Teo et al. 2018). Only 9% respondents have attended earthquake-related program. Thus, it can besaid that very few earthquake-related programs were organized in the area where very few
respondents participated. On the other hand, 12% respondents learned about earthquake from theirneighbors. This finding is consistent with that of Cumiskey et al. (2018); Eisenman et al. (2007);
Teo et al. (2018), who concluded that knowledge about hazard is also passed from one person toother through social interaction.
Among the respondents, only about 44% respondents have appropriate (though not scientific)
knowledge about earthquake. About 12% respondents think that earthquake is only the movement ofbuildings, which reflect their lack of knowledge about earth- quake. In case of knowledge about
immediate response, from Table 5 it can be observed that about 88% respondents think that theyshould move to a safe place immediately during an earthquake including moving up to the roof topof a building or getting down from the building, which is not appropriate response. Though about 9%
respondents claimed to know about earthquake through attending earthquake-related program, only
5.4% respondents have appropriate knowledge about immediate response, and only 1.9%
respondents could respond appropriately during their experience of past earthquake.
Here, worth mentioning is the fact that about 32% respondents panicked and showed no response.
Table 6 shows association of respondents’ actual response during past earthquake expe- rience andtheir knowledge about earthquake and immediate response during an earth- quake. Actual response
during past earthquake does not have any significant relation with respondents’ knowledge about
earthquake (G = 0.107, p > 0.05) but has weak and significant relation with their knowledge about
immediate response during an earthquake (G = 0.371, p < 0.05). Thus, people’s lack of knowledge
about appropriate response is reflected by their inappropriate response during past earthquake
experience or not remem- bering the event.
In the study area, there is a Ward Disaster Management Committee (WDMC) which is a government
committee at ward level for disaster management. When respondents were asked about whether theyknow about the WDMC in their area, unfortunately about 84% respondents stated that they do notknow about it. So there is a lack of communication between the authority and the inhabitants of the
area.

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3.4

Perception about earthquake

Only 44% respondents perceive their area to be vulnerable to earthquake. Figure 2 shows reasons ofearthquake vulnerability of the area in ranked order according to the respond- ents. The respondents
mentioned geographic location, unplanned establishment of build- ings and high density ofdevelopment in the area as prime reasons for vulnerability of their area. Though for the purpose ofthis research, earthquake vulnerability of the buildings was assessed, the results were not shared withthe respondents. In such condition, only 26% respondents perceive the building they are residing into
be vulnerable to earthquake. Fig- ure 3 shows reasons of earthquake vulnerability of the buildings inranked order according to the respondents, and Table 7 shows association of the perception withdifferent factors. Old buildings and defective construction practices have been identified as the mostimpor- tant reasons for perceiving their building to be vulnerable to earthquake. The perception isstrongly and significantly associated with year of construction (G = 0.468, p < 0.05). The buildings
constructed before 1995 are considered as most vulnerable by the respondents compared to newly
constructed buildings. Thus, people’s perception is compliant with real- ity regarding age of thebuilding. When compared with the perception of the respondents about earthquake vulnerability andthe actual earthquake vulnerability (according to RVS) of their building, it has been found that eventhough 37% buildings in the study area are actually vulnerable to earthquake, only 26% respondents
think that their building is vul- nerable for damage due to potential earthquake. Only 10%
respondents from vulnerable buildings think that their building is vulnerable. The association is weak
and insignificant (G = 0.017, p > 0.05). When both of the perceptions of respondents about area andbuilding vulnerability to earthquake have been compared, it could be observed that most of the residents (46%) think that neither the area nor their building are vulnerable, with strong and significant
association (G = 0.462, p < 0.05). Such conditions reflect that the residents’ lack of clear perception
about their earthquake vulnerability.
To reduce earthquake vulnerability of the buildings, retrofitting is necessary which depends on thewillingness of the building owner to invest for this purpose. Table 8 shows the comparison of thebuilding owners’ willingness to invest for retrofitting their building. About 39 owners perceive that
their buildings are vulnerable, but they are not willing to invest money to strengthen their buildings.
It can also be seen that 162 own- ers who do not perceive their building vulnerability are also
unwilling to invest money. Again, 70 owners whose buildings are actually vulnerable are not willingto invest for reducing vulnerability of their buildings. On the other hand, 152 owners whose buildings are actually not vulnerable are willing to invest money for strengthening buildings if necessary.
Thus, perception or actual building vulnerability do not reflect owners’ willingness to invest for
retrofitting their building (G = 0.124, p > 0.05 and G = 0.069, p > 0.05, respectively).
74About 67% of the respondents expressed their willingness to get involved in the dis- astermanagement process as volunteer. Table 9 shows the distribution of the mentioned duties ranked by
the respondents willing to get involved in the disaster management pro- cess as volunteer. Most
respondents have given priority to rescue mission and showed interest to participate in this operation.
According to their preference, these willing peo- ple can be utilized for implementation of CBDM in
the study area. Table 10 shows the association of the respondents’ willingness to participate in thedisaster management process in the study area as volunteer with their perception about earthquake
vulner- ability of the area, where weak but significant association can be observed (G = 0.274, p <0.05). Thus, their willingness to participate in CBDM is influenced by their perception about theearthquake vulnerability of their area, which reflect the influence of their perception on theirdecision.

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3.5

Distinction among awareness, knowledge and perception about earthquake

Table 11 shows association of the respondents’ awareness, knowledge and perception about
earthquake. From the table, it can be observed that the earthquake awareness of the respondents has
significant association with their knowledge. It is very strongly associ- ated with their knowledge
about earthquake (G = 0.928, p < 0.05), weakly associated with their knowledge about immediate
response during an earthquake (G = 0.369, p < 0.05), and moderately associated with their pastearthquake experience (G = 0.433, p < 0.05). That is, the lack of knowledge leads to the lack ofawareness. But here the alarming fact is that though 85% respondents claimed to be aware ofearthquake, 41.4% and 80% of them have inappropriate or no knowledge about earthquake andimmediate response during an earth- quake, respectively, and about 83.1% of them could not respond
appropriately or could not recall their past earthquake experience. Overall, it can be said that
although there is significant relation among awareness and knowledge, awareness does notnecessarily mean having appropriate knowledge or does not necessarily ensure appropriate response.
Awareness also has significant association with respondents’ perception about earth- quake
vulnerability of the area and their willingness to participate in the disaster manage- ment in the study
area as volunteer, but the association is insignificant for their percep- tion about earthquake
vulnerability of their building (G = 0.221, p > 0.05). Awareness is strongly associated withrespondents’ perception about earthquake vulnerability of the area (G = 0.622, p < 0.05) andmoderately with their willingness to work as volunteer (G = 0.588, p < 0.05). Thus, most of theaware people perceive their area to be vulnerable and are willing to work as volunteer. This finding
is consistent with other literature who found association of awareness with their perception(Ainuddin and Routray 2012; Bankoff 2013; Morrow 1999; Wood et al. 2010). But, they are
indifferent about the vulnerability of their building or in other sense, vulnerability of their own (G =0.221, p > 0.05.) This find- ing is consistent with that of Paul and Bhuiyan (2010) who stated thatpeople believe that they or their property would not experience damage at the event of a disaster.
Perception of respondents about earthquake vulnerability of the area is weak but sig- nificantly
related to their knowledge about earthquake (G = 0.345, p < 0.05). This finding is consistent with thefindings of Alam (2016); Flint and Luloff (2005); Roder et al. (2016); Soffer et al. (2011), whoconcluded that hazard knowledge has greater influence on people’s hazard perception. In contrary,
there is no significant association between people’s per- ception about earthquake vulnerability of
their building and their knowledge about earth- quake (G = 0.101, p > 0.05). Thus, people develop
their perception based on knowledge, but when it comes to their own vulnerability, then they remain
indifferent about it. This finding is consistent with that of B. K. Paul and Bhuiyan (2010) who statedthat people believe that they or their property would not experience damage at the event of a disaster.
76

Again, willingness of the respondents to work as volunteer has no significant relation with either of
their knowledge about earthquake or earthquake response (G = 0.071, p > 0.05 and G = − 0.025, p >0.05, respectively). But such willingness is weak but significantly related to their past earthquake
experience (G = 0.220, p < 0.05). Overall, it can be said that earth- quake knowledge has little
influence on people’s actual preparedness behavior, which is consistent with Anderson-Berry (2003)
who concluded that hazard knowledge does not necessarily translate into appropriate preparedness
and adaptation behaviors.
3.6

Relation of awareness, knowledge and perception about earthquake with personal context andsocial capital
Tables 12, 13, 14 and 15 show association of the respondents’ awareness, knowledge and perception
about earthquake with their personal contexts including gender, age, education level and building
ownership status, respectively. It is evident Table 12 that the associa- tions of respondents’ genderwith their earthquake awareness and knowledge are very weak as well as insignificant representing
similar level of knowledge and awareness among male and female respondents. This finding
contradicts with that of Teo et al. (2018), who con- cluded that level of awareness varies among maleand female residents. In case of percep- tion, respondents’ gender has very weak and no significant
relation with their perception about earthquake vulnerability of the area (G = 0.093, p > 0.05). Thisfinding complies with that of Roder et al. (2016), stating that men and women feel the danger at thesame level. On the other hand, perception about earthquake vulnerability of the building the respondents are residing is lower among male respondents though the association is weak and sig- nificant(G = − 0.225, p < 0.05). Again, willingness to work as volunteer for disaster man- agement ismoderately and significantly associated with gender (G = 0.486, p < 0.05). Male respondents are
more willing compared to female respondents to work as volunteer. It is also evident in the researchof Soffer et al. (2011) who found that men are more confident to respond than female. Table 13
shows that the associations of respondents’ age with their earthquake awareness, knowledge as wellas perception are very weak as well as insig- nificant representing similar level of awareness,knowledge and perception among different age group of the respondents. This finding contradicts
with that of Ho et al. (2008); Roder et al. (2016) who stated that older people have greater perception
compared to younger.
Table 14 shows that awareness, knowledge and perception of the respondents about earthquake
differs with their education level. Awareness of the respondents about earth- quake, knowledge about
earthquake and their past earthquake experience, and perception about earthquake vulnerability of
the area have negative very weak but significant asso- ciation with education level (G = − 0.199, p <77

0.05; G = − 0.183, p < 0.05; G = − 0.183, p < 0.05; G = − 0.199, p < 0.05, respectively). Willingness
to work as volunteer has nega- tive weak but significant association with their education level (G = −0.270, p < 0.05). It represents that educated people are more aware as well as have better knowledge
and per- ception about earthquake. This complies with the findings of other studies (Cutler et al.
2018; Habiba et al. 2012; Shaw et al. 2004; Soffer et al. 2011; Taghizadeh et al. 2012; Teo et al.
2018). In contrary, the association of respondents’ education level is very weak and insignificant
with their knowledge about immediate response and perception about build- ing vulnerability.
From Table 15, it can be observed that respondents’ building ownership status has very weak but
significant association with their earthquake awareness (G = 0.197, p < 0.05). Thus, owners are moreaware of earthquake. Despite this fact, respondents’ perception about earthquake vulnerability of
their building and their willingness to work as volunteer are negatively and significantly related toownership, where the relations are very weak and weak, respectively (G = − 0.16, p < 0.05 and G = −0.250, p < 0.05 correspondingly). Thus, despite having better awareness, owners are in denial of thevulnerability of their building and are not willing to work as volunteer. In contrary, tenants havebetter perception about vulnerability and willingness to get involved in disaster management
regardless of having lower awareness. Ownership has very weak with no significant association withknowl- edge (about earthquake, immediate response and past experience) and perception about
earthquake vulnerability of the area (G = 0.033, p > 0.05; G = 0.030, p > 0.05; G = − 0.152, p > 0.05;
and G = − 0.091, p > 0.05, respectively). This finding contradicts with other researches who foundthat tenants stay in an area for temporary basis so they are less wor- ried about vulnerability of thearea compared to owners who would stay in the area lifelong (Ainuddin and Routray 2012; Morrow
1999; Rahman 1996; Tapsell et al. 2002; Wood et al. 2010).
Tables 16 and 17 show association of the respondents’ awareness, knowledge and percep- tion about
earthquake with their social capital including social interaction and duration of stay of respondents inthe study area, respectively. From the analysis, it has been found that about 75.8% respondents in the
study area discuss about earthquake with their acquaintances, especially with their family members(69.28%). Only 27.86% respondents discuss with the local people, whereas 25.43% respondents
discuss with other building users. Mainly female, middle-aged and literate people take part in suchdiscussions. When this social interaction of respondents has been compared with their awareness,knowledge and perception about earthquake, it has been found that respondents’ social interaction
has significant association with their earthquake awareness, knowledge as well as perception, except
for their knowl- edge about immediate response during an earthquake (G = 0.205, p > 0.05). Social
interaction has very strong relation with respondents’ awareness (G = 0.818, p < 0.05), moderate
relation with earthquake knowledge (G = 0.459, p < 0.05), weak relation with past earthquake
78

experi- ence (G = 0.250, p < 0.05), moderate relation with perception about earthquake vulnerabilityof the area (G = 0.431, p < 0.05), weak association with perception about earthquake vulner- abilityof their building (G = 0.293, p < 0.05), and moderate association with respondents’ willingness toparticipate in disaster management as volunteer (G = 0.512, p < 0.05). Thus, respondents whointeract more with their acquaintance are more aware, have more appropri- ate knowledge and havebetter perception about earthquake. This finding is consistent with that of other researches (Cope etal. 2018; Rufat et al. 2015; Smit and Wandel 2006).
From the analysis, it has also been found that about 40% respondents have been living in the study
area for more than 20 years, whereas only about 6% of them have been living in this area for lessthan a year. Based on findings from other researches, it is expected that people living in a locality for
longer duration have strong sense of belonging to the place and community bonding as well as betterknowledge about the area which in turn lead to better hazard perception and effective participation in
local disaster management (Teo et al. 2018; Wang and Wan Wart 2007). But in this study it has beenfound that respondents’ duration of stay in the study area has very weak as well as no significant
influence on their social interaction, and awareness, knowledge and perception about earthquake.
Both new and old residents have similar social interaction, and they have expressed analogous
aware- ness, knowledge and perception about earthquake.
3.7

Influence of awareness, knowledge and perception about earthquake on vulnerability of the

area
Tables 18 and 19 show influence of general physical and social profile of the area and awareness,knowledge and perception of respondents about earthquake on the vulnerability of the area. From
Table 18, it can be observed that the study area is vulnerable to earth- quake due to the hazard,
exposure and fragility of local people. From Table 19, it can be observed that the difference foundamong people’s earthquake awareness, knowledge and perception influences their existing response
capacity as well as adaptive capacity. Such influence impacts on vulnerability of the study area
reducing in some case and increas- ing in some other. Aware people are capable of accepting therisk. But with inappropriate knowledge, they are unable to understand the risk leading to poor
judgments and deci- sions regarding earthquake management and inappropriate response leading toincreased casualty. Moreover, people develop their perception based on their awareness and to someextent based on earthquake knowledge. Thus, misconception leads to wrong perception about
earthquake. Most of the people are also not capable of accepting and understand- ing their own
vulnerability. The WDMC can co-ordinate the disaster management in the study area. But with thelack of communication between the authority and the inhabitants, WDMC will fail to do so. All these79scenarios lead to reduce response capacity and resil- ience of local people in the study area. People
with inappropriate knowledge may not feel the urge to attend training programs and take necessary
actions. Again, people without having proper perception will not participate in different training
programs willingly. Thus, adaptive capacity of local people in hampered. All these factors lead toincreasing their earthquake vulnerability. On the other hand, aware people can also be easily
encouraged to participate in different training programs. People have greater trust on electronic
media and also have good social interaction which can be utilized in the future for knowledge andcapacity building for disaster management in the future. The building owners can be guided to invest
for reducing the fragility of their building accordingly. People are willing to work as volunteer basedon their past earthquake experience irrespective of the knowl- edge they possess. These people canbe involved in CBDM in the study area. The WDMC can take necessary measures in this regard for
future capacity building.

4 Conclusion
This research is intended to understand existing condition and difference among people’s awarenesslevel, knowledge and perception as well as their underlying personal and social contexts influencing
earthquake vulnerability of a community in Mymensingh, Bangladesh.
Majority of the respondents stated that they are aware of earthquake. These people are expected to becapable of accepting the risk. On the other hand, aware people can also be easily encouraged toparticipate in different training programs. The main source of their knowledge is electronic media.
Very few people attended earthquake-related pro- gram representing infrequency in organization andlack of participation in such programs. Respondents could also learn about earthquake from theirneighbors. The trust of people in these sources can be utilized in the future for knowledge andcapacity building for disaster management in the future. Unfortunately, a significant portion of the
respondents do not have appropriate knowledge about earthquake and its immediate response.
Moreover, the respondents attending earthquake-related programs do not possess appropriate
knowledge regarding immediate response. Such finding reflects that even the few earthquake-related
programs were not effective to train the people about appropriate response during an earth- quake.
With inappropriate knowledge and misconception, people are unable to understand the risk leading
to poor judgments and decisions regarding earthquake management and inappropriate response
leading to increased casualty. This is reflected by their inappropri- ate response during pastearthquake experience or not remembering the event. Moreover, people with inappropriate
knowledge may not feel the urge to attend training programs and take necessary actions. The analysis
shows that the lack of knowledge leads to lack of awareness. But though a significant portion of the80

respondents claimed to be aware, majority of them do not have appropriate knowledge about
earthquake and the immediate response. Thus, knowledge of the respondents about earthquake andimmediate response do not comply with their stated awareness. This awareness and knowledge of
people in the study area are not related to respondents’ gender or age. But educated people are moreaware and have better knowledge.
Despite the inconsistency between earthquake awareness and knowledge, people develop theirperception based on their awareness and to some extent based on earthquake knowledge. They havestrong perception about earthquake vulnerability of the area as well as the building they are residing
in. Such perception is higher among educated people irre- spective of their gender and age. But theperception is inconsistent with the actual build- ing vulnerability. Thus, misconception andinappropriate knowledge lead to wrong percep- tion about earthquake. Most of the people are notcapable of accepting and understanding their own vulnerability and thereby avoiding necessary
actions to address the vulnerability. Again, such denial is higher among male respondents andowners irrespective of their age or education level. People without having proper perception will nottake the risk seriously and thereby will not feel the urge to participate in different training programs
willingly. Major portion of the owners are willing to invest for retrofitting and thereby strengthening
their building, though such willingness is not guided by their perception or actual vulner- ability of
their building. It will be easier to motivate the owners to strengthen their build- ing where necessary.
In contrary, as owners are developing their perception without prop- erly understanding thevulnerability, at the time of execution they may not act accordingly though they have expressed their
willingness to do so.
There is WDMC for disaster management at local level in the study area which can co-ordinate thedisaster management in the study area and can take necessary measures for capacity building. But
with the lack of communication between the authority and the inhabitants, WDMC will fail to do so.
Though most of the respondents do not know about the presence of WDMC in their area, majority ofthem are willing to get involved in dis- aster management process in the area as volunteer. Aware
people and people remembering past earthquake experience showed more willingness in this regard,
where knowledge has no significant influence on such perception. Male and educated respondents
especially who are living in the area as tenants are more willing to participate in disastermanagement. These people can be involved in CBDM in the study area.
For addressing the difference among earthquake awareness, knowledge and perception of local
people and thereby reducing their vulnerability, different initiatives for awareness raising andknowledge sharing will have to be taken. In this regard, the findings of this study should beconsidered. Electronic media, training programs and social network can be utilized further because81people are used to and already have trust in these sources. Innova- tive and informative programs canbe designed to be telecasted in electronic media. All the sources should contain appropriate
information including separate training programs for awareness raising and knowledge about
earthquake and associated risk; preparedness and immediate response measures, and capacity
building for CBDM. Awareness raising and knowledge about earthquake and associated risk willbring out local people from their misconception, build the capacity of the people to understand theirown vulnerability and thereby leading to better perception. Special attention will have to be given on
building owners so that they can come out of their denial and can accept their own vulnerabil- ity. If
necessary, separate training programs will have to be organized for them to build their capacity tounderstand the earthquake vulnerability of their buildings themselves at preliminary level. They
should be able to realize the situation when they should contact experts for detailed assessment of
their building. They will also have to be motivated to take necessary measures to address theirbuilding vulnerability accordingly with proper understanding and reasoning. The awareness andknowledge training program should also focus on motivating people to participate in CBDM because
better awareness and percep- tion have found to lead people to higher willingness to work as
volunteer. Female, less educated residents and owners are less willing to participate in disastermanagement. Additionally, young people are more energetic and enthusiastic, and they are asset fora locality. So, special focus will have to be given on female and young people to motivate their
participation in this regard. Hands on training and drill programs should be organized at local level toensure effective preparedness and immediate response. If people attend this program after the firstone, then they will be able to learn and grab the preparedness and response measures effectively withproper reasoning supported by their appropriate knowledge. Planning will have to be done toeffectively implement CBDM in the study area considering peoples’ preference of duty. Training
programs will have to be organized for capacity building of the willing people to work as volunteer
according to their preferred duty. Such consideration of preference will ensure their effective
participation in CBDM.
These initiatives are not enough to achieve the goal if people do not participate here effectively.
Participation of all gender, age and education group of people irrespective of their building
ownership status should be ensured. From the analysis, it has been found that willingness toparticipate is influence by peoples’ awareness and perception. So peo- ple will have to be motivatedat first to attend the training programs on awareness raising and knowledge about earthquake andassociated risk. In light of appropriate awareness and knowledge, people will be motivated toparticipate in training programs on earthquake pre- paredness and response, and capacity building for
CBDM. As educated people in the study area have been found to be more aware, have better82perception, have higher willingness to work as volunteer and can readily understand the information,
they can be utilized here to motivate local people to participate. Moreover, young people can also be
utilized here. Such motivation can be done utilizing the social interaction among the residents which
has been found very effective in the study area. Moreover, training of trainer’s method can beutilized here for capacity building at local level utilizing their own resource. In this regard, educatedand young people in the study area may be given training at first and then they can be utilized indifferent training programs as trainer. This will in turn increase acceptability among local people
motivating them to participate in the training programs. To effectively implement CBDM in the
study area, capacity of the WDMC will have to be strengthened to establish link and communication
with local people and organize earthquake-related training programs themselves at local level
without outside help or intervention.
This study brings out that people’s awareness, knowledge and perception about earth- quake hassignificant impact on vulnerability of a community. Without consideration of these contexts,
different measures may fail to reduce vulnerability of a community due to the lack of relevance,
applicability or acceptance. Therefore, it is necessary to understand the context of local people’s
awareness, knowledge and perception about the hazard in a community. This research can be utilized
as a guideline for this purpose in other areas of the country, as well as in other countries withnecessary adjustment and modification considering local context.

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Philippines91PART-VI

ANALYSIS AND SEISMIC RETROFITTINGPROCEDURE OF UNREINFORCED MASONRYBUILDING-A CASE STUDYBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By:

Ishfaq Aziz
Shamontee Aziz
Raquib Ahsan
Mehedi Ahmed Ansary

92Introduction
In the context of structures of Bangladesh masonry structures, comprise a good portion of the totalbuildings built in the twentieth century. Majority of the unreinforced masonry structures still
standing today were built in the Pakistan period (1949-1971). At that time any sort of nationalbuilding regulations or provisions of seismic codes did not properly exist. Owing to the tectonic
framework and existence of three major active fault lines within the country (Raihanul et al, 2016)
and being the most densely populated region in the world, Bangladesh is in imminent threat ofseismic hazard in a catastrophic scale. Due to the structural inadequacy and existence in a country
vulnerable to seismic hazard, these non-engineered structures may sometimes turn out to be real life
incarnations of death traps. The present use of these masonry structures as government offices,
headquarters, factory units, religious centers and even residential quarters increases the risk ofdamage and casualties in case of a seismic event. So, engineering assessment of these structures withproperly developed procedures along with required retrofitting and rehabilitation is a dire need.
For Unreinforced Masonry structures the load bearing walls are subjected to gravity loading andlateral loading (in case of wind and earthquake). In addition to compressive stresses due to gravity
loading, the walls also must experience shear stresses due to both gravity and lateral load. While
exposed to strong earthquakes, creation of tensile and shearing stresses in the walls of masonrybuildings is the primary cause of damage suffered by these structures. To ensure minimum casualties
in an earthquake either the seismic demand on the structure has to be reduced by including additional
shear walls, infill walls, steel bracing, base isolation devices or by retrofitting (i.e. Ferro-cement, RC
overlay, RC jacketing) of all the structural members concerned (load bearing walls, beams, slabsetc). The primary objective is to increase its seismic resistance to a defined performance level as per
the design code’s (BNBC 1993) requirement so that the probability of total failure/collapse remains
out of the equation. These procedures are largely based on academic studies carried out by
researchers. For example, Gorai and Maiti (2016) presented various techniques for strengthening
damaged structures. Jadhav and Patil (2012) investigated a 60 years old RC water tank and its
structural members which had reinforcement corrosion and deteriorating concrete and those were
repaired using reinforced cement grouting and polymer mortar. Chaturvedi and Patel (2016)
observed improved behavior of RC jacketed beams in terms of deflection and strength. Seleem et al(2003) evaluated the static response of beams retrofitted with U shaped RC jackets in terms ofstrength, stiffness and composite action. The test result indicated a vital effect of the presence ofstirrup in the concreting jackets in enhancement of both stiffness and ultimate load capacity of theretrofitted beam. Surface roughness is a vital determinant in retrofitted beam performance level.
Cheong and MacAlevey (2000) observed differences in behavior of jacketed beam whose interface
93was fully and partially roughened. RC jacketed beams showed significant improvement in flexure
capacity and mechanical behavior, compared to ordinary reinforced beams of the same dimension,even though cores of the jacketed beams were damaged (Fatih, 2004). Shehata et al (2009)
investigated the impact of shear stud in strengthening existing beams by addition of high strength
concrete layers. As per studies by Sachin and Urmil (2012), use of shear connectors and bonding
agent paired with micro-concrete is much more efficient, compared to other jacketing alternatives, inenhancing the performance level of beams.
One method of retrofitting of the masonry walls is ‘Ferro-cement lamination’, which, on infilled
masonry changes the shear dominated failure to flexure failure. Under lateral cyclic loading, aneffective mesh reinforcement of 0.16% increases the lateral strength, initial stiffness and energy
dissipation approximately by two folds (Sen et al, 2019). The composites ductile strength isdependent on the effective volume fraction, mesh type and its orientation within the matrix (Prawel
and Reinhorn, 1982). Ferro-cement overlay having wire mesh oriented at 45° had the highest
ultimate capacity compared to other orientations. But in perspective of strength to cost ratio wire
mesh having 0° orientation had the most economically efficient outcome (Prem et al, 2010). Under
static cyclic test, this retrofitting technique increased the in-plane lateral resistance by a factor of 1.5(Abrams and Lynch 2001). Study by Reinhorn and Prawal (1985) led to the definition of the failure
mechanism. According to their research, the ferro-cement overlay either fails by diagonal tension(ductile) or de-attachment (bond failure). Bond anchors have a dominant effect on the developmentof these mechanism. Another effective way of increasing the shear strength of the load bearing walls
to the extent that flexural failure consistently occurs is to apply a thin layer of cement plaster overhigh strength steel reinforcement (Sheppard and Tercelj, 1980). This improves the in-plane resistanceof shear by a factor of 1.25 – 2.90 (Jabarov et al, 1980, Sheppard and Tercelj, 1980). The out-ofplane resistance is substantially improved as is the composite ductility. The improvement of strength
depends on the strengthening layer thickness, cement mortar strength, reinforcement quantity andmeans of bonding with the retrofitted wall and the degree of masonry damage (ElGawady et al,2004). There are several case studies reported in professional literature where reinforced plastercoatings were used to strengthen existing masonry structures (Willie and Dean, 1984, Guoliang,1980). Ghiasi et al (2013) explained different retrofitting options for URM foundation and theirapplicability form a practical perspective. He strategized to rehabilitate the structure either byphysical extension of the existing foundation or by improvement of subsoil layers. Apart from these,
there have been several case studies leading to complete restoration of historical masonry structure
based on finite element analysis and laboratory experimental data (Bozkurt et al, 2016; Hancilar etal, 2011; Sayin et al, 2019).
94

This study was initiated considering the vulnerability of the URM structures in case of earthquake in
Bangladesh. It started with background survey and appraisal of in-built condition of the structure.
The required parameters for Detailed Engineering Assessment (DEA) were assessed by conducting
in-situ as well as laboratory tests. Next, Finite element analysis of the structure was done as per thefield reports to analyze its response to seismic loading under the current state. Based on the
numerical analysis results, laboratory tests and past researches, retrofitting techniques were designed
for all the load bearing members which were found to be inadequate. Finally, the retrofitting designs
were implemented which was followed by a quality control program. Though the studies conductedby researchers and professional literatures depict building retrofitting procedure, there are nocomprehensive reference on unreinforced masonry structures rehabilitation. So, this study aims toserve as a representative application of research-based methodology in seismic retrofitting of URM
structures in developing countries.

Methodology
Building features
A visual observation of an unreinforced masonry building having plan dimension of 31m × 10.7 m(102’ × 35’) was done to note down the existing condition of the building and some structuralfeatures to facilitate the further study. The building was constructed in 1960 and have been using as
an office building. There are two identical stories from ground floor with two additional stories
extension on southern (Grid A to C of Figure 3.5b) side. Some building features are summarized inTable 1.
Table 1: Building features of the studied building

Building features
Floor system
Floor area
Foundation system
Load bearing wall
thickness
Floor height
Construction material

Description
Beam supported RC slab
332 m2 (3570 sqft) per floor
Wall foundation
375mm on ground floor, 250mm on the other stories
Ground floor: 4.47m (14.66 ft), Other floors: 3.20m(10.50 ft)
Brick and reinforced concrete

Visual observation
Survey of the as-built condition of the existing structure was carried out to verify the as –built
condition of the whole structure. Initial measurements were taken to prepare as-built drawings.
Structural conditions of various structural members of the building were assessed during the fieldsurvey. Apparently, the structural members of the building were in a good condition. No visible signs95

of cracks and distress were found in the beams, load bearing walls and slabs. Some of walls on the
ground floor appeared to be damp. Partition walls were found to be weak. Figure 3.1 shows the load
bearing brick columns, which will be susceptible to short column failure under seismic loading.

Figure Error! No text of specified style in document..1 Load bearing brick columns

Data collection and testing
Strength of the concrete in the existing beams and slabs has been assessed by extracting concretecore samples. Brick aggregate was used in the concrete as coarse aggregate. The compressive
strength test of concrete cylindrical core was conducted according to ASTM C42. Diameter of thecore sample was 68mm with minimum length/diameter ratio of 1.0. The core strength test results are
summarized in Table 2. Combine failure represents the failure of both mortar and aggregate.
Table 2: Core strength test results

Sl. No.

Location

Sample Identification

1
2
3

GF
GF

GF Roof, C-1
GF Roof, C-2
1st floor
roof C-1
1st floor
roof C-2
1st floor
roof C-3
1st floor
Beam-1: C-1
1st floor
Beam-5: C-2

1st floor

4

1st floor

5

1st floor

6

1st floor

7

1st floor

Crushing
strength
MPa (psi)
11.4 (1605)
11.9 (1730)

Type of failure

12.1 (1750)

Combined

8.1 (1170)

Combined

24.5 (3550)

Combined

21.8 (3160)

Combined

17.7 (2570)

Combined

96

Combined
Combined

Compressive strength test of brick was done as per ASTM C67. Two samples were collected, andcompressive strength were found to be 20.75 MPa and 17.51 MPa. Brick shear test (Table 3) was
done on masonry wall in order to estimate allowable shear strength Fv, which in turn was used to
calculate allowable compressive stress f’m and modulus of Elasticity of masonry Em using proposed
equation of Bangladesh National Building Code (BNBC 1993). Minimum shear stress was used to bein safe side. The compressive strength of brick, compressive strength of masonry and modulus ofelasticity of brick were found to be 17.51 MPa, 7.5 MPa and 5625 MPa respectively. Mortar grade
was M2 having mix proportion of 1:4. These values would be used for structural analysis of themasonry structure.
Table 3: Experimental data of brick shear test on masonry wall

No.

Location

1
2

GF Interior wall (G/1-2)
GF Exterior Wall (K/12)

Shear Stress, σ
Mpa (psi)
10.34 (1500)
6.89 (1000)

Area, A1mm2 (in2)
858 (1.33)
858 (1.33)

Load, PkN (lb)8.87 (1995)
5.92 (1330)

Presence of reinforcement in beams and slabs were done using Ferroscan. The determination of rebar
diameter using ferroscan imaging is questionable. The standard deviations of the rebar diametermeasurement results are significantly large, meaning the spread of the measurement data arescattered over a large area. (Salek, 2015). This gives the impression of unreliable measurement
outcomes. So, in capacity assessment of the beam and slab, smallest possible diameter (16mm forbeam, 10mm for slab) was assumed.
Ferro-scan report showed the 1st floor beams (400mm x 300mm) to have 4 tension reinforcement.
As for the 2nd floor beams having cross section of 500mm x 300mm the reports showed 5 rebar in
the tension zone. For analysis, the rebars were assumed to be 16mm diameter bars. Schematic
diagrams of the beams are depicted in Figure 3.2. As for slab, the ferro-scan reports show, the rebars
were spaced at 6” c/c in both directions. Ferroscan test results are shown in Figure 3.3.
The standard penetration test (SPT) is performed to obtain an approximate measure of bearing
capacity and dynamic soil resistance. SPT value suggests that, the upper soil (up to 6.0 m depth)
corresponds to very stiff cohesive soil which is suitable for moderate load bearing structures(Terzaghi and Peck, 1967). Ground water level at -5.75m EGL. Soil test results are shown in Table 4
and Table 5.

97

113

500

113

400

300
300
(a)

(b)

Figure Error! No text of specified style in document..2 Schematic diagram of floor beams, (a) 1st floor (b)2nd floor

(a)

(b)

(c)

Figure Error! No text of specified style in document..3 Ferroscan of GF beam E1‐E2 [(a) lateral (b) bottom]and (c) slab

Finite element modelling
As the extreme ends of the reinforced concrete beams rested in masonry wall, they tend to behave
like simply supported beams. Therefore, they are assigned hinge restraints in FE model. The slabs
were modelled as shell element. The load bearing wall were modelled as membrane. Element
dimensions were taken from the as-built drawing. Average compressive strength of 13.8 MPa (2 ksi)
was judiciously selected for structural analysis of the building. Minimum yield strength (275.79
MPa) and tensile strength (482.63MPa) of the 40 grade rebar were taken for the analysis purpose.
Seismic loading parameters e.g. zone coefficient (Z=0.15), strength reduction factor (R=6.0),
importance coefficient (I=1.0), exposure category A (urban-sub urban area), wind load (V=210
Km/hr), are taken from BNBC 1993 as per site location. Site coefficient was taken 1.0 as the soil isstiff clay at foundation level. Surface area method was used for wind load analysis and maximum
wind load deflection limit h/500

was taken considering 100% wind effect. The building wasanalyzed for gravity loads (dead and live loads) as well as code specified lateral loads. Loading usedin the finite element analysis is shown in Table 6. The 3D model and the plan view of the structure isshown in Figure 3.5.
98

Table 4: Soil property borelog-1

Depth
(m)
1.0 - 6.0
6.0 - 9.0
9.0 –
15.0

Soil property BH-1
0

BH‐1

BH‐2

SPT value

SPT value

6 12 18 24

00

Stiff to very stiff silty clay. (Highplasticity)
Medium stiff to stiff, clayey silt withfine sand. (Medium compressible)
Medium dense, silty fine sand, trace
mica

1.5

1.5

3

3

4.5

4.5

1.0 - 6.0
6.0 - 9.0
9.0 –
15.0

Medium stiff to very stiff silty clay.
(High plasticity)
Stiff, clayey silt with fine sand.
(Medium compressible)
Medium dense, silty fine sand, tracemica

6

Depth (m)Soil property BH-1

Depth (m)Table 5: Soil property borelog-2

Depth
(m)

6 12 18 24

0

6

7.5

7.5

9

9

10.5

10.5

12

12

13.5

13.5

15

15

SPT – BH 1

SPT – BH 2

Figure Error! No text of specified style in document..4Standard penetration test on site

Sensitivity Analysis
Sensitivity analysis was done on the developed model to choose the optimum size and number ofmesh for which results obtained will be reliable and accurate to the desired degree and at the sametime, it will require optimal time to run and execute the analysis of the model. Coarser meshproduced lower stresses in the walls with respect to finer mesh. So, optimum mesh sizes wereselected by trial and error considering two vital factors.
Analysis run time99

Results having similar accuracy compared to those with finer mesh size.
An illustration of a small part of the wall in elevation 1 is depicted in

(a)

(b)

Figure Error! No text of specified style in document..5 Complete finite element model of URM building[(a) 3D model (b) Typical plan]
Table 6: Super imposed static loads

Loading type
Live load (office building)Live load on porch
Live load on stairs
Internal partition wall load
Floor finish wall load

Code specified load (kN/m2)
2.87
0.48
4.80
1.44
1.44

Table 7: Sensitivity Analysis of the structure

Number of shellelements

40

128

512

1152

Maximum Shear
Stress for 10-inch
wall

93

100

106.5

108

% change in stress---7.53 %

6.5 %

1.41 %

Image of a Wall

Results and Discussion
Adequacy check of load bearing walls

100The structural system of the building is primarily unreinforced masonry system resting on wall
foundations. According to the BNBC 1993 guideline, Working Stress Design (WSD) method wasused to access the structural adequacy of the masonry elements of the structure.
Slenderness ratio check
According to the BNBC 1993, masonry wall’s slenderness ratio shall not exceed 20. The slenderness
ratio of the walls of this structure falls within this limit, which can be seen, from Table 8.
Table 8: Slenderness ratio of the building

Story details

Effective height

Slenderness
Ratio
11.7

Comment

175.5 in (4.46 m)

Effective
thickness
15 in (0.38 m)

Groundfloorst
1 - 3rd floor

115.5 in (2.93 m)

10 in (0.25 m)

11.55

SatisfiedSatisfied

Maximum compressive stress (Fa) check
In unreinforced masonry structures, load-bearing walls are by far the most vulnerable element toseismic loading. These elements are primarily designed to carry vertical loads by compression.
Allowable limit of axial compressive stress, Fa (Eqn 1; Eqn 4.3.1 BNBC 1993) was calculated to be
1.38 MPa (200 psi). As shown in Table 9, the load bearing walls satisfy the vertical compressive
stress requirement due to service load combination (Dead+ Live).
F_a= 〖f'〗_m/5 [1-(h'/42t)^3 ](1)

Where, h’ is the effective height of the wall and t is the effective thickness of the masonry wall.
Table 9: Maximum axial compressive stress generated in FEM model for load bearing walls

Wall
Designation
1A-1L
1D-1K
2A-2B
2C-2K
A1-A2
B1-B2
C1-C2
C4-C5
G1-G2
K1-K2

Generated Max
compressive stressMPa (psi)
0.52-0.59* (75-85*)
0.52-0.59* (75-85*)
0.69* (100*)
0.69* (100*)
0.62 (90)0.83 (120)0.52 (75)0.21* (30*)
0.34 (50)0.41 (60)Remarks(Limit state)
SatisfiedSatisfiedSatisfiedSatisfiedSatisfiedSatisfiedSatisfiedSatisfiedSatisfiedSatisfied*In very small portions of the wall surface the stress values have been found to be much greater thanthe maximum values presented in Table 9. So those small portions were neglected since these were
too small compared to the whole wall surface. For example, the point of the wall at which a beam101

rests the compressive stress is above 120 psi, and even at some points it is greater than the allowable
limit due to stress concentration. So, these points of stress concentration are to be taken into account
while retrofitting of the structure.
Maximum shear stress Fv check
In seismic event, load-bearing walls are subjected to repetitive in plane and out of plane horizontal
loading. Due to not having any reinforcing element they have insignificant resistance to horizontal
shear stress. The allowable limit of shear stress of unreinforced masonry was calculated to be
0.069MPa (10 psi) (Eqn 2; Eqn 4.3.5, BNBC 1993 and Eqn 6.7.6, BNBC 2017).
F_v=0.025√(〖f'〗_m )<0.40 N/mm2(2)

Table 10 shows the generated shear stress on the masonry wall for service load combination (Dead +Live) as well as lateral loading (Dead + Earthquake). Majority of the wall section are vulnerable tofailure under horizontal shear. Figure 4.1 shows the visual representation in typical floor plan viewof the adequate and inadequate walls for both the loading criteria of BNBC 1993 and 2017. The
seismic forces calculated as per BNBC 2017 were much higher than those calculated as per BNBC
2006. This is because, the Response Reduction Factor (R), for load bearing unreinforced masonry
structure is 1.5 in BNBC 2017 which is much less than that of BNBC 2006 (R=5). So, the allowable
limit of shear stresses due to dead and seismic forces of BNBC 2017 is exceeded for all the walls.
(Figure 4.1). The retrofitting was also done taking consideration into the forces of higher magnitudes(Seismic forces of BNBC 2017)
Table 10: Maximum shear stress generated in FEM model for load bearing walls

Wall
Designation

Maximumshear stress
D+L
MPa (psi)

Remarks(Limit state)

1A-1L
1D-1K
2A-2B
2C-2K
A1-A2
B1-B2
C1-C2
C4-C5
G1-G2
K1-K2

0.07* (10*)
0.01* (2*)
0.10 (14)0.04* (6*)
0.20 (29)0.21 (30)0.14 (20)0.06* (8*)
0.09 (13)0.03* (5*)

SatisfiedSatisfied
Not Satisfied
Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Satisfied
Not Satisfied
Satisfied

Maximumshear stress
D+EQ
MPa (psi)
BNBC 1993
0.14 (21)0.03* (5*)
0.19 (27)0.19* (28*)
0.15 (22)0.16 (23)0.19 (28)0.06* (8*)
0.15 (22)0.10 (15)102

Remarks(Limit state)
Not Satisfied
Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Satisfied
Not Satisfied
Not Satisfied

Maximumshear stress
D+EQ
MPa (psi)
BNBC 20170.76 (110)0.09 (13)0.90 (130)0.57 (83)0.37 (54)0.39 (56)0.50 (73)0.16* (23*)
0.43 (63)0.31 (45)Remarks(Limit state)
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied

Adequate wall section

(a)

(b)

Inadequate wall section

Figure 4.1:Wall condition in typical floor plan (a) BNBC 1993 EQ loading condition (b) BNBC 2017 EQ
loading condition

At some small locations of the walls, actual maximum stress was found to be much greater than thetabulated maximum values (asterisk marked). This may have happened due to stress concentration at
locations where beams rest on walls or at sharp corners (i.e. Window/door frames). These werediscarded as they covered a negligible portion of the wall surface and were not representative of theactual maximum stress.

Adequacy check of Reinforced Concrete Members
Adequacy check of beams
The dimensions of all the beams of 1st floor (Ground floor roof) and 2nd floor (1st floor roof) arerespectively 400mm × 300mm and 500mm × 300mm. The Ferro-scan test was able to determine thenumber and location of longitudinal bars in the beams but gave a vague idea about the exact diameterof the rebar. So, to be most conservative, 16mm diameter reinforcement bars were assumed as
longitudinal bars during beam adequacy check. None of the beams were adequate to withstand theservice load moment as shown in Table 11 and Table 12. The inadequacy of the all the beams wasdue to the combination ‘1.4DL + 1.7LL’.
Table 11: Analysis result of beam (1st floor)

Beamdesignation
1D-2D
1E-2E
1F-2F
1H-2H
1I-2I
1J-2J

Steel requirement(mm2)
Compression
TensionOverstressed
Overstressed
Overstressed
Overstressed
Overstressed
Overstressed
Overstressed
Overstressed
Overstressed
Overstressed
Overstressed
Overstressed

Comment
Failure – Inadequate section
Failure – Inadequate section
Failure – Inadequate section
Failure – Inadequate section
Failure – Inadequate section
Failure – Inadequate section

Table 12: Analysis result of beam (2nd floor)
103

Beam
designation
1D-2D
1E-2E
1F-2F
1H-2H
1I-2I
1J-2J

Steel requirement(mm2)
Compression
Tension
2845
1865
2860
1883
3011
2057
2393
1345
2587
1569
2691
1688

Comment
Failure – Inadequate reinforcement
Failure – Inadequate reinforcement
Failure – Inadequate reinforcement
Failure – Inadequate reinforcement
Failure – Inadequate reinforcement
Failure – Inadequate reinforcement

Adequacy check of columns and slabs
The design of RCC column assuming a minimum of 1% of rebar percentage was found to beadequate as per FEM analysis and the governing combination was ‘1.4DL + 1.7LL’.
According to the analysis, 10 mm bars placed 150 mm apart is adequate. The scan reports of theslabs suggest that rebars are spaced at around 140 mm c/c along short direction of the slabs which
conforms to the structural requirement of the slab. The maximum deflection of the slabs and theminimum slab thickness were checked and found to be within the limit prescribed by the code.
Adequacy check of wall foundation
The foundation system of the building consists of wall foundation. Figure 4.2 shows the excavatedfoundation of exterior and interior wall, which have a depth of approximately 0.94m and 1.0m
respectively. From the borelog, the top layer is found to be medium stiff to very stiff silty clay withan average SPT value of nearly 6.

(a)

(b)

Figure 4.2: Foundation excavation [(a) External wall footing (b) Internal wall footing]

Undrained shear strength of clay, Cu was estimated from the SPT-N value from co-relation set byHera et al (1974). Using Skempton's bearing capacity factor Nc = 6.7 for strip footing, the bearing
104

capacity of the soil is estimated to be approximately 2.25 tsf. A factor of safety of 3.25 wasconsidered for future extension. From the finite element analysis, the support reactions of the walls
were found due to service gravity load which were used to find the stress on the soil to further
compare it to the allowable bearing capacity of the soil. It was finally found that the provided
foundation was inadequate against bearing capacity failure.

Retrofitting of structural members
Retrofitting of RC beams
Reinforced concrete structural element can lose their strength and stiffness due to repetitive dynamic
loading forcing inelastic deformation, incorrect design and degradation of building materials. In thiscase U shaped RC jackets were used for retrofitting of RC beams. This was chosen as among otheravailable options (externally bonded steels plates, fiber reinforced plastic (FRP), Carbon fiber
reinforced polymer (CFRP)) are prone to premature de-lamination failure, due to mismatch of tensile
strength and stiffness with that of RC elements. These are also quite labor intensive and costly. Table
13 and Table 14 show the governing moments in all the beams.
Table 13: Analysis result of beam (1st floor)

Beamdesignation

Max bending
moment – kN-m
BNBC 1993

1D-2D
1E-2E
1F-2F
1H-2H
1I-2I
1J-2J

389.42393.77412.86332.77356.38366.31Governingbending moment –kN-m
BNBC 1993

412.86Max bending
moment – kNm
BNBC 2017349.84353.75370.98298.73320.03328.99Governingbending moment– kN-m
BNBC 2017

370.98

Table 14: Analysis result of beam (2nd floor)

Beamdesignation

Max bending
moment – kN-m
(BNBC 1993)

1D-2D
1E-2E
1F-2F
1H-2H
1I-2I

400.17402.38423.84336.09363.64Governingbending moment –kN-m(BNBC 1993)
423.84105

Max bending
moment – kNm (BNBC
2017)
359.20361.16380.52301.37326.22Governingbending moment– kN-m(BNBC 2017)
380.52

1J-2J

378.38339.52

RC jacketing is done by extension of the existing beam cross section, with a new layer of highstrength concrete reinforced with both shear and flexural reinforcement of 500 MPa. Shear
reinforcement was designed as per IMRF requirement. For the retrofit design the effective slab
thickness of 100mm was taken into account. Retrofitted extension of depth and width was taken to
be 100 mm. So, the new dimensions of 1st floor and 2nd floor retrofitted beams became 600mm ×500mm and 700mm × 500mm respectively. Table 15 and Table 16 present the retrofitting design ofbeams.
The existing reinforcement was considered to be 40 grade 16mm diameter rebar. Only 50% of theirarea was considered to be effective in resisting flexural tension as these were not at the same
effective depth as the newly retrofitted bar. For retrofitting of beams the concrete mix ratio waschosen as 1:1.2:1.7. 5 mm down stone chips were used as coarse aggregate and 100% Sylhet sand (F.
M.=2.4) was used as fine aggregates. Super plasticizer was used as an admixture. The compression
zones of the beams mostly consist of the old concrete which has a compressive strength of 13.8 MPa(2 ksi) and the newly cast concrete (〖f'〗_c=35 MPa or 5 ksi) was mostly in the tension zone whichis expected to be undergo flexural cracking. So, the concrete will contribute only in compression andthus the compressive strength was considered as 13.8 MPa (2 ksi) in retrofitting design.
Table 15: Retrofitting solution for 1st floor beam considering different material strength
Rebarsfc'
Description
Code
Momentbdfy(kN-m) (mm) (mm) (MPa) (MPa)BNBC
412.86
500
500
500
13.79
5#7
1993
1st Floorbeam
BNBC
370.98
500
500
500
13.79
4#7
2017
Table 16: Retrofitting solution for 2nd floor beam considering different materials
Description Code Moment
b
d
fy
fc'
Rebars(kN-m) (mm) (mm) (MPa) (MPa)BNBC 423.84
500
600
500
13.79
4#7
nd
1993
2 Floorbeam
BNBC 380.52
500
600
500
13.79
3#7
2017
To facilitate the retrofitting work, at first the existing plaster was removed, and old concrete wasthoroughly chiseled (fully roughened surface) before retrofitting. Air blower was used to remove
dust and clean off any loose debris. U shaped stirrups (10mm diameter bars @ 100mm c/c at
106supports and 150mm c/c at mid span) were drilled (75mm) into the existing slab, securing it by
epoxy. Longitudinal bars were placed as per design requirement. To ensure composite action
between the concrete interfaces 10mm diameter @ 150mm c/c shear keys (one end hooked) wereembedded alternatively (staggered pattern) into the existing beam. Embedded length was 75mm.
Surface bonding reagents was applied to bind the new concrete with the existing surface. Steelshuttering and propping were set in place. For concreting, 150mm diameter holes were drilled into
the top slab. Concrete strength for beam was 35 MPa which was confirmed by cylinder test with 28
days curing.
Figure 4.3 and Figure 4.4 present the beam cross section of the beam with the details of the flexural
design. Figure 4.5 and Figure 4.6 show the long section of beam along with shear reinforcement.

Figure 4.3: Retrofitted beam of 1st floor

Figure 4 4: Retrofitted beam of 2nd floor

107Figure 4.5: Longitudinal section of retrofitted beam of 1st floor

Figure 4.6: Longitudinal section of retrofitted beam of 2nd floor

Retrofitting of Masonry Walls
Two alternative methods of retrofitting of the masonry load bearing walls were proposed viz. byferrocement overlay and by reinforced concrete jacketing. However, due to shear strength
requirement of the structure for higher seismic loading of BNBC 2017, thicker ferrocement overlay
with more wire meshes was found to be cumbersome for construction, for which reinforced concrete
jacketing was done for retrofitting of all the walls in the existing building. Both the methods are still
described in this section with short details of the procedure.
Ferrocement Overlay
One of the economically feasible retrofitting methods is ferrocement retrofitting. Ferro-cement is anorthotropic composite material having a high strength cement mortar matrix reinforced with steel
wires in the form of a mesh (Horizontal/diagonal). It improves ductile strength and in-plane inelastic
deformation capacity making the retrofitted structure more resilient against seismic forces. As it iscost effective and low labor intensive which make it economically feasible for developing countries.
The direct tensile strength is dependent on the bonding between the existing masonry and composite
layer, the reinforcement distribution within the matrix, its orientation w.r.t the load direction (Prawel
and Reinhorn, 1982) and volume fraction〖 V〗_f. V_f is the ratio of volume of mesh reinforcement
to the volume of composite layer (reinforcement mesh and matrix). Ferro-cement reinforced with108

square/rectangular mesh, the volume fraction and effective area of reinforcement were calculatedusing following equation mentioned in the Eqn 3 (BNBC 1993, Appendix D) V_f= (Nπd_b^2)/4h (1/D_t + 1/D_l ) (3)Where, Dt and Dl are center to center spacing of wires aligned transversely and longitudinally inreinforcement mesh; db is diameter of the wire mesh; h is thickness of the composite layer(reinforcement mesh and matrix) and N is the number of layers of reinforcements. Effective area ofreinforcement (Asi) per layer of mesh reinforcement in resisting direct tensile stress in cracked ferrocement section can be estimated by (Eqn 4; BNBC 1993, Eqn 12.4.2, Rahman 2002) –A_si= ηV_f A_c(4)

Where, η is global efficiency factor of mesh reinforcement and Ac is cross sectional area of theferro-cement overlay. From this effective reinforcement area 〖(A〗_si) nominal tensile resistancewas determined (Eqn 5; BNBC 1993, Eqn 12.4.3). Allowable tensile stress of the wire mesh wastaken to be 60% of the yield strength f_y (BNBC 1993, Art. 12.4.3.2).
N_n= 0.6A_si f_y(5)

A systematic procedure was proposed for the retrofitting purpose. The existing plaster of themasonry wall was chipped off up to a 20mm depth. The chipped wall was thoroughly cleaned by air
blower to remove dust and loose debris of chipped bricks. For cracked masonry walls, cement slurry
was applied to ensure proper bonding between the cracks. Galvanized nails were drilled in (75mm)
according to design specification. Next, 5mm thick cement mortar was troweled onto the surface ofmasonry wall. Cement mortar was made by mixing ordinary portland cement of type I with naturalriver sand as fine aggregate passing through no. 4 sieve (2.36mm) having fineness modulus in rangeof 2.6 ~ 2.65. Mixing proportion of the mortar was 1:2.5 with water cement ratio of 0.45. This wasfollowed by the attachment of 18 gauge (A.W.G) wire mesh having 8.5mm opening to the newly
plastered masonry wall surface. The wire mesh was held in place by previously affixed 32mm nails.
The nails were fixed in by epoxy in a square grid pattern (250mm c/c). As cover, a second layer ofmortar with thickness of 10mm was applied on the wire mesh. In case of multiple layers of wire
mesh, spacers were placed in between the reinforcement layers to control their position. Then cement
mortar was passed between the 18 gauge meshes aided by a high-speed surface vibrator. The
retrofitted wall was cured for 28 days with hessians. Detailing of the ferrocement design issummarized in Table 17.
Table 17: Ferro-cement retrofitting of the walls detailed design (BNBC 1993 loading)Max. shear
stress
(psi)
1A-1L
21
Wall

A.G.S.

Description

ThicknessVf

18 gauge

External side:

19mm

2.03%

109

CapacityNn (psi)
31.39

2A-2B
2C-2K
A1-A2
B1-B2
C1-C2
G1-G2

27
28
22
23
28
22

K1-K2

15

2-layer wire mesh

18 gauge

External side:1layer wire mesh

16mm

1.21%

15.70

Reinforced concrete retrofitting
The construction of a Ferro-cement section consisting of more than two wire mesh layers ispractically cumbersome. Therefore, for high stresses where two wire meshes are not sufficient
reinforced concrete jacketing of the walls can serve as a potential alternative for retrofitting.
However, in this case the total shear force is to be taken by the RC section. The design of the RC
section is similar to that of the reinforced concrete shear wall. Three-inch (3”) shear wall was
proposed and the total shear is taken by the concrete (V_c) and the steel (V_s). This strengthening
method leads to significant improvement in the shear resistance of the jacketed walls (Churilov andJovanoska, 2013). Detailing of the reinforced concrete retrofitted wall is shown in Figure 4.7.
Same procedure was followed for the RC retrofitting purpose. This steel reinforcement was arranged
as a vertical and horizontal square mesh of 10mm diameter bars, spaced at 150mm c/c (Figure 4.7). 8
mm dia shear keys were installed spanning 1000 mm in both directions. They are affixed 75mm into
the surface of the exiting masonry wall by epoxy resin. Before concrete casting surface bonding
reagents was applied to bind the new concrete with the existing surface. The placement and spacingof the shear keys (in case of RC jacketing) should be such that those are to be inserted in mortar
joints of the masonry walls and not in bricks in order to prevent cracks in the bricks. Material
properties and detailing of the reinforcement is shown in Table 18 and Table 19.

Figure 4.7: Detailing of reinforced concrete wall retrofit
Table 18: Material propertied of retrofitting materials

Material Properties
Yield strength of steel deformed bar:Compressive strength of concrete:

500 MPa34.5 MPa
110

Concrete mix ratio:
Coarse aggregate:

1:1.2:1.7
5mm down stone chips
100% Sylhet sand as fineaggregate
Super plasticizer

Fine aggregate:
Admixture:

Table 19: Reinforced concrete overlay design requirement (BNBC 2017 loading)WallMax. shear
stress
(psi)

1A1L
2A2B
2C2K
A1A2
B1B2
C1C2
C4C5
G1G2
K1K2

Final
Design details

Reinforcement
Detailing(lb/in)#3 @ 187.5 mmc/c#3 @ 187.5 mmc/c#3 @ 262.5 mmc/c#3 @ 462.5 mmc/c#3 @ 437.5 mmc/c#3 @ 312.5 mmc/c#3 @ 2525 mm
c/c

110

1100

130

1300

83

830

54

540

56

560

73

730

23

230

63

630

#3 @ 375 mm c/c

45

450

#3 @ 612.5 mmc/c#3 @ 6 inch c/ci.e.
10 mm @ 150 mm c/c
Vertical andHorizontal direction

Retrofitting of Footing
After retrofitting, the building will be vertically extended upto 4 story. So, from a full 4-story finite
element model, vertical reactions, F_z at the supports (along base of the wall) for unfactored gravity
loads were extracted. With these, the additional load of the 3-inch RC jacket and wall footing below
grade were added to find the total vertical load per unit length of the wall footing. Then it wasconverted to stress and compared to the allowable capacity (3.1 tsf for a factor of safety equal to 4)of the soil. Major portion of the existing wall footing was found to be inadequate against bearing
capacity failure.
The most cost effective and viable retrofitting option is lateral extension of the entire strip offoundation. This was used to determine the required amount of extension to the footing width tocomply with the allowable bearing capacity of 3.08 tsf.
Table 20: Required horizontal extension of strip footing
Elevation

Wall

Load per unit

Extension on Each
111

Governing

1

2
A
B
C
G
K

1A1L
1L1K
2A2B
2C2K
A1A2
B1B2
C1C2
G1G2
K1-k2

length(kN/m)

Side
(mm)

188.3

60.9

177.1

42.4

283.6

219.9

250.0

163.8

189.4

62.8

192.8

68.4

205.8

90.2

171.2

32.5

164.4

21.2

Extension(mm)

225 mm oneach side

Table 20 shows the required extension for each wall. Horizontal extension was done in two
increments. The retrofit stripes were casted using reinforced concrete, which was merged with theexternal 75mm thick retrofitted RC jacket above EGL.
Figure 4.8 shows the details of retrofitted strip footing. Baseline - existing ground level: As
reinforcement 12 mm diameter L shaped bars were placed vertically along with straight horizontal
bars at an equal spacing of 250 mm in both directions.
The reinforcement of the walls (for RC jacketing) are continued to the foundation. On the other side
rebars are provided as temperature and shrinkage reinforcement.
At its base 2-12mm diameter horizontal rebar were used as corner reinforcement. These were set
along the length of the strip foundation with shear key holding them in place. 12mm diameter shear
key at 250mm spacing were used with an embedded length of 75mm. The shear keys were drilled inand fixed by epoxy.

Figure 4.8: Strip footing detailing
112

Conclusion and Recommendation
To summarize, we can conclude that a case study has been presented here where a masonry building
was selected for study. The detailed engineering assessment of the building was conducted as per thenational building code which was followed by the retrofitting of the whole structure as required. The
following points from the whole study are primarily outlined.
The stresses on the load bearing walls were investigated for the seismic loading of the boththe versions of the national building code (BNBC 1993 and BNBC 2017) and it was found that thestress due to the loading of BNBC 2017 was much higher than that of BNBC 1993 due to a low
response modification factor, R for masonry load bearing walls in BNBC 2017 compared to the 1993
version.
Alternative methods of retrofitting were proposed for the load bearing walls where it was also
recommended that for higher stresses the ferrocement jacketing was not feasible for construction due
to the requirement of more than two layers of wire mesh. For these cases, Reinforced Concrete
Jacketing of the masonry load bearing walls turns out to be a handy solution to withstand high shear
stress due to lateral load and at the same time it will contribute in resisting the gravity loading alongwith the existing wall.
The retrofitting of the inadequate beams was done by Reinforced concrete jacketing which
increased the depth and the reinforcement of the beams resulting in increased flexural strength.
The sizes of the base of the wall footing were increased to reduce the stress under footing andkeep it below the bearing capacity of the soil.
In case of newly cast concrete in walls, beams and footings for retrofitting, the shear keys
were installed in all member to ensure load transfer between the existing and the newly built
structural portions.
Thus, this study is expected to provide elaborate details of the assessment of the age-old masonry
structures by Finite Element Analysis as well as the design and procedures of the required retrofittingof all the structural components involved.

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116PART-VII

INTEGRATION OF EARTHQUAKE RISKSENSITIVITY IN LANDUSE PLANNING: AN
APPROACH FOR AN AREA UNDERDEVELOPMENT AT LOCAL LEVELBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By:

Uttama Barua
Mehedi Ahmed Ansary117

1. Introduction
Earthquake is one of the most destructive natural hazards which occur less frequently than floods,
but can affect much larger areas and can have long-lasting economic, social, and political effects(Akhter, 2010; Erdik, 2006; World Bank & EMI, 2014). In recent decades, earthquake risk in cities
have increased due to the high rate of urbanization, unplanned growth, faulty land-use planning, poor
construction practice, inadequate infrastructure and services provision and environmental
degradation, which is again compounded due to unplanned urbanization and development in highrisk zone (Erdik, 2006). It is evident from the experiences of historical earthquakes in different
countries that, the occurrence of an earthquake cannot be prevented but proper risk reduction andmanagement measures can reduce the risk and consequences of this devastating event. In Japan,
earthquake destruction especially during the Great East Japan Earthquake in 2011 could be reduced
significantly compared to its magnitude through disaster risk reduction and management initiatives(Mimura et al., 2011; WBI, 2012). A good lesson learned from the comparative analysis of Haiti and
the Chile earthquakes in 2010 reveals that, although the Chilean earthquake was 31 times stronger
and released about 178 times more energy (need to check??) than the Haiti earthquake, the smaller
quake produced more extensive damage in Haiti due to lack of disaster risk reduction andmanagement initiatives (Bapat, 2010; Bárcena et al., 2010; DesRoches et al., 2011; Wayman, 2010).
In recent years, Risk-sensitive Land Use Planning (RSLUP) is getting significant importance among
different approaches for disaster risk reduction and management. Landuse planning is a complex
process to rationalize and regulate activities of individuals and groups in order to promote thesustainable use and development of land embracing both the natural and the built environment (WBI,
2006b). Sound and equitable landuse planning and management is very effective approach to ensuresustainable natural hazard mitigation to ensure lower disaster losses of human life, physical,
environmental and institutional assets ( Becker & Johnston, 2002; Becker et al., 2013; Fat-Helbary
et al., 2004). Therefore in the process of landuse planning, consideration of disaster risk reduction
and management is very important along with social, economic and environmental requirements of asociety (WBI, 2006b). In general, the process of mainstreaming disaster risk management parametersin land use planning is termed as RSLUP. RSLUP promotes urban growth without generating new
risks, helps identify and mitigate the root causes of disaster like those entrenched in existing landdevelopment practices, modifies and reduces vulnerable conditions of people and places, preempts
disaster damage before it happens rather than cleaning up after, reduces vulnerability and losses ofpeople and increases their ability to recover and hasten the process of reconstruction andrehabilitation (Godschalk et al., 1998; WBI, 2006e; World Bank & EMI, 2014). Hence RSLUP canplay a significant role to reduce earthquake damage effectively (Olshansky, 2001).
118

RSLUP is more effective for application at local level as it can address the local issues in detail(Bendimerad & von Einsiedel, 2010; Erdik & Durukal, 2008; Fat-Helbary et al., 2004; Motamed etal., 2012). Application of RSLUP also depends on the state of development in an area. It is mostconvenient to apply RSLUP while planning for development in a new area (WBI, 2006e). In
contrary, in case of developed area, the scope of RSLUP is very limited where some strategies for
improvement or redevelopment can be proposed (WBI, 2006e, 2006f). In an area at development
phase there is scope to apply RSLUP, but is challenging to some extent. It can be done throughproper correction and modification of the proposed plan (Fat-Helbary et al., 2004). Therefore, study
on RSLUP in an area under development at local level is necessary.
Numerous studies have been carried out in different countries on RSLUP, e.g. New Zealand ( Becker& Johnston, 2002; Becker et al., 2013), Egypt (Fat-Helbary et al., 2004), Iran (Motamed et al., 2012),
Taiwan (Hung et al., 2013), China (Kim & Rowe, 2013), India (Kamat, 2015), Nepal (EMI, 2010),
Istanbul (Erdik & Durukal, 2008; Ilkisik et al., 2013; WBI, 2006d), Philippines (Bendimerad & von
Einsiedel, 2010; Reyes, 2004; WBI, 2006a), etc. Burby, Deyle, Godschalk, and Olshansky (2000);and Hudson (2011) focused on implementation of RSLUP through policy initiatives by local, federal
as well as state governments. Çabuk (2002); Wang (2012); and Hofer, Marquis, Veith, and Ceci
(2013) studied and proposed approaches for RSLUP. Erdik and Durukal (2008); Becker andJohnston (2002) and Becker et al. (2013) investigated the integration of earthquake hazards inlanduse plans and policy statements at the national level. Kim and Rowe (2013) and Kamat (2015)
tested the effectiveness of integrating risk sensitivity in urban master plans to limit future
development in disaster-prone areas considering earthquake as well as other hazards at the regional
level. Bendimerad and von Einsiedel (2010); EMI (2010); Fat-Helbary et al. (2004); Hung et al.
(2013); Motamed et al. (2012); Reyes (2004) integrated earthquake risk sensitivity in landuse
planning at the city level. But none of the researches focused on earthquake RSLUP of an area underdevelopment at local level.
In this background this research aims to understand earthquake risk sensitivity of a landuse plan of
an area at development phase at local level and to formulate RSLUP strategies to be incorporated in
the landuse plan addressing the risk sensitivity.

2. Methodology of the study
2.1 Selection of the study area and reference area
The historical trend of seismicity and some recent tremors occurred in Bangladesh and adjoining
areas indicate that the country is at high risk of exposure to potential earthquake (Akhter, 2010;
European Commission, 2017; Kamal, 2013; Roy, 2014). Dhaka, the capital city of Bangladesh is119experiencing unplanned land-use without disaster risk consideration, and violation of building codesand by-laws (Hasnat & Hoque, 2016; World Bank & EMI, 2014). Such rapid and uncontrolled
urbanization is increasing the vulnerability of populations and infrastructure (CDMP, 2009b; UDD &ADPC, 2016; World Bank & EMI, 2014). Therefore different national and international studies haveidentified Dhaka to be highly vulnerable to earthquake (Davidson & Shah, 1997; Davidson et al.,
2000; Kamal, 2013; RADIUS, 2000). Considering the earthquake vulnerability of Dhaka city,
several researches have been carried out focusing on earthquake risk assessment and management(Ahmed et al., 2012; Ansary et al., 2004; Jahan, 2010; Rahman et al., 2012; Rahman et al., 2015;
Sadat et al., 2010). Several initiatives have been taken by the Government of the Republic ofBangladesh (GoB) for integrating risk sensitivity in landuse planning process for sub-districts andmunicipalities, Mymensingh Town, and Rajshahi Metropolitan area (RDA, 2019; UDD, 2011, 2013;
UDD & ADPC, 2016). But none of the researches focused on earthquake RSLUP in Bangladesh.
The URMT is one of the township projects initiated by Capital Development Authority (RAJUK)(RAJUK, 2016). The purpose of such housing projects are to reduce the pressure of population in
Dhaka city; maintain the balance of environment by proper urbanization; reduce the existing acuteproblem of housing; expand civic facilities; development of new township and expansion of
economic facilities; and solve future housing demand (RAJUK, 2016). The URMT project wasplanned to be developed in three phases (i.e. 1st, 2nd and 3rd). Among them, the first phase wasstarted in 1966 and completed by around 1992. Immediately after the completion of first phase, thesecond phase was initiated and was completed by around 1998. It consists of 1.77 square km land
and is divided into four sectors (i.e. 11, 12, 13 and 14). Under the project area, 5152 housing plots
were allotted. Among them about 1000 plots are still vacant (Field survey, 2016). Thus till date 80%
development of buildings has been completed in the area.
At present, the third phase of URMT is under development process (RAJUK, 2010, 2016). It is
located in northern part of Dhaka Metropolitan area, which has been identified as highly earthquake
prone area (EMI, 2014). The project area of the study area includes around 9.11 square kilometer
land. It is divided into four sectors, i.e. 15, 16, 17 and 18. Additionally, separate area is selected forcommercial purpose designated as “Central Plaza”. Figure 1 shows the sectors in the study area. The
four sectors are further divided into 40 neighborhoods. According to the present development trend
in Bangladesh, the current land development projects of public agencies are allocating a part of the
projects for multistoried apartment block housing schemes (Shamsuzzaman, 2014). Consistent withthis trend, gross area of 0.87 square kilometer of land in the study area is allocated for multi-storied
apartment buildings titled “Uttara Apartment Project”, which is designated in sector 18. Based on the120

size of apartments to be provided, the sector is divided into three blocks (Block A, B, and C)
according to the proposed plan.

Figure 1: Sectors in the study area (URMT 3rd phase)

According to RAJUK (2016), land development works in the study area has mostly been completed.
About 92% of site development work has already been done and about 70% road construction work
has already been completed. The construction of apartment buildings in Block A (Sector 18) is at the
final phase and these apartments have already been allocated. Dhaka Electricity Supply Co.
(DESCO) is doing the electrification work of the project area as deposit work who will takenecessary action to connect and supply of electricity as per allotter’s demand. Electric poles havealready been partially installed in all sectors. Dhaka WASA will execute the work of water supply inthe study area as deposit work and construction of deep tube well and pipeline network will startsoon. Gas line installation work will be executed with the consultation with TITAS Gas Company.
Remaining work is in progress. So, still there is scope to reduce earthquake risk in this area throughrevision of the landuse plan integrating RSLUP strategies. Considering these conditions, third phaseof URMT has been selected as the study area for this research.
To understand the earthquake risk of the study area, it is necessary to study the development trend(consistency of landuse with proposed plan and trend of building height) and earthquake
vulnerability of the building stocks. But the study area selected for this research is at development121phase. Therefore, it is necessary to select a recently developed area assuming that similar
development will take place in the study area, considering it as the reference area for this research.
For this purpose, the second phase of URMT has been considered as the reference area in thisresearch. Figure 2 shows satellite image of the study area (URMT 3rd phase) and the reference area
(URMT 2nd phase) along with their location in Dhaka city in the inset. From the figure it can beobserved that the study area and the reference area are located in the north-western part of Dhaka
city.

Source: Google Satellite Image (2016) and RAJUK (2016)
Figure 2: Satellite image and location of the study area (URMT 3rd phase) and the reference area (URMT 2nd phase)

2.2 Data collection and processing
Necessary data required for this research have been collected from secondary and primary sources.
Landuse map of the study area (Third Phase of URMT) and reference area (Second Phase of URMT)
have been collected from RAJUK (RAJUK, 2010, 2016). The GIS shapefiles of geomorphology andsuitability of structures considering the geomorphic unit of Dhaka Metropolitan area have beencollected from Geological Survey Of Bangladesh (GSB) (GSB, 2016). Furthermore, the map ofDetailed Area Plan (DAP) for Dhaka Metropolitan area has been collected from RAJUK (RAJUK,2011). From these collected resources, data of the study area have been extracted for this research.
Primary data have been collected mainly from the reference area (Second Phase of URMT) to
understand development trend and earthquake vulnerability of the building stocks. The time frame ofthe surveys has been September 2016 to December 2016. To understand development trend in thereference area, a field survey has been carried out to collect data regarding present landuse, vacant122

plots and building information (building use and number of storey). To assess earthquake
vulnerability of buildings in the reference area, a survey has been conducted for sample buildings.
Total number of buildings in the reference area is 3,777 [4152??] (Field survey, 2016). To conduct
the survey, a sample size of 360 buildings has been taken considering the confidence level at 95%
and confidence interval at five. Samples have been chosen in the reference area through stratified
random systematic sampling in two steps. At first step, samples have been distributed in thereference area according to the proportion of buildings in each of the four sectors. At the second step,
samples in each of the four sectors have been distributed on the basis of the proportion of buildings
according to plot size and number of stories. To ensure even distribution of the samples in thereference area, sample buildings have been chosen from each of the road segments in the sectors
accordingly. After selection of sample buildings in the reference area, a sidewalk survey with visualinspection and limited data collection has been utilized to collect building-related information forearthquake vulnerability assessment.
2.3 Assessment of earthquake risk sensitivity of the landuse plan of the study area
The study area is at the risk for exposure to potential earthquake (EMI, 2014). So to formulate
earthquake RSLUP strategies to be integrated in the landuse plan of the study area, it has beennecessary firstly to understand its risk sensitivity. The evaluation has been done on the basis of somerisk sectors or risk themes. Risk sectors or themes are distinctly manageable clusters of probabilities
and vulnerabilities for which a coordinated approach action is necessary through exploration of risk
factors in each sector (WBI, 2006f). World Bank and EMI (2014) has proposed some risk themes
and their corresponding risk factors in Risk-Sensitive Land Use Planning Guidebook for Dhaka city
under Bangladesh Urban Earthquake Resilience Project. Considering the context of this research andthe status of development of the study area, eight of the risk themes and their corresponding risk
factors have been selected. The landuse plan of the study area has been analyzed from theperspectives of these risk themes and corresponding factors to understand it’s risk sensitivity toearthquake. Table 1 shows risk themes, risk factors and corresponding methodology for analysis ofearthquake risk sensitivity of the landuse plan of the study area. Need to mention Table 2
somewhere???

123

Table 1: Risk themes, risk factors and corresponding methodology for analysis
Risk themes
1. Macro-formrisks

2. Risks in urban
texture3. Risks in landuse
incompatibilities

4. Risks inhazardous uses

5. Special risk
areas

Risk factors
1.1 Development andsettlement pattern in thehighhazard-prone zone
1.2 Connectedness to the restof the city
2.1 Availability of resources
2.2 Interconnectedness in thearea as means of escape andaccess to rescue and relief
2.3 Width of roads
3.1 Landuse incompatibilities
in the plan
3.2 Consistency ofdevelopment with the landuse
plan in reference area
4.1 Hazardous landuse (LPG
and petrol stations, chemicals,
explosives, power plant, etc.)
and landuse within 200m
radius around them
5.1 Geomorphologic condition
5.2 Designated water bodies
and flood flow zones andcorresponding landuse in plan

6. Risks inbuilding stocks

6.1 Vulnerability of building
stocks in reference area

7. Open space
scarcity risk

7.1 Interconnectedness andadequacy of open space (park,
play-fields, green space, carpark, etc.)

8. Risks in criticalfacilities

8.1 Adequacy of criticalfacilities (health, education,community centers, fire service
stations, etc.)

Methodology
Landuse plan of the study area.
Primary road network in the study area for disaster managementwith support from the city center.
Natural resources in the landuse plan of the study area. For
utilization in emergency.
Internal road network and width in the study area for means ofescape and access to rescue and relief after an earthquake.
Allocation of land for different use as well as their comparative
placement in the plan of the study area with respect toclassification of landuse zones as per Table 2. To reduces risk
and ensure effective disaster management.
Assessment of present landuse with respect to plan in thereference area (Second Phase of URMT) based on collectedprimary data. To ensure execution of the plan accordingly.
To avoid secondary hazards (explosion, fire, pollution, etc.),
development within 200-meter radius of the hazardous uses
should be restricted (Argo & Sandstrom, 2014; Brender,
Maantay, & Chakraborty, 2011; MfE New Zealand, 2002).
Analysis was done by creating buffers of 200-meter around thehazardous uses in the landuse plan of the study area using“Create Buffer” tool in ArcGIS 10.3.1.
Soil condition for development in the study area. Soil unsuitablefor development lead to greater damage after an earthquake.
Condition of water bodies and flood flow zones in the landuse
plan of the study area with respect to the proposal in Detailed
Area Plan (DAP) by RAJUK (RAJUK, 2011). If not preserved, it
increases risk for exposure to liquefaction and flood.
Preliminary building vulnerability assessment in the reference
area (Second Phase of URMT) based on collected primary data
(Discussed in the next section).
Open spaces in the landuse plan of the study area with respect toPrivate Housing Project Land Development Rule, 2004 (MoHPW
GoB, 2004), requirement to be used for evacuation space (onesquare meter space per person) (CDMP, 2009a; Xu, Okada, He,& Hatayama, 2006) and interconnectedness among them.
Educational facility, community centers, government offices,
religious facilities, etc. in the landuse plan of the study area withrespect to Private Housing Project Land Development Rule, 2004
(MoHPW GoB, 2004) and requirement to be used for evacuation
space (one square meter space per person) (CDMP, 2009a; Xu etal., 2006).
Health facilities in the landuse plan of the study area with respect
to Private Housing Project Land Development Rule, 2004
(MoHPW GoB, 2004) for emergency response after anearthquake.
Fire service station in the landuse plan of the study area
considering 3-kilometer service area around a station (Tishi,
2015). Analysis was done by creating buffers of 3-kilometeraround the hazardous uses in the landuse plan of the study area
using “Create Buffer” tool in ArcGIS 10.3.1.

124

Table 2: Classification of landuse zones
Zone
Residential

Permitted use
Residence

Commercial

Educational

General merchandise store (e.g.
food market, books and officesupplies, etc.)
Heavy, medium and lightindustries
School; College; University

Agriculture

Agriculture; Forestry; Gardening

Fisheries; Livestock

Open space(recognized formal
areas)
Mixed use

Park; Playground; Lake; Zoo

Temporary structures withlight activities

Industrial

Combination of residential,commercial, offices, etc.
No development
Temporary use, e.g. agriculture,zone (hazard-prone)
gardening, forestry
Urban reserve (riskExisting uses with no further
prone area)
extension
Source: Adapted from UDD and ADPC (2016)

Conditional use
Small business shops (e.g.
confectionary shop, corner
shop)
Light industries withlimited manpower or types

Restricted use
Commercial; Industrial; Anytype of polluting activities

Commercial with limited
categories
Student dormitory

Residence; Educational (e.g.
school, college, etc.)
Commercial; Industrial;
Residential
Commercial; Industrial;
Residential
Commercial; Industrial;
Residential

Residence; Educational (e.g.
school, college, etc.)

Industrial
Temporary structures withlight activities
Improvement of existing
structures

Commercial; Industrial;
Residential
Any type of new construction

2.4 Preliminary building vulnerability assessment in the reference area
After an earthquake, impacts in urban areas are greater due to the collapse of buildings. Vulnerable
buildings increase the risk of damage and loss. Earthquake vulnerability assessment of building
stocks is a very difficult and time-consuming process. Preliminary inspection or simplified
assessment procedures require limited engineering analysis based on information from visualobservations and structural drawings or on-site measurements to get a gross impression about thestructure requiring. Among different methods for preliminary earthquake vulnerability assessment ofbuilding stocks, Federal Emergency Management Agency- Rapid Visual Screening (FEMA-RVS)(Level I) and Turkish Simple Survey Procedure (Level I) are the most commonly used (FEMA,
2015; Sucuoğlu & Yazgan, 2003a, 2003b; Sucuoğlu et al., 2007). Considering the suitability, several
studies have utilized these methods for preliminary earthquake vulnerability assessment of buildings(Kumar et al., , 2017; Srikanth et al., 2010). These have also been utilized in some studies in
Bangladesh (Alam et al., 2008; Jahan, 2010; Rahman et al., 2015; Sadat et al., 2010; Sarraz et al.,2015). Therefore, to get a simplified image of building vulnerability in the reference area,
preliminary inspection has been carried out in this research.
For FEMA-RVS, the data collection form for moderate seismic zone was utilized as Dhaka falls in
the moderate seismic zone (BNBC Draft, 2020). The building attributes observed are: number ofstorey, building type, vertical irregularity and plan irregularity (FEMA, 2015). Vertical irregularity isidentified if one part of the building expands vertically than the other part leading to shift in centroid125

resulting in reduction of its strength to hold the building during an earthquake (FEMA, 2015).
Deviation of building floor plan from a rectangular plan is called plan irregularity, which leads toirregularities in stiffness and strength distributions of buildings increasing the risk of damage duringstrong ground excitations (FEMA, 2015; Jahan, 2010). Based on collected attributes, building
vulnerability scores of sample buildings were calculated through modification of the basic structural
hazard score by adding (or subtracting) score modifiers related to observe performance attributes.
After that, the buildings have been categorized into five grades based on their corresponding RVS
scores reflecting likely seismic damage as per European Macro Seismic Scale (EMS-98) (Table 3).
Table 3: Different damage grades corresponding to RVS scores of buildings
RVS
score (S)
S<0.3

Grade

1
0.3<S<0.7
2
0.7<S<2.0

3

2.0<S<3.0
4

S>3.0

Explanation

Likely damage

Negligible to slightdamage: No structural
damage, slight nonstructural damage
Moderate
damage:Slightstructural
damage,moderate
non-structural damage
Substantial damage:Moderate structural
damage, heavy nonstructural damage

Fine or hairline cracks in plaster over frame members, walls at thebase, or partitions and infills.
Fall of small pieces of plaster or loose stones from upper parts ofbuildings in very few cases.
Cracks in columns and beams of frames, structural walls, orpartition and infill walls.
Fall of brittle cladding and plaster, mortar from the joints of thewall panel.
Cracks in columns and beam-column joints of frames at the baseand at joints of coupled walls.
Large cracks in partition and infill walls.
Failure of individual infill panels or non-structural elements(partitions, gable walls etc.).
Roof tiles detach.
Spalling of concrete cover, buckling of reinforced bars.
Large cracks in structural elements with compression failure ofconcrete and fractures of reinforcement bars.
Bond failure of bars, tilting of columns. The collapse of a few
columns or of a single upper floor.
Serious failure of walls.
Partial structural failure of roofs and walls.
The collapse of ground floor parts (e.g. wings of the building).
Total or near total collapse of a building.

Heavydamage:Moderate structural
damage, heavy nonstructural damage

Destruction:
Very
heavy
structural
damageSource: Adapted from (FEMA, 2015; Jahan, 2010)
5

The building attributes observed for Turkish simple survey are: number of storey, presence of a soft
storey, presence of heavy overhangs, presence of short column, possible pounding between adjacentbuildings, and apparent building quality (Sucuoğlu & Yazgan, 2003a, 2003b; Sucuoğlu et al., 2007).
If a building has a floor which is open in character (for parking garages, retail space or simply a floor
with a lot of windows) making it 70% less stiff than the floors above it, then it is considered as a soft
storey building. Soft storey lead to lesser opportunities to install shear walls to distribute lateralforces resulting in reduction of capacity to cope with the lateral forces caused by the swaying of thebuilding during a quake (Sucuoğlu & Yazgan, 2003a, 2003b; Sucuoğlu et al., 2007). Heavy overhang
is observed in the form of heavy balconies or over hangings floors in buildings shifting the mass126

center upwards and giving rise to increased seismic lateral forces and overturning moments during
earthquakes (Ozcebe et al., . Apparent building quality can be judged by observing the material andworkmanship quality and the care given to its maintenance, where it is assumed that buildings withpoor apparent building quality possess weak material strengths and inadequate detailing (Sucuoğlu &Yazgan, 2003a, 2003b; Sucuoğlu et al., 2007). Structural columns which are short in length relative
to other columns on a particular storey of buildings are considered short columns, which may failduring an earthquake either by being crushed due to compression or by bending due to their greater
stiffness (Sucuoğlu & Yazgan, 2003a, 2003b; Sucuoğlu et al., 2007). Pounding between two adjacentbuildings is one of the main causes of building damages in an earthquake, which occurs when theseparation distance between adjacent buildings is not large enough to accommodate the relative
motion during earthquake events (WBI, 2006f).
In Turkish simple survey method, three types of building vulnerability assessment scores have beendeveloped for three seismic intensity zones in terms of probable Peak Ground Velocity (PGV), i.e.
Zone I (60<PGV<80), Zone II (40<PGV<60), and Zone III (20<PGV<40) (Sucuoğlu & Yazgan,
2003a, 2003b; Sucuoğlu et al., 2007). Dhaka falls in Zone II (BNBC, 1993), which has been
followed in this research. Based on the vulnerability parameters of sample buildings in the reference
area obtained from survey, the vulnerability scores have been calculated by adding (or subtracting)them accordingly (Sucuoğlu & Yazgan, 2003a, 2003b; Sucuoğlu et al., 2007). After that, thebuildings have been categorized into four damage states based on their corresponding Turkish simple
survey scores reflecting likely seismic damage (Table 4).
Table 4: Possible damage states corresponding to Turkish simple survey scores
Turkish simple
survey (Level 1)
score
>=100
80-10050-80

DamageState

Likely damageNone
No visual sign of damage
Light
Hairline incline or flexural cracks in the wall. Flaking of plaster.
Moderate
Concrete sealing. Cracking in walls and joints between panels.
Severeor Wide and through cracks in the wall. Crushing of walls or out of plane toppling
0-50
Collapseof walls. Local structural failure and local or total collapse.
Source: (Sucuoğlu & Yazgan, 2003a, 2003b; Sucuoğlu et al., 2007)

After score calculation, building vulnerability components and vulnerability have been analyzed
through frequency distribution and correlation testing in SPSS 17 and Microsoft Excel 2013.
Correlation among the vulnerability components and vulnerability have been carried out using nonparametric Spearman's rho technique because they are discrete ordered data.

1272.5 Formulation of earthquake RSLUP strategies
After assessment of risk sensitivity of the landuse plan of the study area, the earthquake RSLUP
strategies to be incorporated in the landuse plan have been formulated. For this purpose, firstly,
review of the strategies have been done from different literature. After that key informant interviews
have been carried out to understand their expert opinion in this regard. While interviewing theexperts, findings from earthquake risk sensitivity of the landuse plan of the study area have been
explained to give them a brief understanding about the purpose of the interview. Based on literature
review and findings from the key informant interviews, earthquake RSLUP strategies to beincorporated in the landuse plan have been formulated addressing the risk sensitivity accordingly.
3. Earthquake risk sensitivity of the landuse plan of the study area
The earthquake risk sensitivity of the proposed landuse plan of the study area is described as per therisk themes in the following sections.
3.1 Macro-form risks
Figure 3 shows the landuse plan of the study area proposed by RAJUK. The landuse in the study area
has been planned considering grid pattern. Here different uses have been allocated by RAJUK on thebasis of ‘Private Housing Land Development Rule, 2004’ (MoHPW GoB, 2004) to ensure minimum
land for urban community facilities in the area which will improve the living quality(Shamsuzzaman, 2014). The proposed density of the study area is 300 persons per acre and projected
total population to be accommodated within its area is around 0.65 million people (Shamsuzzaman,2014).
Among different uses shown in Figure 3, most significant uses of total area are road network(33.73%), residential (33.39%) and commercial (11.81%). Mainly five categories of residential
landuse have been proposed including residential plots, public residential high-rise apartment,
private residential housing apartment, government staff housing, and accommodation for urban poor.
The commercial landuse in the area includes central business district (CBD), plaza, warehouse andkitchen market for the study area as a whole, commercial plots (along the primary roads surrounding
the neighborhoods), and neighborhood shops, centers and plaza at neighborhood level to maintainhierarchy of commercial use. Rest of the uses include communication (road network), hazardous
uses, critical facilities (educational, health and public service), utility service, and water body andopen space. From the overall landuse allocation in the plan of the study area, it can be said that theallocated uses are suitable for earthquake risk management but these uses should be resilient toearthquake

128

Source: Adapted from RAJUK (2010)

Figure 3: Landuse plan of the study area
.From the analysis of road network, it could be found that, there are mainly twelve primary roads inthe study area connecting it with the city. In addition to the primary roads, there is the western Dhaka
flood protection embankment road constructed on the east bank of the Turag river, which is locatedat the west side of the study area. Additionally, the proposed route of Metro Rail Transit (MRT) line
six is going through the study area. Table 5 shows the primary road segments connecting the study
area with the rest of the city along with their width, connectedness, and direction.
Table 5: Primary road segments connecting the study area with the city
Primary roadsegment name
Sonargaon Janapath
Avenue 1
Avenue 2
Avenue 3
Avenue 4
Avenue 5

Road width

Connectedness

Direction

210 ft
110 ft to 210 ft
100 ft
100 ft to 210 ft
210 ft
100 ftFlood protection embankment – URMT 2nd phase
Avenue 4 - URMT 2nd phase
Avenue 3 -Ashulia and Tongi
Flood protection embankment - Avenue 2 - Mirpur
Avenue 3 -Ashulia and Tongi
Avenue 3 - Cantonment

East - West
East - West
North-South
East - West
North-South
East - West129

Avenue 6
Avenue 7
Avenue 8 and
Avenue 9100 ft
100 ftNorth-South
East-West

100 ftAvenue 7 - Avenue 5 - South Avenue
Avenue 3 - Cantonment
West Avenue - Avenue 4 - Avenue 2 - URMT 2nd
phase
Avenue 3 -Ashulia and Tongi (Parallel to Floodprotection embankment road)
Avenue 3 - Avenue 6 - Cantonment

West Avenue

110 ftSouth Avenue
Flood protection
embankment road

200 ftAshulia - Mirpur

North-SouthMRT line 6

Metro rail

Uttara - Mirpur - Farmgate - Karwan Bazar Shahbag–MotijheelNorth-South110 ftEast-West
North-South
East-West

From Figure 3 and Table 4, it can be observed that in the east-west direction the primary road
network connects the study area with URMT Second Phase and channel the traffic towards flood
protection embankment road. Again in the north-south direction, there is only two continuous
primary road network including flood protection embankment road and MRT line six. Among them,
MRT line six has limited points of access with fixed stations. Avenue 4 connects the area with
Ashulia in the north direction, but it is not continuous in south direction. Thus, at the time of
emergency situation, there will be excessive pressure on flood protection embankment road.
Furthermore, there are some points of intersection where bottlenecks are created due to change inroad width. All these factors will hamper the response, rescue, and relief after an earthquake withsupport, control and guidance from the city center.
3.2 Risks in urban texture
The roads in the landuse plan of the study area are arranged in a grid pattern, where road width varies
between 20 ft. to 210 ft. (Figure 3). There are three hierarchies of roads including access road(connecting the plots and different landuse: width varying from 20 ft. to 50 ft.), secondary road(connecting different neighborhoods: width varying from 60 ft. to 70 ft.) and primary road(connecting secondary roads and city center: width varying from 100 ft. to 210 ft.). Along with roads
for vehicular movement, there are provisions for walkways beside the lakes which also act as abuffer between the lakes and the residential areas. Moreover, there are twelve bridges over the lakes
proposed in the plan to connect the roads over water bodies without interrupting them.
From the analysis of the internal road network and their width, it can be concluded that the proposed
grid patterned road network ensures interconnectedness within the study area (Dill, 2004).
Additionally, the widths of the proposed roads are sufficient for emergency movement within thearea. This will further confirm means of escape and access to rescue and relief after an earthquake.
Though the internal road network is well connected with sufficient road width, some roads areplanned over lakes connected by bridges and culverts. If any of them are damaged, then it will create
interruption of vehicle movement and thereby cause more congestion. The interconnected lakes and130walkways along them can also be utilized as an alternative to road transport. It will reduce somepressure on road network at the time of emergency. These waterbodies can be utilized as a source ofwater at the time of emergency for different purposes.
3.3 Risks in landuse incompatibilities
The study area has been divided into four sectors which have been further arranged into 40
neighborhoods. The neighborhoods have been planned consisting of residential plots, commercial
plots, educational and recreation facilitates. For major commercial uses, a central plaza has been
proposed and the plots along the primary roads have been designated for commercial purposes.
Additionally, other uses (e.g. bus depots, parking facility, waste management facility, power grid,etc.) have been kept outside the neighborhoods. Thus, the landuse in the study area has been planned
considering compatibility among different uses.
Consistency of the development with proposed plan has been analyzed in the reference area. From
the analysis it could be observed that landuse of about 7.73% plots have been changed by plotowners. Most significant change is conversion of residential plots into commercial use, that ofresidential and commercial to educational. Figure 4 shows consistency of the development withproposed plan in the reference area showing proposed landuse and existing landuse. From thisanalysis it can be said that the landuse is changing violating the proposed one. If similar trend
continues in the study area, then it will increase risk of the area to earthquake.
3.4 Risks in hazardous uses
In the study area, hazardous landuse comprise of 2.25% of total area. There are together nine
proposed gas stations and petrol pumps, collectively six power and substations proposed, MRT
depot, provision for waste management and recycle area in 0.13 square kilometer land, and a water
treatment plant with an area of 0.03 square kilometer. Figure 5 shows location of these hazardous
uses and landuse within 200-meter buffer area surrounding them in the study area. From the map, it
can be observed that there are residential, commercial, educational and public services within thebuffer area which represents a higher risk of these areas to the earthquake. Additionally, a portion of
the MRT line six lies within the buffer area which represents greater risk.

131Source: RAJUK (2016)

Figure 4a: Proposed landuse

Source: Field Survey (2016)

Figure 4b: Existing landuse

Figure 4: Consistency of the development with proposed plan in the reference area
3.5 Special risk areas
Figure 6 shows the geomorphology of the study area including geomorphologic composition and thesuitability for construction on the analyzed on the basis of geomorphologic composition by GSB.
From the figure, it is visible that major portion of the study area (about 67.1%) consist of floodplain,
marshy land and depression, and Madhupur terrace. Therefore, about 71.21% of the study area
comprise of very weak foundation condition for which specialized foundation design is required,17.58% is in weak foundation condition for which pile foundation is required, and 11.21% is suitable
for all kind of infrastructure.
In consistent with this analysis, the soil test in MRT depot located in the study area by Dhaka Mass
Transit Company Ltd. (DMTCL) found that the soil condition is unfit for erecting necessary
infrastructures including MRT workshops, administrative building, washing plant and rolling stocks.
Thus, a project for land development of MRT depot located in the study area has already been
implemented by Dhaka Mass Transit Company Ltd. (DMTCL) costing around 560 million BDT to
make around 50 acres of land fit for taking load of infrastructures for 100 years (from 2016 to 2018).
According to DMTCL, dynamic compaction and sand piling compaction have been done through132

compaction of land with sand and soil layer by layer (DMTCL, 2018). Thus, the geomorphic
condition increases risk of landuse in the study area.

Figure 5: Location of hazardous uses and surrounding landuse in the study area
From the analysis of waterbodies in the landuse plan of the study area, it can be observed that there
are interconnected lakes proposed to be preserved by RAJUK comprising of about 7.67% of total
study area. Though the amount of water bodies proposed to be preserved seems to be suitable toaddress the risk, while comparing with the proposal of DAP it has been found that some waterbodies
proposed in DAP to be preserved have not been preserved. Figure 7 shows the water bodies in the
study area designated in DAP but not addressed in the proposed landuse plan. Thus, all thedesignated water bodies have not been addressed in the proposed plan which may have entailed
increased vulnerability to earthquake hazard in those areas. Thus it may increase risk of landuse toearthquake.
133Source: GSB (2016)

Source: GSB (2016)

Figure 6a: Geomorphologic

Figure 6b: Suitability for construction

composition

considering geomorphic composition

3.6 Risks in building stocks
Earthquake risk in building stocks of the reference area (Second Phase of URMT) was assessedassuming that similar situation would appear in the study area if development trend remains same.
Majority of the buildings in the reference area are RCC with very few masonry buildings. Height ofthe buildings range from one storied to 11 storied. About 70% of them are 6-storied. The reason isthat, till 2006 the building height was restricted up to six storey by the government due to its close
proximity from the airport. After 2006 the restriction was withdrawn. In 2008 the “Dhaka Mahanagar
Building (Construction, Development, Protection and Removal) Rule 2008” was amended which
regulated building construction by Floor Area Ratio (FAR) (MoHPW GoB, 2008). This enabledconstruction of high rise buildings maintaining FAR ratio. Thus, after 2008 rate of construction ofhigh rise buildings in the area increased. Figure 8 shows the distribution of buildings with respect tonumber of storey in the reference area.

134

Figure 7: DAP designated water bodies not addressed in the proposed plan of the study area

All of the plots in the reference area are regular shaped (rectangular). But most of the buildings haveextended portions for veranda as well as irregular shape or extended portions to enhance architectural
beauty. These practices have led to the problem of vertical irregularity, plan irregularity as well as
heavy overhang. Thus, among the sample buildings in the reference area, 85% buildings haveproblem of severe vertical irregularity, 76.1% of the buildings have heavy overhang problem andabout 64.4% of the buildings have problem of plan irregularity. These problems also increase massof buildings leading to vulnerability. Around 84.7% have the problem of soft storey as the groundfloors of these buildings are used for car parking. Very few buildings in the reference area haveproblem of short column (16.4%) and none of the buildings have pounding effect. The reason behindsuch finding is that all of the buildings in the reference area have minimum gap with othersurrounding buildings as per requirement (MoHPW GoB, 2008). The buildings in this area havemostly been built after the year of 2000. So apparent quality of most of the buildings are good
(24.2%) or moderate (57.2%). In most cases existing earthquake-resistant building codes were not135applied. Figure 9 shows distribution of buildings in the reference area with respect to building
vulnerability through RVS method and Turkish simple survey method respectively. From theanalysis it can be observed that major portion of the sample buildings is highly vulnerable toearthquake. Thus the overall scenario of building stocks in the reference area is not in a goodcondition from the earthquake vulnerability perspective, where the most important vulnerability

Percentage of buildings

factors are vertical irregularity, soft storey, heavy overhang and plan irregularity.
80
60
40
20
0
1

2

3

4

5
6
7
Number of storey

8

9

10

11

Percentage of buildings

Figure 8: Buildings with respect to number of storey in the reference area
90
80
70
60
50
40
30
20
10
0

81.1

13.6
0

5.3

0

Grade 1
(Negligible
damage)Grade 2
Grade 3
Grade 4 (Heavy(Moderate(Substantial
damage)damage)damage)Building vulnerability (RVS)

Grade 5
(Destruction)

Percentage of buildings

Figure 9a: RVS method
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
.0

67.5

16.4

8.3

7.8
None

Light
Moderate
Building vulnerability (Turkish simple survey)

Figure 9b: Turkish simple survey method
136

SevereFigure 9: Distribution of buildings with respect to building vulnerability in the reference area
Table 6 shows the correlation between the building vulnerability and their vulnerability components
in the reference area. Short column and pounding effect do not have significant effect on building
vulnerability or other components. So these two factors have been removed from correlation
analysis. From the table, it can be observed that there is a very strong-significant relation between
buildings’ soft storey and vertical irregularity reflecting that buildings with soft storey also havevertical irregularity. Again, buildings’ vulnerability according to RVS method is very strongsignificantly related to vertical irregularity and soft storey, and according to Turkish simple survey
method is strong-significantly related with number of stories, vertical irregularity, plan irregularity
and soft storey. Thus the most significant factors influencing building vulnerability are verticalirregularity and soft storey. Additionally, buildings’ vulnerability according to RVS method andTurkish simple survey method is strong-significantly related to each other, meaning that the findings
from assessment through two methods are consistent.
Thus, the analysis of building vulnerability to the earthquake in the reference area represent that,most of the buildings are highly vulnerable to earthquake. The most important building vulnerability
factors are vertical irregularity, soft storey, heavy overhang and plan irregularity. Among theminfluence of vertical irregularity and soft storey on vulnerability are most significant. Such condition
has been arisen due to building construction violating building codes and regulations mostly focusing
on beautification. If the similar trend continues in the study area, then similar vulnerability ofbuildings to the earthquake will result in there.
3.7 Open space scarcity risk
In the study area, about 7.69% area (0.7 square km) is designated as open spaces in the proposed
plan, including 12 parks, 36 playfields, two graveyards, 0.05square km green belt, and 0.26 square
km open space. The playfields are evenly distributed in all of the neighborhoods adjacent to theeducational facilities. The parks are mostly located along the lakes. The green belt is located along
south-eastern sides of Sector 18 to separate the waste management plant, water treatment plant andthe neighborhoods from the water retention area located at the southern portion of the area.
Considering the projected total population for the study area (about 0.65 million), 0.53 square km
land is required for playgrounds and parks, whereas the available space is 0.39 square km area as per
Private Housing Project Land Development Rule, 2004 (MoHPW GoB, 2004). According to Xu etal. (2006)and CDMP (2009a), the evacuation spaces do not provide long time shelter and therefore aminimum per capita space (one square meter space per person) is required for the victims of an
137

earthquake. Thus, the open spaces in the study area altogether have the capacity to accommodate
about 0.7 million people for evacuation at the response phase of an earthquake, whereas the
projected population for the study area is around 0.65 million. So it can be said that the parks andplayfields are not sufficient as per requirement of the standard, but altogether they are sufficient toserve the area at the time of emergency. The open spaces are evenly located and well connected byroad networks, and the parks along the lake are connected by walkways, which is suitable for
emergency response after an earthquake.
3.8 Risks in critical facilities
In the study area, the critical facility comprises of 3.39% of total area, which includes educational,
health and public service facility. Considering the projected total population for the study area (0.65
million), required space for educational facilities is 0.44 square km area whereas the available spaceas per landuse plan is 0.22 square km area (including schools, educational institutions, and special
institutions). Moreover, there are three religious facilities comprising 0.03 square km area whereas
the requirement as per standard is 0.1 square km. Therefore, provided educational and religious
facilities are not sufficient to serve the area. From evacuation perspective though open spaces in the
study area are sufficient, the additional evacuation space may be required to combat the situation
supporting surrounding areas where educational and religious facilities in the area may serve thepurpose.
The health facilities proposed in the study area includes one hospital block and a club with 0.03
square km area which is sufficient as per the standard. But the facility is located at the north-western
corner of the study area which may not be accessible by all the residents of the study area after anearthquake. There are two fire service stations in the landuse plan of study area. Considering 3 km
service area, it can be observed that two fire stations are sufficient to serve the area at the event ofemergency response after an earthquake. Additionally, there are two police stations and one RAJUK
site office in the landuse plan of study area. These are approximately centrally located, which can beutilized to coordinate earthquake management activities in the study area.

4. RSLUP strategies to be incorporated in the proposed plan of the study area
From the analysis it can be said that, though there are positive aspects of the landuse plan of thestudy area, the risk issues affect the earthquake risk sensitivity of the plan adversely. Therefore, theseissues are necessary to be addressed accordingly. The most fundamental approach in RSLUP is tosimply avoid proposing a development on lands that have seismic hazard and subsurface structures,
which is executable at planning phase and for areas land values are comparatively low (Fat-Helbary138

et al., 2004; UDD & ADPC, 2016). But for the areas with high man to land ratio and high land
values, such approach may prove to be unsustainable. Burby et al. (2000) explained the tradeoffs
while prioritizing the RSLUP strategies for adoption as:“…Some emphasize long-range strategies, while others react to current development proposals.
Some try to reduce development in hazardous areas, while others accept such development but focus
on site and building design to reduce vulnerability. Some redirect public investment, but most seek toregulate or influence private development. Some are regulatory, and others are voluntary…”
Hence, formulation of RSLUP strategies depends on risk sensitivity of landuse plan as well as the
local context of the area to be considered. Therefore, based on the analyzed risk sensitivity, RSLUP
strategies to be incorporated in the landuse plan of the study area reflecting the risk sensitivity were
formulated based on literature review and key informant interview. The formulated strategies are
discussed in the following sections.
4.1 Spatial strategies to revise the landuse plan
Based on the findings from analysis, some unavoidable revision of the landuse plan of the study area
are necessary to reduce the earthquake risk.
4.1.1 Promote mixed-use development
From the analysis of the risks in landuse incompatibilities it has been found that the landuse
proposed in the plan of the study area is compatible. But at present, mixed-use development isincreasing day by day whether planned or not. Moreover, from disaster management perspective,
mixed-use development is encouraged to increase self-sufficiency of a local entity, reduce
dependency on the city and thereby reduce dependency on connectivity with rest of the city (UDD,2013). Thus to avoid spontaneous development and guide the trend, mixed-use development should
be promoted in the study area. In such development, corresponding building construction rules andregulations should be followed accordingly (WBI, 2006b, 2006e).
4.1.2 Road widening and extension
From the analysis of the macro-form risks it has been found that connectedness of the primary roadsin the study area are insufficient in the north-south direction and there are bottle-necks at someintersections due to inconsistent road width, which may hamper the connection of the area with thecity. To address these issues, some of the primary roads should be extended at the southern portionof the study area and some road should be widened. Figure 10 shows the proposed locations whereroad widening initiative should be implemented and arrow sign represents the road segments which139should be extended in the study area. To implement these proposals, RAJUK can make necessary
corrections in the landuse plan. If the adjacent plots have already been allocated, then initiative
should be taken to acquire the required land (Burby et al., 2000; EMI, 2010; Reyes, 2004; UDD,
2013). Tax incentive can be provided for owners leaving space for this purpose (EMI, 2010; Mitchell& Myers, 2013).

Figure 10: Proposed locations for road widening and extension in the study area
4.1.3 Preservation of the waterbody proposed in DAP
From risk in special areas in the landuse plan of the study area it can be observed that some of the
DAP designated water bodies have not been addressed in the proposed plan. The DAP (1995-2015)
should be strictly followed. Even if the smaller portions cannot be revived, the greater waterbody
designated in the DAP should be preserved accordingly. For this purpose, necessary measures should
be taken to make to make change in the landuse plan of the study area designating the waterbody.
4.1.4 Community level parking facilities to discourage soft-storey
Risks in building stocks reflect that one of the possible major causes for building vulnerability which
may arise in the study area is soft storey due to open space at ground floor for parking purpose. To
140

discourage soft-storey in buildings due to building level parking, alternative community levelparking facilities should be provided, e.g. common parking space at neighborhood, multistory
parking facility, etc. For this purpose, the neighborhood level commercial spaces can be utilized.
4.1.5 Relocation of petrol pump
Analysis of risks in hazardous uses reflects that a portion of the MRT line six lies within 200 m
buffer area of hazardous uses. This petrol pump should be relocated to a safer location. Moreover
location of petrol pump filling station should be decided based on the guidelines (Biswas & Hossain,
2016; BPC, 2014).
4.1.6 Additional provision for health facility
Risks in critical facilities revealed that there is only one large health facility which is not sufficientfor emergency situation. Moreover, it is located at the north-eastern corner of the study area. So
additional health facilities should be provided at different locations of the study area. Considering theexisting landuse, health facilities can be provided in the central plaza, CBD as well as at some of thecentral commercial spaces of the neighborhoods. This will ensure sufficient facility as well as proper
distribution of health facilities in the study area.
4.2 Earthquake resilient planning of hazardous uses
From the analysis of risks in hazardous uses it has been found that there are several hazardous uses inlanduse plan of the study area. Moreover, there are residential, commercial, educational and public
services within 200 m buffer area of these hazardous uses. As the area is at development phase where
many of the plots have been sold, so it is difficult to relocate the hazardous uses in the area. Again,from macro-form risks perspective, MRT line going through the study area along with a MRT depot
is positively influencing its connectivity with the city which is also hazardous. Despite having
positive impact, the presence of the MRT line and the depot poses threat for secondary hazard in the
study area after an earthquake.
Considering all these facts, earthquake resilient planning and design of these hazardous landuse
should be done as per international standards (MfE New Zealand, 2002; MIACC, 1995; UNECE,
2017). A buffer area or green belt should be provided around these uses to protect the surroundingarea from its effect. A proper management plan should be developed to ensure safe handling andmanagement of hazardous materials (biological, solid, chemical, etc.) especially at the MRT depot,
waste management area and water treatment plant in the study area with specialized and modern
equipment and techniques considering disaster resilience. Environmental pollution should be141

regulated for these hazardous uses by environmental protection legislations in Bangladesh (MoEF
GoB, 2002a, 2002b).
4.3 Building height restriction to control density and prevent resonance during earthquake
After making necessary corrections in the landuse plan of the study area integrating earthquake risk
sensitivity in it, some strategies will have to be taken to manage the development accordingly
considering risk sensitivity. As the area is at risk for potential earthquake, the development in thearea will have to be managed to control density and thereby reduce risk of potential damage. The
projected population density for the study area is 300 persons per acre which was decided on thebasis of standard. From the analysis of risks in building stocks it could be observed that majority ofbuildings in the reference area are 6-storied. But at present the trend for construction of high risebuilding is increasing. If this trend continues in the study area, then density may cross the estimateddensity limit. Therefore, the estimated density should be maintained through height restriction
considering soil condition (Bendimerad & von Einsiedel, 2010; EMI, 2010; Reyes, 2004; UDD,
2013; WBI, 2006b, 2006e; World Bank & EMI, 2014).
Buildings and earth, all have natural periods, where hard bedrock has higher frequencies than softer
sediments. If the period of ground motion matches the natural period of a building, it will undergo
resonance with the largest oscillations possible (IRIS, 2018). It multiplies the frequency during anearthquake causing greatest damage. Analyzing the destruction caused by the earthquake of 19
September 1985 in Mexico City, Flores, Novaro, and Seligman, (1987) concluded that based on soil
condition, severe damage of buildings were occurred due to soil-building resonance effect for
specific building height. Similar results have been explored from the experience of seismic sequencein Molise (Italy) in 2002, by Mucciarelli et al. (2004). Thus, certain building height for certain soil
condition causes resonance during an earthquake increasing building damage. This height can becalculated using following equations as per BNBC Draft (2020) considering that at resonance periodof building and soil becomes equal.
Period of building, TB = N/10 second; [Here, N= No. of storey]
Period of soil, TSoil = 4H/Vs30 seconds; [Here, H = Thickness of soil layer andVs30 = Average shear wave velocity for top 30m of soil]
Building height above or below this specified height is considered safe. Therefore, to prohibit
resonance of buildings in the study area during an earthquake and thereby reducing risk of building
damage, building height with possibility of resonance should be determined for each plot throughsoil test. After that allowable height range should be considered plot wise. This determined building142height should be utilized to restrict building height for controlling density and preventing resonanceduring earthquake.
4.4 Earthquake resilient design and construction
Earthquake resilient landuse plan is not enough to address the risk alone, if the buildings are notconstructed following earthquake resiliency then the casualty, damage and loss would increase after
an earthquake. Especially damage or collapse of critical facility buildings would result in failure indisaster response and management after an earthquake. Analysis of risks in building stocks reveals
that most of the buildings in the reference area are vulnerable to earthquake, where the major causes
are vertical and plan irregularity, soft storey, and heavy overhang. Such condition has been arisen
due to building construction violating building codes and regulations mostly focusing on
beautification. If the similar trend continues in the study area, then similar vulnerability of buildings
to the earthquake will result in the study area. Again, from the analysis of risks in urban textures it
has been found that internal road network in the study area is well connected with sufficient roadwidth, but there are several bridges connecting the roads over lakes. If any of them are damaged,
then it will create interruption of vehicle movement and thereby cause more congestion. Failure in
MRT line would not only interrupt disaster management but also pose great risk of secondary hazard.
Similarly, failure in hazardous uses would lead to secondary hazards. Additionally, special risk areas
analysis shows that the geomorphic condition increases risk of landuse in the study area.
Therefore, earthquake resilient construction methods will have to be followed for designing andexecuting all sorts of construction as per BNBC as well as Building Construction Regulation(BNBC, 1993; BNBC Draft, 2020; MoHPW GoB, 2008). Prior to designing and construction, soil
test should be done. Based on soil analysis, height restriction for buildings should be determinedthrough soil analysis. Required designs should be done to ensure soil compactness accordingly.
Where necessary, compactness of soil should be ensured through foundation, soil treatment, pilingand other suitable approaches. Additionally, according to Newton’s law of force, force of earthquake
on a building, F=ma; [Here, m = building mass, a = acceleration of earthquake]. Thus force ofearthquake increases due to increased mass of buildings. Hence, ornamental extensions of buildings
for beautification leading to plan irregularity, vertical irregularity and heavy overhang should beprohibited. Additionally, buildings should be constructed using materials reducing building mass andthereby reduce impact of earthquake. For this purpose, researches should be conducted to invent low
mass durable building material. Consideration of all these factors while designing and executing
construction would lead to earthquake resiliency.

1434.5 Policy initiatives to execute earthquake resilient landuse plan and construction
Earthquake resilient landuse plan would fail to achieve the goal if they are not executed accordingly.
Risks in land-use incompatibilities discloses that the landuse is changing violating the proposed planin the reference area. Again, analysis of risks in building stocks reveals that most of the buildings in
the reference area are vulnerable to earthquake, which has been arisen due to building construction
violating building codes and regulations mostly focusing on beautification. If the similar trend
continues in the study area, then it will result in increased earthquake risk. Therefore, policy
initiatives will have to be taken to execute earthquake resilient landuse plan and construction
accordingly.
An effective approval system for landuse and building design approval should be developed, where
landuse and building design approval certificates should be issued by respective authorities
endorsing compliance with landuse plan and BNBC ensuring earthquake resiliency (BNBC, 1993;
BNBC Draft, 2020). Without having such certificates, one should not be able to proceed forconstruction. If any of the owners require financial assistance for earthquake resilient building
construction, risk financing should be introduced (Baur & Parker, 2015; OECD, 2018; RMS, 2018;
World Bank Group & GFDRR, 2015).
Additionally proper monitoring system should be developed (Bendimerad & von Einsiedel, 2010;
EMI, 2010; Reyes, 2004; WBI, 2006c, 2006f; World Bank & EMI, 2014). It should be performed
continuously at regular interval for confirming efficiency and continuity, which can be done at threephases of construction, e.g. pre-construction phase (to ensure certification of approval for consistentlanduse and resilient building design by endorsed design and construction farms or technicalpersons); during-construction phase (landuse and building construction by endorsed workers andmonitoring as per approval); and post-construction phase (yearly, half-yearly, or semi-yearlymonitoring landuse and building as per approval at regular interval and prohibit any unauthorizedalteration). Moreover, monitoring should ensure preservation of other landuse in the area accordingly
preventing any sort of encroachment and alteration. Here special attention should be given on
hazardous uses, open spaces, waterbodies, roads and pedestrian walkways.
To reduce pressure on the central system as well as strengthen and increase efficiency, themonitoring system should be decentralized at community level involving local co-operatives andvolunteers, with support from technical institutions (e.g. engineering universities, diploma
institutions, etc.). Online record keeping and monitoring system should be developed for easy
monitoring of unauthorized building design change as well as to ensure transparency andaccountability (WBI, 2006c; World Bank & EMI, 2014). Capacity of the related persons for approvaland monitoring should be developed through proper training. Moreover, proper training and144

endorsement system should be promoted for the design and construction farms, technical persons andworkers (mason, bar binder, etc.) for earthquake resilient building construction.
Landuse change without approval, and building construction without design approval or violationfrom approved design should be considered illegal. In such case, there should be the provision ofpenalty for violation (Bendimerad & von Einsiedel, 2010; WBI, 2006c; World Bank & EMI, 2014).
Liability of design and construction farms or technical persons for their design as well as workers forconstruction should be increased. In case of violation, provision for the reduction of rating or
suspension of license should be developed. Tax incentive can be provided for compliance withproposed landuse and approved design. In contrary heavy tax penalty (e.g. double tax) can be
imposed for non-compliance (EMI, 2010; Mitchell & Myers, 2013).
4.6 Planning for disaster management
In addition to resilient landuse planning and its appropriate implementation strategies, planning fordisaster management should be done utilizing the local resources accordingly and appropriately.
Where necessary, additional supports should be provided.
In the landuse plan of the study area there are several hazardous landuse, which can be witnessedfrom risks in hazardous uses. Therefore, appropriate planning should be done for management of
secondary hazards. Central control panel at one point or some secondary control panels at several
points of the study area should be planned and established for electricity and gas supply. This will
enable to immediately switch off the supply in the area during an earthquake and thus avoid
secondary hazards in the study area from explosion. Firefighting facilities should be preserved in thebuildings, e.g. hose pipe, fire extinguisher, sprinkler, etc. to immediately combat fire and reduce
dependency on Fire Service and civil Defense (FSCD). Risks in urban texture brings out that there
are interconnected lakes in the landuse plan of the study area which can be utilized for different
purposes of disaster management. The usability of water bodies as source of water for firefighting
and other purposed should be ensured through installation of pumps at different points. Additionally,
the lakes should be cleaned at regular interval.
The interconnected lakes can also be used for waterway as an alternate to roadways. If promoted
appropriately, the lakes can connect the study area with northern portion of the city, and URTM 2ndphase and Cantonment at eastern direction. Thus, the waterway will be able to ensure internal
connectedness in the study area as well as connection of the study area with other parts of the city.
Thereby, the macro-form risk of the area that is insufficient connectedness in the north-south
direction can be overcome to some extent. To support waterways, parking facilities should be
provided at different points beside waterbodies to park the car and use waterways and thereby
145establishing a link between road and water transport network. Helicopter support and arrangement of
helipad should be provided at open spaces as an alternative way for rescue and relief operations inthe study area with outside support (Bozorgi-Amiri et al., 2017; Caprì et al., 2009; Ozdamar, 2011).
Again, risks in urban texture shows that the internal road network is well connected with sufficientroad width. But to ensure internal connectivity of the study area for internal response, rescue andrelief activities, uninterrupted movement of emergency vehicles after an earthquake should besafeguarded. In this regard a traffic management plan should be prepared regulating direction of
traffic movement in the access roads in the study area, e.g. one-way or two-way traffic flow (Barrett
et al.,2000; Liu et al., 2007; Madireddy et al., 2011). Additionally, to discourage people from using
vehicle for evacuation after an earthquake, it is necessary to provide proper and uninterrupted
pedestrian movement facility in the study area (Reyes, 2004; WBI, 2006e). An evacuation planshould be prepared for the study area to ensure proper response in the study area after an earthquake(Argyroudis, Pitilakis, & Anastasiadis, 2005; Bendimerad & von Einsiedel, 2010; Reyes, 2004; Xu,
Okada, et al., 2008; Xu et al., 2006; Xu, Okada, Takeuchi, & Kajitani, 2007). A dumping ground fordebris accumulation should be planned so that even if some portion of the roads get blocked bydebris, they can be removed immediately to ensure continuity of the internal roads, especiallyevacuation route.
Open space scarcity risk reveals that the open spaces in the study area are sufficient, evenly
distributed and well connected. These open spaces should be maintained at regular interval to ensure
their cleanliness, environment and usability as evacuation space. For this purpose, proper and regularmonitoring, inspection and management plan should be prepared. Public toilet should be provided in
the open spaces, which can also be used at the event of emergency. According to analysis of risks incritical facilities, there are several centrally located government facilities in the landuse plan of thestudy area which can be utilized for coordination of earthquake management. The coordination
center should be equipped with necessary facilities and equipment to be used for a response to an
earthquake, e.g. search and rescue equipment, dry foods, and water, tents, medicines, etc.
Management capacity in the open spaces, disaster shelters and critical facilities should be developed
to shift rapidly to emergency performance. Disaster management related legislations should be
followed properly to ensure proper earthquake management in the study area (MoDMR GoB, 2012,2015, 2017, 2019). Communication among different stakeholders in the study area should beestablished for proper co-ordination after an earthquake (MoDMR GoB, 2019).

146

4.7 Preparedness of local people
Appropriate execution of the earthquake risk sensitive landuse plan in the area would mostly dependon acceptability by the people living in the area. If they can perceive the importance, thenacceptability would increase. Again such perception can be improved through proper awareness.
Therefore, the earthquake risk-sensitive landuse plan of the study area should be disseminated among
the plot owners of the study area to make them understand about the risk (WBI, 2006f; World Bank
& EMI, 2014). Different awareness raising and training initiatives should be taken to improveunderstanding of the local people about earthquake preparedness, importance of landuse complianceand importance of constructing earthquake resilient buildings (Saito, 2007; WBI, 2006f). This would
increase household level preparedness of the community for earthquake, and also encourage them toconstruct their building ensuring compliance with proposed landuse and earthquake resilience.
Based on the above discussion, Table 7 summarizes the RSLUP strategies corresponding to the risk
themes and risk factors as well as the positive and negative aspects found from the risk sensitivity
analysis of landuse plan of the study area.

5. Conclusion
This research proposes a methodology to develop RSLUP strategies for earthquake to be integrated
in the landuse plan at local level for an area under development through analysis of risk sensitivity ofthe plan and addressing them accordingly. From the analysis it can be said that earthquake risk
sensitivity was not considered or integrated in the proposed landuse plan of the study area.
Moreover, from the development trend in the reference area (URMT second phase) it could beobserved that landuse changes without approval and the building are not constructed considering
structural resilience. If similar trend of development continues in the study area, then it will result inincreased earthquake risk sensitivity leading to greater damage after an earthquake. Based on
findings from risk sensitivity analysis of landuse plan of the study area, RSLUP strategies have been
proposed on the basis of literature review and expert opinion. The strategies include spatial change inthe landuse plan to address earthquake risk, earthquake resilient planning and construction ensure
earthquake resiliency in built environment, policy initiatives to execute the plan accordingly and,planning strategies to ensure appropriate disaster management, and finally awareness raising
activities to ensure acceptability of the RSLUP strategies by local people.
In this research the study area considered has been in the development phase. So, for analysis of risk
sensitivity in terms of consistency of development with proposed landuse and risk in building stock,a reference area has been selected in this research assuming that similar development will take placein the study area if the current trend of the reference area continues. In reality, the development trend147may vary. While assessing risk sensitivity of landuse plan of the study area, risk in life-lines could
not be considered in this research because the layout of life-lines of the study area was not available.
Additionally, in this research earthquake and its secondary hazards have been considered, but otherhazards (e.g. flood) could not be incorporated considering the scope of this research.
Despite having the limitations, this research will guide policymakers to understand risk sensitivity ofthe proposed landuse plan of URMT (Third phase). This will guide them to take necessary steps tointegrate the proposed strategies to reduce earthquake risk and thereby increase earthquake resilienceof the study area. This research can also be replicated in other areas of Bangladesh as well as in othercountries with necessary modification considering local perspective and other hazard scenarios.
This research brings out the great scope for reduction of earthquake risk through integration of risk
sensitivity in landuse planning by theme-based assessment and strategy development. It brings outthat for integrating earthquake RSLUP strategies in landuse plan effectively, assessment of risk
sensitivity of the plan is a must. Such analysis is more effective at local level as detailed local level
analysis brings out the actual scenario more accurately. Thus it enables development of RSLUP
strategies with more relevance and effectiveness. This research will guide policymakers tounderstand the importance and application of RSLUP and thereby take necessary for this purpose toincrease earthquake resiliency of an area. In future, researches should be carried out to assess risk
sensitivity of landuse considering multiple hazards. Moreover, similar research should be carried out
to integrate RSLUP strategies in developed areas because the scenarios and approach would be
different for such areas.

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PART-VIII

ASSESSMENT OF RIVER BANK EROSION AND
CHANNEL SHIFTING OF PADMA RIVER
USING MULTITEMPORAL SATELLITE DATAAND GIS TECHNIQUE
BANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By:

Ahad Hossain Hridoy
Mehedi Ahmed Ansary156

1. Introduction
The rivers have played a significant role in the lives and living of people in Bangladesh. Rivers arehighly sensitive to environmental conditions. Alluvial channels can respond or readjust at a range ofrates to the variations caused by water and sediment inputs, active tectonics and human activities at a
range of spatial and temporal scales. Any changes, whether natural or anthropogenic, can initiate adeparture from a state of dynamic equilibrium This may, in turn, result in channel instability causingchanges in channel form and pattern. For Bangladesh, changes in frequency, magnitude and depth offlooding are very important. On average, 21% of the area of the country (31,000 km2 ) gets
inundated by floods annually and 21% of the population (assuming uniform population distribution)
is vulnerable to annual flooding and, in exceptional cases, more than 60% of the country or 70
million people are affected by flooding. In 1987, 1988 and 1998, Bangladesh experienced threeextreme floods, leaving trails of devastation and human misery.
The Ganges is one of the major and dynamic rivers in the world originating in the Gangotri glacier ofthe Himalayan (Hossain et al. 2005). The river is named Padma where it enters Bangladesh territory .
The river traverses through India nearly 2150 km (km) and in Bangladesh is nearly 366 km (Hossain
et al. 2005). In Bangladesh the Padma River is joined by the mighty Jamuna (Lower Brahmaputra)
near Rajbari and meets with the Meghna at Chandpur (Allison 1998). The Padma–Meghna–Jamuna
river systems supply every year about 1.2 billion tons of sediments from Himalaya and distribute thiswithin the Bangladesh delta (Kudrass et al. 1998). Coleman 1969) also mentioned that every year
about 2.5 billion tons of sediments are discharged by these river systems in Bangladesh. This highsediment discharge naturally exerts an effect on the sedimentation rates in the rivers (Anon 2014;
Anon 2015; Khan 2015). The Padma River left the signature of its dynamism in the surroundings ofthe present course. Over time the river changes course with bank erosion and leaves the sediments inthe older course. Due to bank erosion and shifting, neighboring communities and individuals had torelocate to new areas every few years as they lose their properties (Elahi et al. 1991; IRIN 2008;
Islam and Rashid 2011). Some people have had to migrate several times. Bangladesh has a uniquehydro-geological setting and deltaic floodplain which is jointly formed by the deposition of theGanges (Padma), Brahmaputra (Jamuna) and Meghna River. Because of the geo-morphology near
Padma River location, river morphology, and the monsoon climate render Bangladesh highly
vulnerable to natural disasters, primarily, floods, cyclones and bank erosion. Bank lines of the Padma
River are particularly unstable. The Padma is wide with major erosion occurring along the left banknear Harirampur upazila of Manikganj district, where an acute erosion problem exists (Banglapedia
29/01/2015). Most recently, the right bank of the Padma has also come under threat of extreme
erosion, particularly in Naria upazila of Shariatpur district. Victims have to live under the open sky
157

as their dwellings were washed away by river waters. Croplands have also been washed away.
However, in the present paper, an attempt has been made to monitor and map the bank erosion andaccretion of the Lower Padma river. The better understanding of such erosion-accretion processes, aswell as techniques to detect such changes, are very useful for planning and management of thefloodplain environment of the Padma river. There is the availability of satellite image data at various
spatial and temporal resolutions provides tremendous opportunity to monitor river bank erosion anddeposition processes. This study report the overall scenario of the affected area of Lower Padmacomparing past forty years of data of erosion and accession of Lower Padma River. This report has
been developed based on the assessment of this massive disruption districts near to Lower Padma
with the assist of Geographic Information System (GIS), and on field validation with transect walk.
Here, GIS and RS methods had been used to analyse satellite image data.

1.1

Literature Review

1.1.1 River Bank Erosion
River bank erosion is important geomorphologically in effecting changes in the river channel course
and in development of the flood plain. These are amongst the most dynamic elements of thelandscape and thus an understanding of the processes is fundamental to our explanation of thedevelopment of fluvial features. River erosion is also important economically due to the loss of farmland and the undermining of structures adjacent to the river channel.( Journal of Hydrology, 42(1979)
Erosion is a geomorphic process that detaches and removes material (soil, rock debris, and associatedorganic matter) from its primary location by some natural erosive agents or through human or animal
activity. (Woodward, J. and Foster, I.A.N., 1997)
River bank erosion is important geomorphological changes in the river channel course and indevelopment of the flood plain. These are amongst the most dynamic elements of the landscape andthus an understanding of the processes is fundamental to our explanation of the development offluvial features. River erosion is also important economically due to the loss of farm land and theundermining of structures adjacent to the river channel; this problem is often underestimated in
Britain. Relatively few detailed studies of the processes of bank erosion have been made until
recently, but these have shown the considerable activity of rivers including several in Britain(Wolman, 1959; Twidale, 1964; Walker and Arnborg, 1966; Lewin, 1972; Hill, 1973; Knighton,
1973; Mosley, 1975).
In a study on river bank erosion along the left bank of the mighty Padma River in the Charghat andBagha Upazilas of Rajshahi District has been assessed using temporal Landsat imageries and socio158

economic approaches. Remote sensing data analysis indicates that about 7297 hectare bank areas
have been eroded during last 40 years since 1975. In 2015, erosion poses high threat to the Pakuria
and Gargari Unions that is estimated 5392 hectare comprising 74% of the total eroded area.
Excessive sedimentation within the channel and change in thawed as well as flow directions due tothe adverse effects of the Farakka barrage are the main causes of the left bank erosion. Based on thesocial survey findings, about 79% of the respondents found river bank erosion, while 69% of themthought river course changing, 55% observed less water flow during summer and 45% realized flood
are the major disasters in the area. Most of the respondents (97%) mentioned that river bank erosion
have affected their daily life and livelihoods. About 84% of the sampled population opined that theylost homestead and operated lands due to the bank erosion (Md. Shareful H., S. Mahmud-ul-islamand Md. Sultan-Ul-Islam)
Knowledge of rates of erosion is fundamental to full interpretation of the landscape, especially inevaluation of the time period required for development of particular features and in assessment of theinfluence of climatic, geological and environmental conditions (Brunsden, D., and Kesel, R.).
River bank erosion occurs primarily through a combination of three mechanisms: mass failure,
fluvial entrainment, and subaerial weakening and weathering. Subaerial processes are often viewed
as ‘preparatory’ processes, weakening the bank face prior to fluvial erosion. Within a river basin
downstream process ‘domains’ occur, with subaerial processes dominating the upper reaches, fluvial
erosion the middle, and mass failure the lower reaches of a river (Pauliner R. Couper and Ina P.
Maddock)
Many of these problems are associated with sampling both in space and time and are exacerbated bya lack of standardization of terms and methods which makes comparison and accumulation of datadifficult. Measurement of river bank erosion has the advantage that the process is relatively rapid
compared with many other geomorphological processes but this enhances its importance ininterpretation of landscape change. Knowledge of rates of bank erosion is also of value ininvestigation of the effects of human activities on channel processes and in short-term prediction oferosion and planning of erosion control (Hooke, J.M., 1980).
1.1.2 GIS and RS
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze,
manage, and present all types of geographical data. The key word to this technology is Geography –this means that some portion of the data is spatial.
Remote Sensing (RS) instruments are acquiring the data by scanning the Earth surface. Different
types of scanning systems are using in Remote Sensing for acquiring the data. A scanning system159

collecting data over a various wavelengths but not continuous is referred as Multi spectral scanningsystem.
The River bank erosion mostly occurs when the river is flooded or soon after the flood. Besides riverbank erosion is occurring at Padma River throughout the year. It is a natural geomorphic process or
disturbance that shapes the land. Satellite remote sensing provides frequent synoptic view of an area
and comprises comprehensive information about the earth surface. Great number of earth observing
satellites orbits around the globe to provide high resolution spatial and temporal data of earth surface.
Landsat is among the most widely used satellite, somewhat in light of the fact that it has the longest
time series of data in currently available satellites.
GIS and RS are important tools which can aid in identifying river changes and bank erosions
situations. A study on Jamuna River was taken with GIS and RS for the measurement by ImranKhan, Muneer Ahammad and Shiblu Sarker Postgraduate Student, Department of Water Resources
Engineering, Bangladesh University of Engineering and Technology for river bank erosion analysis.
In a study on the Brahmaputra River characterized by frequent bank erosion leading to channel
pattern changes and shifting of bank line The study is aimed at quantifying the actual bankerosion/deposition along the Brahmaputra River within India for a period of eighteen years (19902008) (Sarkar, A., Garg, R.D. and Sharma, N.).
In a study it was identified that, Analysis of a series of Landsat images, between 1973 and 1992,
revealed the dynamic nature of the river bank and riverine islands over that period, including channel
migration, movement and widening, submergence, erosion and accretion of riverine islands. Within
the study period, the erosion rate in the Brahmaputra–Jamuna River is 160 m per year, indicating theseverity of erosion hazard along the river(Khan, N.I. and Islam, A., 2003).
1.1.3 SPSSSPSS Statistics Base forms the foundation for many types of statistical analyses, allowing a quick
look at data and its easy preparation for analysis. Easily build charts with sophisticated reporting
capabilities, formulate hypotheses for additional testing, clarify relationships between variables,
create clusters, identify trends and make predictions.
SPSS Advanced Statistics makes analysis and conclusions more accurate when working withcomplex relationships in data, it offers powerful and sophisticated and multivariate analysis
techniques.
SPSS Categories provides tools to obtain clear insight into complex categorical, numerical and highdimensional data. Understand which characteristics consumers relate most closely to your brand, or
determine customer perception of your products compared to others. SPSS Decision Trees helps you
better identify groups, discover relationships between them and predict future events through the160exploration of results and visual determination of how your model flows. Create visual classificationand decision trees directly within the Statistics suite of products and present results in an intuitive
manner.
Correlation Regression
Correlation and Regression are the two analysis based on multivariate distribution. A multivariate
distribution is described as a distribution of multiple variables. Correlation is described as theanalysis which lets us know the association or the absence of the relationship between two variables‘x’ and ‘y’. On the other end, Regression analysis, predicts the value of the dependent variable basedon the known value of the independent variable, assuming that average mathematical relationshipbetween two or more variables (Bland, J.M. and Altman, D.G., 1994).
Standard Deviation
The standard deviation (SD, also represented by the lower case Greek letter sigma σ or the Latin
letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data
values. To calculate the standard deviation of a data set first need to calculate the mean of the data
set. If the data set is considered as x the mean is,
Mean, x̅ = Σ(x)/n; n is the number of distribution.
Standard Deviation, Sx= Σ (x-x̅ ) ^2/n;
Correlation Coefficient
The linear correlation coefficient r, which is a numerical measure of the strength of the association
between two variables representing quantitative data. Using paired sample data, the value of r is use
to conclude that there is (or is not) a linear correlation between the two variables (Dakhil, N.K. andAbdullateef, A., 2016).
If the two data set are x and y and the means are x̅ and ȳ and standard deviation are Sx and Sy, thenCorrelation Coefficient, r=1/ (n-1)*Sx*Sy*Σ ((x-x̅ )*(y- ȳ))Linear Regression
Algebraically describes the relationship between the two variables x and y. The graph of theregression equation is called the regression line (or line of best fit, or least-squares line). (Bland, J.M.
and Altman D.G.,1994). There are three types of linear regression, they are1.

Simple Linear Regression

ŷ = b0+ b1x
2.

Multiple Linear Regression

ŷ = b0+ b1x1+ b2x2+…..+ bnxn
3.

Polynomial Linear Regression

ŷ = b0+ b1x1+ b2x2^2+…..+ bnxn^n161The regression equation expresses a relationship between x (called the explanatory variable, orpredictor variable, or independent variable) and (called the response variable or dependent variable).
The preceding definition shows that in statistics, the typical equation of a straight line “y = mx + b”
is expressed in the form,ŷ = b0+ b1x; Where, b0 is the y-intercept and b1 is the slope.
The slope b0 and y-intercept b1 can also be found using the following formulas.
b0= ȳ-b1 x̅
b1=r*(Sy/Sx), Here r is the Correlation Coefficient of the distributed data;\
1.2

Problem Statement:

For decades, the Padma River has meandered, twisted, and weaved in different shapes throughcentral Bangladesh. Each zigzag and turn tells a geologic story of the region, such as a large flooding
event or the opening of a nearby dam. These events can lead to intense erosion along the banks of theriver, displacing farms, homes, and even lives. Every year, hundreds (sometimes thousands) of
hectares of land erode and fall into the Padma River. Since 1967, more than 66,000 hectares (256square miles) have been lost—roughly the area of Chicago. (World of Change: Padma River - NASA
Earth Observatory)In general, bank erosion is greatest on the outside of the river bends, where several processes caninfluence the land deterioration: changes in river flow, land masses moving downslope, andvegetation wearing away where the river meets the land. Erosion rates vary over time with themagnitude of the flood flow and the type of bank material. In 1998, a large flood rose over thesebanks, exacerbated by the opening of the Farakka barrage (dam) in India. The Padma also has a large
sand-bed, which is easily erodible. From 1998-1999, the erosion rate peaked at 3,120 hectares (12square miles) per year.
In recent Padma has been experienced massive river erosion. Padma River was in a rising trend andspeedy current is engulfing houses and establishments rapidly near areas to the Lower Padma. The
extreme erosion patterns have two main causes. First, the Padma is a natural, free-flowing river withlittle bank protection, other than some occasional sandbags to protect buildings. Over the years,
researchers have observed an increase in the river's sinuosity and braiding. Second, the bank sits on alarge sand bed that can be eroded quickly (World of Change: Padma River - NASA Earth
Observatory)
The erosion swallowed houses, schools, dams, roads, business establishments, mosques. The
residents of riverbank are going through days of fear of further erosion while several more houses are162

still at greater risk Locals have started demonstrating for government action to tackle erosion by theLower Padma.
Meanwhile, the two-storied building of the Upzilla Health Complex, which was over 500 meters
away from the bank a couple of months ago, is being devoured now. Over 4,000 families havebecome homeless and many affluent farmers broke during the same period.

2.

DATA AND METHODOLOGY

2.1 Approach of the Study
This chapter provides insight on how the research has been conducted. This chapter deals withdiscussions on research methodology, sampling techniques and data collection process. It also
addresses the research investigation by highlighting the discussion of semi-structured interviews. The
research has been conducted following a methodology with conformity to the scopes and objective of
the study. The methodology is described in detail as below in figure 2.1.

Define Research Problem

Review Literature

Consultancy With Expert

Design Research
DirectObservation

Reconnaissance Visit

ExpertOpinion
SurveyStudy Area Selection

Primary Data Collection

Data Collection

Literature andjournal Review
Newspaper sandonline articles

Secondary Data Collection

Data Analysis

Interpret & Report

Figure 2.1: Methodology Flow Chart163

2.2 Study Area Selection:
This study concentrates the Padma river is located in Bangladesh, is 120 km long and its width varies
from 4 to 8 km (Chowdhury 2003). The study area (Fig. 2.2) embraced the confluence points of the
Padma-Jamuna at Goalanda, Rajbari and the Padma-Meghna at Matlab, Chandpur. The study area
which lies between the latitude 23°50'–23°13'N and longitude 49°43'–90°39'E, and it passes throughthe Chandpur, Dhaka, Faridpur, Madaripur, Manikganj, Munshiganj, Rajbari and Shariatpur districts.
The Padma river is characterized by Ganges river system. The annual average discharge is 35,000
m3 and the average width of the river is 10.3 km (CEGIS 2010).

2.3 Data Acquisition
2.3.1 Primary Data
To conduct this research, the necessary data were mainly collected from satellite image analysis. The
research methods adapted in this study is analytical in nature because the study tries to find out trendof river bank shifting, erosion and deposition of Padma River in selected districts Chandpur, Dhaka,
Faridpur, Madaripur, Manikganj, Munshiganj, Rajbari and Shariatpur districts were selected as study
area because these districts cover distinct length of Padma River from both sides. Major reasons
behind the selection of these districts as study area are—these districts are located on the bank ofPadma River. River bank shifting and channel migration is very rapid in this area and finallymajority portion is being eroded and newly deposited. An initial field survey was taken too Sariatpur
District and Harirampur Union to verify current situation of Lower Padma River Bank and a site
investigation was taken.
2.3.2 Secondary Data
The Remote Sensing (RS) and Geographic Information System (GIS) methods and other statistical
data techniques have been used for the assessment of river bank erosion-accretion and identificationof bank line shifting pattern of the Padma river. The remote sensing techniques provide a great
possibility to analyze the environmental processes in local or global scale. Landsat images with their
30 m resolution are suitable among others for land cover mapping and change monitoring.
Identification of the channel migration pattern of rivers from satellite images of different years using
GIS and Remote Sensing technology is found very much useful for studying the fluvial
geomorphology of a river. For the purpose of this study the dry season satellite images from 1988 to2018 have been collected. The details of these images are shown in (Table 2.1). Bank lines of theseyears were digitized from the geo-referenced satellite imageries using the ARC GIS and erosionaccretion pattern due to the lateral movement of the active river channel have been estimated using
164the GIS software. Along with these, all the major channels of the Lower Padma River were also
digitized to show the channel migration pattern of this highly morphologically active river.
Two bands as band 3 and band 6 from LANDSAT 8 and LANDSAT 7 Images and band 2 and band
5 from oldest LANDSAT 5 TM were taken To maintain the similarity of spatial resolution andmaximum band configuration among all the images. Some important articles have accessed fromCEGIS library. Other secondary data sources include BBS, Banglapedia, news articles, publishedworks and various online sources. The data also collected from various kinds of university journals
and unpublished research papers, related books and web sites.

Figure 2.2: Lower Padma River Area
Table 2.1: List of LANDSAT Images Data Set

Image
Year
1988
1989
1990
1991
1992
1993

Satellite
LANDSAT_5
LANDSAT_5
LANDSAT_5
LANDSAT_5
LANDSAT_5
LANDSAT_5

Path/Row Acquisition Land
Date
Cloud
Cover
137/044
1988/11/01 16
137/044
1989/11/20 0
137/044
1990/12/09 3
137/044
1991/11/26 0
137/044
1992/11/12 0
137/044
1993/11/15 1
165

Scene
Cloud
Cover
15
0
3
0
0
1

GroundResolution
30m x 30m
30m x 30m
30m x 30m
30m x 30m
30m x 30m
30m x 30m

Source1994/12/04 00
1994
LANDSAT_5 137/044
30m x 30m
1995/11/21 00
1995
LANDSAT_5 137/044
30m x 30m
1996/12/25 00
1996
LANDSAT_5 137/044
30m x 30m
1997/11/26 18
17
1997
LANDSAT_5 137/044
30m x 30m
137/044
1998/12/22
001998
LANDSAT_7
30m x 30m
137/044
1999/11/24
00
1999
LANDSAT_7
30m x 30m
137/044
2000/11/26
00
2000
LANDSAT_7
30m x 30m
2001/11/29 2
2
2001
LANDSAT_7 137/044
30m x 30m
2002/11/16 00
2002
LANDSAT_7 137/044
30m x 30m
USGS
2003/11/19 00
2003
LANDSAT_7 137/044
30m x 30m
2004/12/07 00
2004
LANDSAT_7 137/044
30m x 30m
137/044
2005/12/10
002005
LANDSAT_7
30m x 30m
137/044
2006/12/13
00
2006
LANDSAT_7
30m x 30m
2007/12/16 3
3
2007
LANDSAT_7 137/044
30m x 30m
2009/01/19 00
2008
LANDSAT_7 137/044
30m x 30m
2009/12/21 00
2009
LANDSAT_7 137/044
30m x 30m
2010/11/06 55
2010
LANDSAT_7 137/044
30m x 30m
137/044
2011/11/09
00
2011
LANDSAT_7
30m x 30m
137/044
2012/11/11
00
2012
LANDSAT_7
30m x 30m
2013/12/24 0.08
0.07
2013
LANDSAT_8 137/044
30m x 30m
2014/11/25 0.01
0.01
2014
LANDSAT_8 137/044
30m x 30m
2015/12/30 8.2
8.07
2015
LANDSAT_8 137/044
30m x 30m
2016/11/30 0.08
0.07
2016
LANDSAT_8 137/044
30m x 30m
137/0442017/11/01
4.04
6.88
2017
LANDSAT_8
30m x 30m
137/044
2018/12/22
0.08
0.07
2018
LANDSAT_8
30m x 30m
In this study Modified Normalized Difference Water Index (MNDWI) were investigated from the
aspect as land cover types of water body (W) (Ex: figure 2.3).
Two bands as band 3 and band 6 from LANDSAT 8 and LANDSAT 7 Images and band 2 and band
5 from LANDSAT 5 were taken for the study for river presentation with converting the satellite data
into raster data of water body showing the channel patterns of the study area using MNDWI method
given in the figure 2.4.
Cracking point
Cracking point is the value for separating the water body from the raster data. For every images thecracking point variant from every year. The value was measured from the comprising field survey
and google earth map by calculating the water body parameter.

166

Figure 2.3: Landsat Imagery Data

Figure 2.4: Converted into Raster Data with MNDWI

Figure 2.5: Lower Padma Section
167

The overlaid bank line gave the overall the rate of erosion and accretion, islands area and bank line
shifting pattern of the Padma river from 1988 to 2018. Comparing the image vector data fromdifferent periods, were defined the changes of the river channel position over different time series.
The results revealed the places where erosion and accretion occurred during each period and outputs
were mapped. Bankline shifting was measured taking 24 cross-sections with 5 km intervals along theriver. Each sectioned were differently named considering from alphabet “A” to “V” in figure 2.5.
Using GIS tools each sections were calculate to render width of each sections given in the figure 3.3.

RESULTS AND DISCUSSIONS
2.1 Shifting nature of the Padma River channel
Satellite imagery shows how the river has been transforming in shape, size, and location from 1988
to 2018. The images above were acquired by the Thematic Mapper on Landsat 5 (1988) and the
Operational Land Imager on Landsat 8 (2018). All images were acquired in January and February,during the dry season. Shifting nature of the Padma River in our study area is a common fluviogeomorphic phenomenon which can be observed in any part of the rivers. The historical channelalignment is shown in Figure 3.1. The figure clearly shows the dynamic braided nature of the Padma
River. Over the time period, the channel pattern becomes highly irregular and changes abruptly. The
shifting of bank line from 1988 to 2018 on both banks along the Padma river was measured through24 cross- sections at an interval of 5 km along the river and the results are presented in Figure 3.
Analysis of the satellite images of the research area showed that the highest amount of erosion ofland observed in the left bank along the section K from 1994–2005 near Munshiganj district and in
the right bank along the section F from 2005–2018 near Faridpur and Madaripur districts. The
maximum amount of accretion of land was established in the left bank along the section O near
Shariatpur district and in the right bank along the section L near Madaripur district from 1994–2005.
On the other hand a generalized output of the river bank line for the year 1988 and 2018 is shown in
Figure 3.2. From these figure near Manikganj there is tendency of the channel to move towards theright bank. However near Madaripur and Shariatpur the channel is moving towards the left bank sideand then takes more of a straight shape to Chadpur.
The changes in patterns of channel of Lower Padma River are very frequent. In general, bank erosion
is greatest on the outside of the river bends, where several processes can influence the land
deterioration: changes in river flow, land masses moving downslope, and vegetation wearing away
where the river meets the land. The middle sections of the Padma River tend to experience low ratesof erosion. Arcmap 10 was used for erosional and depositional pattern, channel bar development, andbankline shifting analysis. Assessment of erosion and deposition of both banks, polygons of two168

particular years were taken (superimposing) using the union method of Analysis tool. Channel bar
development, its statistics, width of the river at different times, and statistics of the bankline shifting
were calculated by using Microsoft Office Excel 2007 and Arcmap 10. The channel widths fromboth banks of this river at different times of different cross sections were measured perpendicular to
the two banklines at the same fixed geographical points by using the measure tool in Arcmap 10.
24 cross sections were drawn in different parts of the river course (Fig. 3). The river width’s standard
deviations (SD) were calculated for different times on the basis of river widths at different crosssections (Fig 3.3) The river center points from both banks of this river at different times of different
cross sections were pointed out perpendicular to the two banklines at the same fixed geographicalpoints by using the line construction tool in Arcmap 10 (Fig. 3). On the basis of these center points,
river center lines at different times were drawn (Fig. 3) to delineate the shifting of center lines of theriver to both banks at different times.

Figure 3.1: Channel Shifting of Padma River from 1988 to 2018
The shifting is represented within five years intervals in figure 3.2. From this figure picture A shows
the differences of lower Padma Channel pattern from 1988 to 1993, in the figure B channel patternchange is showed from 1994 to 1997, in figure C the channel changes from 1998 to 2003 is169

represented, in figure D channel changes from 2004 to 2008, in figure E channel changes from 2009
to 2013 and in figure changes of channel from 2014 to 2018 is represented.

A

B

C

D

E
F
Figure 3.2: Channel shifting within 5 years intervals from 1988 to 2018170

During the period from 1988 to 1993 and 1994 to 1997 the eroded lands were about 43.4 km2 and49.8 km2 at a rate of about 10.86 km2 and 12.45 km2 year−1 . The erosion rate was increased on theleft bank, while on the right bank the situation was more or less similar in respect of earlier period.
Afterwards, during the period from 1998 to 2003 and 2004 to 2008 the erosion was dramaticallydecreased on both banks. About 15.62 km2 and 91.41 km2 of lands were eroded on the left and rightbanks, respectively, at a rate of about 1.04 km2 and 6.10 km2 year−1.IN 2009 to 2013 the erosion
rate remain same. Consequently, from 2014 to 2018, the erosion was again increased on both banksof the river, while on the right bank the increasing rate was more than on the left bank. About 7.25
km2 and 30.69 km2 of lands were eroded on the left and right banks, respectively, at a rate of about
2.42 km2 and 10.23 km2 year−1 . Over the past 30 years, about 132.6 km2 and 245.7 km2 lands
eroded on the left and right banks, respectively, at a rate of 4.82 km2 and 8.19 km2 year−1.
Therefore, erosion was higher on the right bank than on the left bank over the past 30 years. Duringthat period about 11 km2 and 37.90 km2 lands also accreted on the left and right banks, respectively,
at a rate of 0.28 km2 and 0.95 km2 year−1. In the same period about 20.88 km2 and 107.19 km2
lands again redeposited on the left and right banks, respectively. The study also discloses that theerosion rates were not always same on both banks of the river. Sometimes erosion was more
prominent on the left bank when the situation was reverse on the right bank. Saleem et al. (2019) also
indicated that erosion and deposition vary both spatially and temporally on both banks of the Lower
Padma River.
2.2

Backline Migration and Erosion Assessment

The satellite images from 1988 to 2018 have been visually interpreted to identify river width andbankline migration of the Lower Padma River at different times (Figs. 3.3). The river width has beenmeasured perpendicular to the two banklines at the same fixed geographical points. 24 cross sections
have been drawn in different parts of the river course. These are A - V (Fig. 3.3)
In cross section “A to E” the width of the river follows an increasing trend from 1988 to 1999, andafter that it was a decreasing trend. But the overall trend is a decreasing one at a rate of 0.018 km per
year (Fig. 3.3). . In case of cross section “F to H” the width of the river continuously follows anincreasing trend at a rate of about 0.26 km year−1 ( Fig. 9). In cross section “I to K” the river widthfollows increasing trend from 1988 to 1999, and after that it was a decreasing trend . However, theoverall trend is an increasing one at a rate of 0.03 km year−1 (Fig. 3.3). In cross section “L to O” theriver width was also gradually increased from 1988 to 1999, and after that it has been decreased. But
in the overall result, the width of the river in this section has been increased at a rate of 0.05 km
171year−1 (Fig. 3.3). In cross sections “p and Q” the river width was increased from 1988 to 1999 but
after that slightly decreased. In cross sections “R to V” the widths of the river have been gradually
increased from 1988 to 2018 at a rate of 0.11, 0.10 km year−1 , respectively ( Fig. 3.3). The analysis
shows that the average width of the river follows an increasing trend from 1988 to 1999, but afterthat it was a decreasing one from 1999 to 2014 and again increased from 2014 to 2018 . As a whole,
the river width shows an increasing trend at a rate of 0.083 km year−1 over the past 30 years . The
river width’s standard deviations (SD) have been calculated for different times on the basis of riverwidths at different cross sections (Table 10). The SD indicates that the river width at different partsof its course were not same.

Figure 3.3: Sectioning Lower Padma Naming Acceding A-V
The historical bank line movement of the Padma River is shown in Figure 3.5. An interesting thingvisible from this figure as well as from table 1 of appendix is that the average width of the Padma
River is showing an increasing trend. To verify this observation the average width of the river for all
the available satellite images were determined using the digitized bank lines. A plot was preparedshowing the changing width of the Padma river which is shown in Figure 3.4. The figure clearlyshows the increasing and decreasing average width of the river.
In the figure x axis represents the increasing intervals of time period of 30 years from 1988 to 2018considering one unit as one year and y axis represents the average width of Lower Padma River.

172

Average Width Of Lower Padma River
12000

Width(m)

10000
8000
6000
4000
2000
0
0

5

10

15

Year

20

25

30

35

Figure 3.4: increasing and decreasing average width of the lower Padma RiverIn figure 3.6 the variation in erosion and accretion amount of section A to V are given from 1988 to
2018. In the chart the sections are defined in variant colors and positive results in y axis are
representing the erosion amounts and the negative results explains the accretions represented in the
figure 3.6, as well as Table 2 and 3 define the amount of erosion and accretion in individual section.
From the bar chart the erosion and accretion can be explained throughout the year from 1988 to 2018
and erosion is much frequent in every year. Erosion occurs in every year with a severe amount but
1990, 1995, 2003 and 2007 at section A, D and L had the major impacts on Padma for channel
shifting and river path changes.
And also can be noticed that the accretion amount was not too regular but the changes of
1988,1990,1996,1997,2003,2005,2006 were also contribute a huge impact on channel pattern
shifting.
Considering the low amount of accretions all over the years in 1990, 2005 and 2006 at section E, F
and S to V can be seen from the bars a huge amount of accretion occurred during these time period(figure 3.6). In these periods some huge areas of land increased due to the lack of river flow
resulting decreasing the river widths with a severe amount.

A

B
173

C

D

E

F

Figure 3.5:Erosion and Deposition map of the Lower Padma River in Different Time Duration:
A (1988-1993), B(1994-1997) ,C(1998-2003), D(2004-2008), E(2009-2013), F(2014-2018)
30000
20000
10000

‐10000‐200001988‐89
1989‐90
1990‐91
1991‐92
1992‐93
1993‐94
1994‐95
1995‐96
1996‐97
1997‐98
1998‐99
1999‐00
2000‐01
2001‐02
2002‐03
2003‐04
2004‐05
2005‐06
2006‐07
2007‐08
2008‐09
2009‐10
2010‐112011‐122012‐132013‐142014‐15
2015‐162016‐172017‐180‐30000‐40000‐50000‐60000
A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

Figure 3.6: Erosion and accretion amount chart of every sections from 1988 to 2018174

4.1 Conclusion
Bangladesh is predominantly a riverine country where river bank erosion is an annual disastrous
phenomenon, especially in the Padma river, erosion-accretion is a common and very frequent event.
The channel and bank line pattern of the Padma river changes continuously. The present study showsthat satellite data like Landsat can be successfully used to monitor river bank erosion-accretion using
the application of RS and GIS with multi-temporal satellite images. It has been revealed that sharpchanges in river channel erosion/accretion in recent years resulting in considerable formation andloss of lands and represents the retrospective scenario of the Lower Padma river. The
geomorphological formation and the physical dynamics of the Lower Padma is subject to runoff
from the highest and most tectonically active mountain range in the world, the Himalayas. These
young alpine mountains are naturally subject to severe erosion. Subsequently the Padma River
carries a very heavy sediment load. The combination of large and variable discharges of water andsediment is responsible for the Lower Padma’s braided pattern. Braided rivers are characterized byunstable bank lines and rapid rates of lateral movement. Spatial-temporal analysis of various data
sets reveals that very high rates of erosion can occur over periods of 1 year or a few years, but thesame patterns of erosion are not sustained for many years at the same location and they do not occur
at all locations simultaneously. Similarly, low rates of erosion have been identified in some parts of
the river, but the duration of low rates of erosion is short. Over the whole river, it is found thatriverine islands most often persist from 1 to 7 years, but 30% have lasted for 14 years or more. The
analysis provided the opportunity to predict bank erosion rates over time and space, as well as inphysical direction. It was found that erosion and accretion was more dominant in the middle part of
the river. Results of this study revealed that erosion and accretion were more pronounced in the rightbank than that of the left bank. This finding could be used to measure or estimate the vulnerability ofthe protection measures already in place, estimate potential erosion loss of mainland area, andestimate the population at risk. River bank erosion is treated as one of the foremost natural disasters
responsible for poverty in Bangladesh due to the enormous destruction of resources anddisplacement of large numbers of the population. The hazard also has an impact on unemployment
levels in rural Bangladesh. This study shows the urgency of giving regular wake-up calls to thepeople concerned under potential threat of river bank erosion through a warning system and the needto adopt possible strategies that may assist in mitigating the human suffering and adverse socioeconomic impacts of these recurring natural events.
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Catchments: Environmental Controls, Processes and Problems. Geography: Journal of theGeographical Association, 82(4), p.353.
Xu, H., 2006. Modification of normalized difference water index (NDWI) to enhance open waterfeatures in remotely sensed imagery. International journal of remote sensing, 27(14), pp.3025-3033.
.
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PART-IX

GIS-BASED LANDSLIDE SUSCEPTIBILITY
MAPPING USING ANALYTICAL HIERARCHY
PROCESS (AHP) IN BAGHAICHHARIUPAZILA, RANGAMATIBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By:

Sayma Ahamed
Mehedi Ahmed Ansary

177Introduction
Landslides represent a major threat to human life, properties, infrastructures, and naturalenvironments in most mountainous regions of the world. Statistics from the Centre for Research on
Epidemiology of Disasters (CRED) (Brussels, Belgium) show that landslides are responsible for atleast 17% of all fatalities from natural hazards worldwide (Lacasse and Nadim, 2009) landslide isrecognized as a disaster in mountainous region and responsible for huge social and economic losses.
Particularly, earthquake-induced landslides are significant threats for the inhabitants of mountainareas in terms of casualties, infrastructure damages, and destruction of property (Keefer 1984). The
landslide is a common natural phenomenon which a slope collapses abruptly due to weaken selfretain the capability of the earth under the influence of the gravitational force. It causes for theorientation of bedding planes, slope steepness, water and drainage, soil composition, vegetation,
joints and fractures and by the sudden tremors (Islam and Rahman 2014). Some other reasons that
contribute to the slope failure are the high shear stress, lack of lateral support or removal of support,
weathering and low intergranular force due to seepage pressure (Islam and Rahman 2019).
Bangladesh is highly vulnerable to various types of natural hazards and disasters (Rabby, Hossain,
and Mahbub, 2019). Chittagong Hill Tracts (CHT), in particular, are mainly prone to landslides in
Bangladesh (Rabby and Li 2019). Heavy rainfall during the monsoon is the main triggering event forlandslides in the region (Khan et al. 2012). Global climate change has exacerbated the extreme andprolonged rainfall, resulting in landslides with increased frequency in recent times (Ahmed and
Dewan 2017; Ahmed et al. 2018: Abedin et al. 2020). The active seismic zones, high steep slopes,
unfavorable geological conditions make the hilly part of Chittagong specially Rangamati is one of
the most hazard prone areas in Bangladesh. Besides, the role of people in causing the hillslope
instability is unprecedented, which is manifested through a housing development on the hillslope,hill-cutting, increased population pressure, deforestation, unsustainable agricultural practices, urban
development, and weak governance (Ahmed 2015; Rahman et al. 2017) (11,12). More than 350
people have died as a result of landslides in Chittagong in the last three decslams (Islam and Islam2017). Despite casualties, damages, and environmental degradation caused by landslides, nosufficient conservation efforts and hazard mitigation programs have been conducted in these areas
due to weak institutional capacities. Various studies on landslide prediction and reduction inlandslide damage have been performed. Some other reasons that contribute to the slope failure are
the high shear stress, lack of lateral support or removal of support, weathering and low intergranular
force due to seepage pressure (Islam and Islam 2017). A series of landslide in the Rangamati area inthe recent years indicated that there was a progressive time of occurrence between two consecutive
landslides. A total of 118 people, including five army men, were killed in Rangamati, which was hit
178hardest by the landslides in 14 June, 2017. Baghaichhari is a upazila of Rangamati district, thisupazila is highly vulnerable to landslide because of its geographic features. Moreover, the rapid
population growth and eventually rapid urbanization fosters, unplanned land use practices andobviously illegal hill cutting which is the collective facts for enhancing the vulnerability of landslides
in this locality. In recent years, landslide hazard analysis and risk assessment have become a major
subject in landslide studies and consequently, much of the recent progress has been in these areas. In
particular, the application of information and geospatial technologies such as remote sensing andgeographic information systems (GIS) has greatly contributed to landslide hazard assessment studies
over recent years.
Landslide hazard assessment, including hazard investigation and analysis, refers to identifying andquantitatively describing the potential landslide hazards, and consequently, evaluating the probabilityof occurrence of landslides within a specified period of time. Because landslide hazards haveimportant spatial components related to the initiation of the hazard and the areas affected bylandslides (van Westen et al., 2008). Landslides cause soil erosion, land degradation, and loss of
ecosystem integrity as well as substantial damage to the property, infrastructure, agriculture,
economic development, and human lives (Guzzetti et al. 2012; Pourghasemi and Rahmati 2018;
Yilmaz 2009; Chen et al. 2017). Given its significance, researchers, policymakers, and disastermanagement planners have become interested in understanding the probability of landslides that arelikely to occur in a region– known as landslide susceptibility – so that strategies could be developed
to reduce future risks and damages. Landslide susceptibility assessment, which shows the probabilityof landslides over an area, is critical to minimize the losses and deaths caused by the landslide.
Landslide susceptibility mapping is essential to mitigate landslide disasters, and a landslide inventory
is the first step toward susceptibility assessment (Rabby, Hossain, and Mahbub, 2019). The landslide
susceptibility mapping is a useful technique in identifying the most susceptible areas of a region. The
empirical observation of which is essential for science-based ecosystem management, land use
planning, and disaster mitigation strategies (Ahmed 2015; Lei, Chen, and Pham 2020). Usually, thelandslide susceptibility maps have been inaugurated using different GIS based methods andstatistical analysis, such as the analytical hierarchy process (AHP), frequency ratio, bivariate,
multivariate, Logistics regression, fuzzy logic, and artificial neural network (Matori, Basith et al.
2012). Landslide susceptibility can be investigated using quantitative and qualitative methods.
Quantitative methods determine the mathematical relationship between landslides’ occurrences andtheir associated predisposing factors (Althuwaynee, O.F and Pradhan, 2014). Quantitative methods
can be categorized as deterministic and probabilistic methods. In a deterministic approach, a slope
safety factor, defined as the ratio of shear strength to shear stress, is commonly used to determine an179area’s landslide susceptibility (Cheng, Peng and Hong 2019). This approach is suitable for small
areas due to the challenge of measuring the safety factor over a large area (Merghadi and
Abderrahmane, 2020). Qualitative methods depend on expert knowledge and judgment. Qualitative
methods include field geomorphological analysis and index-based approaches, namely, theAnalytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) (Vakhshoori andZare 2016; Yilmaz, I 2010). In this study, analytical hierarchy process (AHP) is used for landslide
susceptibility mapping. The method selection for landslide susceptibility mapping depends on thescale of analysis, cost, the timeline of the project, and the inventory data. Away from the influence of
aforementioned major factors on the spatial distribution of landslides, rainfall, slope, lithology,
aspect, land use and land cover (LULC) are also a significant factor in the landslide susceptibility
assessment. However, high-resolution satellite data and GIS have also gained significant importancefor construction of thematic data layers used to produce the valid susceptibility mapping. The
production of landslide susceptibility map describes the prone area where landslide may occur in the
future. Therefore, the susceptibility maps of the area are very important for effective management oflandslide hazards and future planning. Considering the extreme causalities occurred in recent time in
the Hill tracts, especially in the Baghaichhari Upazila, Rangamati region due to landslide, this study
aims to produce a landslide susceptibility map for Baghaichhari upazila by integrating Frequency
Ratio (FR) method and Analytical Hierarchal Process (AHP) method. This study also aims toidentify the high susceptible road of this upazila using GIS and remote sensing technique.

Problem statement
Landslides are a common problem in the Chittagong hill districts (CHD) of Bangladesh. CHD is
broadly classified into two major groups, urbanized hill districts (includes Chittagong and Cox’s
Bazar) and indigenous hill districts (includes Bandarban, Khagrachari and Rangamati). Althoughlandslide disasters were infrequent in densely populated Bangladesh in the past, increasing humanactivities such as hill cutting for residential development has resulted in many landslides (Bayes2010). The recent trend of spontaneous urbanization in the hills (i.e., covering approximately 10% of
the total land area of Bangladesh) and the resulting impact of landslides on hilly communities
indicate a sharp escalation of landslide disaster risks in Bangladesh (Ahmed and Rubel 2013; BUETJIDPUS 2015; Ahmed and Dewan 2017). Landslide refers to the movement of debris, rocks, andsoil under the influence of gravity 1 It is a common phenomenon in mountainous areas (Roy, J.;
Saha, S. 2019) and accounts for 9% of the world’s disasters (Galli, M.; Ardizzone, F. 2008).
Landslide causes damage to infrastructure, human fatalities, and economic losses (Cipolla, F.;
Sebastiani, C. 2004). Two types of factors, predisposing and triggering ones. Predisposing factors
180create suitable conditions for landslides, whereas triggering factors initiate the landslides (Guzzetti,
F. 2012). The predisposing factors of landslides include slope, elevation, aspect, curvature, geology,
and land use/land cover (Ahmed, B. 2017)
After the recent landslide occurrence of 2017, landslide susceptibility mapping of the Rangamati
district has become indispensable to identify the future landslide hazard-prone areas. Rangamati,
which is well known as indigenous hilly district (B. Ahmed 2017) suffered highest negative
consequences in its history, as the disaster claimed lives of 152 people in total, severely destroying
6,000 dwellings, roads, telecommunication system, and power supply, along with an economic lossof about USD 223 million 12 The study area of this research is Baghaichhari Upazila of Rangamati
district that is consist of hill tracks and mountains. The roads here are incurved in the hills of
Rangamati. Sajek valley is a critical point of this study area having a reputation of uprising tourist
attraction. During season the human load along with live load of transportations endanger thestability of the hill curved road and the slope of the mountain at higher elevation. Steep slopes, water
saturation, geometry, land cover and previous landslides all play an influential role in landslide in
Baghaichhari Upazila. Also having all the predisposing factors in play, the live load of tourist alongwith their heavy transport acts as another triggering factor as severe as the earth quack and heavy
rainfall.

So, keeping the importance of the study area in mind it is very clear that landslide

susceptibility mapping of this hill track area of Baghaichhari upazila is essential and critical points
are to be marked and monitored to avoid any type of accidental losses of human lives or properties.
The devastating impact of the landslides can be reduced by taking appropriate mitigation measures
after identifying the susceptible hazardous areas. Landslide susceptibility mapping involves thedetermination of the spatial extent of a particular type of landslides, its volume, and the probabilityof its occurrence over a period of time and space (R. & W. C. Soeters 1996) So using landslide
inventory, gradient, slope, land cover data etc. this study has a determination to develop highlandslide prone area susceptibility map.

Review of some literature
Landslides are one of the most significant natural damaging disasters in hilly environments (Ayala etal. 2006). Social and eco-nomic losses due to landslides can be reduced by the means of effectiveplanning and management (Rajakumar et al. 2007). Land-slide hazard assessment is generally basedon the concept that ‘the present and the past are keys to the future’. This is why, most landslide
hazard analyses take into account an up-to-date land-slide inventory that represents the fundamental
tool for identifying the hill-slope instability factors in triggering landslides (Lee and Sambath 2006).

181

n Bangladesh landslide events mainly occur in the hilly parts of Chittagong Division. Chittagong is
the largest port and second largest city of Bangladesh and plays a substantial role in the economic
development of Bangladesh. Locational advantages and opportunities lead to rapid urbanization andcompact urban form (Rahman, 2012). Landslides have occurred frequently in Chittagong city due toextreme rainfall. The devastation aggravates with weak geological structure, unplanned and erratic
use of hills and settlement development. The unplanned and haphazard urbanization (Rahman et al.,2012), land-cover change, coupled with the increased intensity and frequency of heavy rainfall, iscausing landslides in Bangladesh. Land cover changes (e.g., urbanization, deforestation) cause large
variations in the hydro-morphological functioning of hill-slopes, affecting rainfall partitioning,
infiltration characteristics and runoff production (Chau et al., 2004). Many urban dwellers and theirlivelihoods, quality of life, property and future prosperity are being continuously threatened by therisks of rainfall triggered landslides (Ahmed et al., 2014a). The development authority of Chittagong
has identified 30 risky hills (Chakraborty and Uddin, 2014) among 88 hills of 18304 acre (Chisty,2014). More than 10,000 people are currently living in such vulnerable areas. People are living at thetoe and on the slopes of hills with high risk of landslides and associated damage (Mia et al., 2016)(36). Many devastating landslides occurred in these hills in recent past. Landslides triggered byheavy rains in Chittagong claimed at least 185 lives in the last few years.
Landslide is a term generally used to describe the downward movement of a portion of a hill slope
containing soil, rock, and organic materials under the effects of gravity and also the landform that
results from such movement (Highland et al., 2008). Landslide inventory can be seen as datasets ofmultiple events which may include but are not limited to landslide locations, date of landslide, typeof the landslide, potential causes, and damage information (Hervás, 2013). A past disaster event canbe seen as an opportunity to learn the lesson to enhance future disaster mitigation capacity (Rahman
and Kausel, 2012). Thus, it is important to have a critical evaluation of past landslides to understandthe causes and issues related to these events. Landslide inventory is one of the most important data
for many landslide studies such as susceptibility mapping (Ahmed, 2015; Cardinali et al., 2006).
landslide hazard zonation (Anbalagan et al., 2015), slope instability recognition (Soeters and vanWesten, 1996), spatial distribution of mass movement. This kind of information can also be usefulfor urban land-use planning (Rahman and Islam, 2013).
Various geo-structural as well as causative-factor based approaches are already available forlandslide susceptibility zoning. But Geographic Information System (GIS) modelling of landslide
phenomena has taken precedence in recent time. Geospatial technologies like the use of GIS, Global
Positioning System (GPS), and Remote Sensing (RS) are useful in the hazard assessment, risk
identification, and disaster management for landslides. GPS is a space-based global navigation182

satellite system which provides the information of position and time anywhere in the world in all
weather conditions (Akbar and Ha 2011). Previous studies showed the application of GPS for
mapping and identifying landslide zones. GIS is used for data collection, storage, and analysis ofprocesses where geographic information is involved. The use of GIS for land-slide mapping iscommon in various studies. Remote sensing is the science in which information is acquired about thesurface of earth without physically being in contact with it. RS is also used for monitoring andmapping of landslides (Akbar and Ha 2011). Mapping the areas that are susceptible to landslides isessential for proper land use planning and disaster management for a particular locality or region.
Throughout the years, different techniques and methods have been developed and applied in theliterature for landslide susceptibility mapping. Landslide susceptibility maps can be produced using
both the quantitative or qualitative approach (Park et al. 2013).
Qualitative methods simply portray the hazard zoning in descriptive terms (Guzzetti et al. 1999). But
because of the developments in computer programming and geospatial technologies, quantitativetechniques have become popular in recent decades. Moreover, it incorporates the causes of landslides(instability factors) and probabilistic methods (Bai et al. 2010).
There are mainly four methods available to map landslide susceptibility, namely landslide inventory
based probabilistic, deterministic, heuristic, and statistical techniques (Guzzetti et al. 1999). Withinthese techniques, the probabilistic and statistical methods have been commonly used in recent years.
These methods have become more popular, assisted by GIS and RS techniques (Lee and Sambath
2006). The probabilistic (non-deterministic) models like frequency ratio, bivariate analysis,
multivariate analysis, and Poisson probability model (Bui et al. 2013) are more frequently used todetermine the landslide susceptibility zones (Zêzere et al. 2008)
This method is also known as landslide inventory and provides a spatial distribution of existinglandslides represented on a map either as the affected area (polygon) or as point events (Wieczoreck1984 & 1987).
Disadvantage: it does not relate landslides to their causative factors
Advantage: it is economic and can cover a large area
Map Combination
The map combination approach is a simple procedure that combines different thematic maps
based on the knowledge of the expert. This approach involves the following steps (Soeters & vanWesten 1996): the Selection and mapping of landslide controlling parameters.
Thematic data layer preparation with relevant categories of the parameters.
Assignments of weights and rankings to parameters and their categories respectively.
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Integration of thematic data layers. Preparation of landslide susceptibility map showing
different zones.
Disadvantage: It strongly depends on expert knowledge and therefore can inherit human error andbias judgment.
Advantage: It is simple as compared to the other methods, which normally use complex equations
Quantitative Approach
Probabilistic
ApproachThis approach compares the spatial distribution of landslides in relation to different

causative factors. It is based on the Bayesian probability. Some models based on this approach
include conditional probability model, Weight of evidence method, certainty factor method underfavorability mapping model, etc.
Disadvantages: It requires known landslide points as an input data set and can over estimates if thenumber of known landslide points is too much. Therefore, random selection of the landslide pointthat would be used is crucial.
Advantages: The fact that it uses known landslides points makes it the mostsuitable model for landslide susceptibility mapping, as landslide studies are based on the assumptionthat future landslides will occur under similar circumstances as historic landslide.
Multivariate
Statistical
Analysis
Multivariate approaches consider relative contribution of each of the thematic data layer tothe total susceptibility within a defined area. The procedure involves several important steps (Aleotti& Chowdhury 1999):
Identification of percentage of landslide affected areas in each pixel and their classification
into stable and unstable zones.
Preparation of an absent/present matrix of given category of a given thematic layer.
Multivariate statistical analysis and reclassification of the area based on the results and theirclassification into susceptibility classes.
Moreover, GIS-based Multi Criteria Decision Analysis (GIS-MCDA) provides powerful techniques
for the analysis and prediction of landslide hazards. GIS-MCDA belongs to heuristic analysis. These
include the Analytic Hierarchy Process (AHP), the Weighted Linear Combination (WLC), theOrdered Weighted Average (OWA), etc. (Feizizadeh and Blaschke 2013). Analytic Hierarchy
Process (AHP) is used to complete the study.
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Remote Sensing and GIS for landslide susceptibility mappingTechniques for landslide mapping have changed little, in principle, over the past few decades evenwhen newer data sources become available (Sarkar & Kanungo 2004). Landslides are most often
detected and mapped by a combination of interpretation of air photos or multispectral digital imagery
and selected ground verification information (Roering & McKean 2004), and is often based on“professional judgment” (Wieckzorek 1984). There has been a drastic increase in magnitude andfrequency of natural disasters around the globe but at the same time there has been improvements inthe technical capabilities to mitigate them. The increased efficiency of computers has created
opportunities for detailed rapid analysis of natural hazards. The acquisition of information throughremote sensing and spatial data analysis using GIS has improved the capabilities of geo-informatics
in the field of disaster management (Dahal et al. 2007). The following section describes some of the
GIS techniques and remote sensing tools that have been used for landslide susceptibility mappingand early warning.
GIS modeling for Landslide Susceptibility Mapping –Landslide hazard is normally depicted on maps which show spatial distribution of hazard classes.
The development of these maps requires knowledge of the processes active in the area being studied(geological, hydrological, land-cover, and morphological factors), as well as triggering mechanisms
leading to the occurrence of landslides (e.g., rainfall and seismicity) (Kanungo et al. 2009).
Landslide hazard maps typically aims to predict where failures are likely to occur without any clear
indication of when they are likely to occur. However, the focus on time-based modelling techniques
have proved to be useful for providing landslide hazard information needed for planning andprotection purposes (e.g., Brunettii et al. 2010). Geographic information systems and the selection ofparameters that are deemed to influence landslide occurrence in a certain area and the consequentpreparation of corresponding thematic data layers are crucial components of models for landslide
susceptibility mapping (Sarkar & Kanungo 2004). The parameters that are generally deemed togovern instabilities include geology, geomorphology, land use, climatic conditions, hydrology,
vegetation and geohydrology (Dahal et al 2007). These factors can vary both locally and/or
regionally. The derivation of landslide susceptibility maps involves the combination and integrationof spatial information on these factors to provide an indication of the areas where the combination offactors is such that they create an environment conducive to landslide occurrence.
Different approaches have been used to weight landslide controlling parameters and to model
landslide susceptibility maps. The choice of the appropriate technique strongly depends on the natureof the problem, the observation scale and data availability (Temesgen et al. 2001, Lee, Choi. & Min
185

2004, Sarkar & Kunongo 2004). Landslide susceptibility mapping approaches can be grouped into
two broad categories; qualitative and quantitative (Glade & Crozier 2005). In the qualitative
approach, a lot of subjectivity is introduced in preparation of various thematic data layers
contributing for landslide occurrences, which are integrated in a GIS to create a landslide
susceptibility map of the area (Kanungo et al. 2009). The quantitative approach focuses ondeveloping the ways of quantifying the relative importance of various causative factors (Kanungo etal. 2009).
Digital Elevation Model
DEMs are considered one of the basic data sources for three-dimensional modeling of the Earth’s
topography (Mutluoglu, O 2010) and also suitable to provide a snap shot of landscape along with theavailable features having the elevation values (Toz, G.; Erdogan, M. 2008). DEMs have been defined
as digital representations generated with elevation values at the equal grid intervals of the terrain(Yakar, M 2009; Li, X 2017; USGS; Taud, H. 1999). The United States Geological Survey (USGS)
has also defined these models as a digital cartographic representation method for the elevation of theterrain at regularly spaced intervals x and y directions using z (elevation) values referenced to acommon vertical datum. Open source DEMs have replaced higher-resolution elevation models in a
few applications, however, they are not feasible in applications that demand high accuracy. The
accuracy of a DEM is always dependent on the quality of the field survey data collection methods(Wang, T. 2015), and these include contour insertion/plotting, scanning quality, digitization
accuracy, map scale, and interpolation techniques.
Landslide inventory
Landslide susceptibility mapping is essential to mitigate landslide disaster, and landslide inventory is
the first step towards the susceptibility assessment (Guzzetti et al. 2012). Since landslides generally
occur in existing slide areas, it is vital to know the locations of occurred landslides, size oflandslides, and their-related geomorphological factors (Chen et al. 2017) Landslide inventory is adataset of various information associated with landslides, including the absolute and relative
location, date, type, size, distribution, casualties, and causes of landslides (Guzzetti et al. 2012).
Several methods have been used for landslide inventory mapping, including field mapping and visualinterpretation of aerial and satellite images (Rabby and Li 2019). The first step of landslide inventory
is to map the exact location of landslides and then to construct the dataset of landslides (Galli et al.
2008). A good landslide inventory is shareable with broad scientific community and stakeholders(Guzzetti et al. 2012).

186

Landslide controlling parameters
The identification of historic landslides and the analysis of the conditions leading to those landslide
events is critical when attempting to identify landslides controlling parameters (Campbell 1975;
Clerici, Perego, Tellini, Vescovi 2002; Morton, Alvarez, Glade 2005). The parameters affecting
landslide occurrences can be broadly grouped into two categories (1) preparatory factors, whichmake the area susceptible to slope failure and (2) triggering factors, which sets off the movement(Crozier & Michael1986).
The parameters that affect an area’s susceptibility to landslide include (1) geology, (2)geomorphology (3) human activities (4) and landcover (Wu & Siddle 1995; Atkinson & Massari
1998; Sidle et al; Sarkar & Kanungo 2004; Dahal et al. 2007)
Hence, in landslide hazard assessment practice, the term “landslide susceptibility mapping” isaddressed without considering the variable factors in determining the probability of occurrence of alandslide event (Dai et al. 2001). The investigation of triggering mechanisms such as earthquakes
and rainfall are critical but determining the magnitude and temporal behavior of these parameters andhow it relates to landslide susceptibility has proved to be challenging (Sarkar & Kanungo 2004).
The following sections describe some of the controlling parameters affecting landslide development.
These factors have been subdivided into three categories, each contributing to a separate category oflandslide causative factors (preparatory parameter or triggering mechanism). They are:
Static factors – These factors are those that are unlikely to change within a short period oftime like geology, the geomorphology, the soil type and depth and the vegetation type – these define
the landslide preparatory factors.
Variable factors – These are the highly variable factors that can vary seasonally to daily
including vegetation health (NDVI) and productivity and soil water contents – these contribute toboth preparatory factors and triggering mechanisms.
Triggering mechanisms – These are the mechanisms that, when both static and variableconditions are favorable for landslide occurrence, will cause a landslide. Potential triggering
mechanisms include high intensity rainfall events and/or seismic activity. The premise behind thesubdivision lies in the fact that the static factors will define the area's susceptibility to landslide
occurrence (Dahal et al. 2007).
Geology
The geology of an area is a critical parameter controlling the occurrence of landslides and various
studies have used geology as a parameter when modeling landslide susceptibility maps (e.g., Dahalet al. 2007; Temesgen et al. 2010; Singh et al. 2011). Different lithologies have different chemical
187

and physical properties leading to different susceptibility to mass movement. For example, different
rock types have different hydrological properties i.e., transmissivity, hydraulic conductivity andpermeability (Varnes 1984). These properties play a significant role on slope instabilities during
rainfall events. Hence shales and siltstones are considered to be more susceptible to slope instability,
while sandstones and conglomerates are regarded to have moderate to low susceptibilities tolandslide occurrence (Singh et al. 2011). The sequence of the stratigraphy can also determine thestability of the area. One such example is a sequence that consists of an impermeable layer on thebottom, which is overlain by a permeable layer. Such a sequence would have higher potential tosaturate with water during rainfall events, resulting in a higher susceptibility to landslide occurrence.
Additionally, the presence of dykes and sills are of importance since they could have weakening
effects on the lithologies (Singh 2009). The combination of rock types and structures in an area will
dictate the resistance to weathering and erosion processes and ultimately, landslide susceptibility(Singh et al. 2011).
Geomorphology
The geomorphology of any landslide prone area has been found to be the most important controllingparameter by several authors (Sarkar & Kanungo 2004). Information on the geomorphology,
including slope, aspect and profiles can be derived from digital elevation models of the area ofinterest using GIS techniques. Slope is the most substantial parameter influencing landslide
development. On a slope of uniform isotropic material, increased slope correlates positively withincreased likelihood of failure (Chauhan et al. 2010). In order to assess the contribution of various
slope gradients to the development of landslides, it is necessary to know the spatial distribution of
the slope categories, which can be obtained from a DEM (Dai & Lee 2002). The other importantgeomorphologic parameter is relative relief. Landslides generally occur in high relative relief areas.
The relief of the area is defined as the difference between maximum and minimum elevation values
within the area. This parameter can be computed using DEM (Chauhan et al. 2010). Aspect is one of
the most important parameters as it directly and indirectly influences the area’s susceptibility to slope
failure.
Landcover
While landcover is not strictly “static” it is regarded to be relatively stable over the course of few
months. It does not change daily just like rainfall and vegetation. Landcover can be defined as theobserved physical and biological cover on the earth's surface. Glade (2002) concurs that vegetation
cover is an important factor influencing the rate of surface runoff, which enhance chances oflandslide occurrence. For instance, barren slopes are more likely to have landslide occurrence. In
contrast vegetative areas tend to reduce the action of rainfall thereby preventing the erosion due to188the anchorage provided by the tree roots (Gray & Leiser 1982; Greenway 1987) In general, sparsely
vegetated areas are associated with higher runoff during rainy seasons when compared to densely
vegetated areas. Similarly, the type of vegetation would have an impact on slope stability (i.e.,
forested areas are expected to be more stable than grassland). Different soil types have different
properties such as grain size, porosity, transmissivity and hydraulic conductivity, therefore different
soils have diverse influence on susceptibility to slope failure. An increase in absorbed moisture is amajor factor in the decrease in strength of cohesive soils (Zhou 2006); Dahal et al. (2007) have alsoemphasized the importance of soil type as a parameter when modeling landslide susceptibility maps
using the weight of evidence method. It has also been noticed that soil depth between 0.5-2 meters
have maximum susceptibility to landslide (Dahal et al. 2007). It is therefore important to input soil
depth as a static parameter when modeling a landslide susceptibility map.
Anthropogenic influences
Human-induced changes can affect an area’s susceptibility to landslides and must be understoodwhen assessing landslide potential of an area. Examples of such activities are road-cuts,
deforestation, mining artificial vibrations, and cutting of slope toe during construction. One of thecontrolling factors for the stability of slopes is road construction activity (Dahal et al. 2007). Road
cuts in mountainous areas can make the area susceptible to slope instability. The increase in moisture
content in the soil or changing the form of a slope can increase the area’s susceptibility to landslide(Garland & Olivier 1993). Development activities such as cutting and filling along roads and theremoval of forest vegetation are also capable of greatly altering slope form and ground waterconditions and therefore increasing the susceptibility to landslide occurrence (Swanson & Dyrness1975). These altered conditions may significantly increase the degree of landslide hazard present(Sidle, Pearce & O’Loughlin 1985). Trees act as natural anchors during rainy seasons, and therefore
reduce the effect of rainfall on erosion (Gray & Leiser 1982). Deforestations therefore can cause anarea to be more susceptible to slope failure, during rainy seasons.
Variable factors
Vegetation
As mentioned previously, the vegetation in an area has a significant impact on slope instability andvarious studies have emphasized the significance of vegetation on slope failure (Gray & Leiser 1982;
Greenway 1987; Styczen & Morgan 1995). However, it is not only the vegetation type that governs
landslide susceptibility, but also the health and productivity of vegetation at a specific time. The
effect of vegetation on slope stability appears to be complex in that, depending on local conditions ofsoil depth, soil type, slope and vegetation, a vegetation cover in some ways definitely promotes
189stability and in other ways it may not. In a review of behavior of vegetation on slope stability,(Prandini et al. 1977) makes the following points regarding the beneficial effects of forest cover: as awhole forest cover reduces the action of climatic agents on natural mass, in a manner favorable toslope stability by: (1) Intercepting and protecting the mass from the action of sunshine, winds andrains, (2) Retaining a considerable amount of rain water by wetting the large surface made up ofleaves, branches, and trunks and eliminating the water as a vapor, (3) Eliminating, as a vapors, alarge amount of water from the ground by means of evapotranspiration, and (4)Vegetal debris on theforest floor immobilizes a large amount of water and cuts down on runoff and erosion. When
identifying conditions leading up to landslide events, the identification of the vegetative conditions
prior to landslide occurrence can be performed. In this regard, landslides may occur preferentially inareas with little vegetation or in areas where vegetation is stressed due to drought or disease.
Triggering mechanisms
RainfallRainfall is a trigger for several landslides around the globe (Iverson 2000; Cardinali et al 2005).
Water is recognized to be a factor almost important as gravity in slope instability (Varnes 1984).
Landslides triggered by rainfall are caused by the buildup of water pressure into the ground (Cambell
1975; Wilson 1989). Iverson (2000) has also linked slope failure and landslide motion to
groundwater pressure heads that change in response to rainfall. (van Schlkwyk & Thomas 1991)
have argued that prolonged precipitation events associated with high intensity rainfall are often thetrigger for landslides. High intensity and short rainfall duration can trigger mostly shallow landslides
and debris flows in relatively high permeability soils (Corominas & Moya 1999; Corominas 2000).
Whereas long rainfall periods characterized by low to moderate average rainfall intensity can initiate
shallow and deep-seated landslides in low permeability soils and rocks (Cardinali et al 2005).
Analytical Hierarchy Process (AHP)
The Analytic Hierarchy Process (AHP) is due to Saaty (1980) and quite often is referred to, as the
Saaty method. The AHP model, developed by Saaty in the early 1970s, is a kind of multi-criteria
decision-making (MCDM) process that can be used to examine complicated technological,
economic, and social-political matters (Saaty 1977; Saaty and Vargas 1991). The process involves
pair-wise comparisons of decision variables (e.g., objectives, alternatives).

In

the

constructionof a pair-wise comparison matrix, each factor is rated against every other factor by assigning a
relative dominance value between 1 and 9 to the intersecting cell (Dai et al. 2001). In addition, themeasure of inconsistency can be used to successively improve the consistency of judgments. The
consistency index (CI) of a matrix of comparisons is190

CL = (λ_max-n)/(n-1)
where km axis the largest or principal eigenvalue of the matrix, and n is the order of the matrix(Saaty 1977)
The consistency ratio (CR) is obtained by comparing the CI with the appropriate value in a set of
numbers in which each is an average random CI derived from a sample of randomly generated
reciprocal matrices using the scale 1/9,1/8...,1....,8,9:
CR = CI/RI
where RI is the average of the resulting CI depending on order of the matrix given by (Saaty 1977).
A CR above 0.1 indicates an inconsistent treatment of particular factor ratings, necessitating
revisionofthe judgments in the matrix (Saaty 1977; Dai et al. 2001). In general, the AHP

calculates the relative weights for each determinant based on a questionnaire survey; these weighs
are used to generate a pair-wise comparison matrix. However, we used the method of (Wu2002), who reported that the weights for the spatial variables in the multi-criteria evaluation (MCE)were difficult to determine accurately and comprehensively. These issues arise from the process ofobtaining weights in the MCE. As a solution, (Wu 2002) proposed the use of a LR procedure.

Methodology
Study Area
The Rangamati district an area of 6116.19 km2, is situated in the southeast of Chittagong Hill Tracts(CHT) (Rangamati Hill District Council 2011). The study area Baghaichhari is one of the Upazilas ofRangamati district. Baghaichhari Upazila (Rangamati district) area 1868.80 sq km, located in
between 23°04' and 23°44' north latitudes and in between 92°05' and 92°24' east longitudes. It isbounded by Tripura and Mizoram (India) on the north, langadu and barkal upazilas on the south,
Mizoram (India) on the east and dighinala upazila on the west. Baghaichhari Upazila is divided into
Baghaichhari Municipality and eight union parishads: Amtali, Baghaichhari, Bongoltali,
Khedarmara, Marisha, Rupokari, Sajek, and Sharoyatali. The union parishads are subdivided into 19
mauzas and 303 villages. (District Statistics 2011)
The study area consists of 78519 populations; male 42056, female 36463; Muslim 18310, Hindu
4316, Buddhist 1142, Christian 54717, others 34 and a population density of 44 per km2 (BBS2011). The biggest union of Baghaichhari upazila is Sajek. Sajek Valley is an emerging tourist spotin Bangladesh situated among the hills of the Kasalong range of mountains in Sajek union,
Baghaichhari Upazila in Rangamati District. The valley is 1,476 feet (450 m) above sea level.
The average elevation of the study area is 70.39 m above sea level. The study area experiences atropical monsoon climate as the country is in a tropical monsoon region with warm temperatures,191

intense rainfall, frequently excessive humidity, and noticeable wide seasonal variations (Abedin et al.
2020). The average annual rainfall is around 2539.19 mm with humidity level 71.6%, and theaverage annual temperature ranges from a highest of 36.5°C to a lowest of 12.5°C (BBS 2011;
Abedin et al. 2020). April is the warmest month in the Rangamati district with an average maximum
temperature of 33°C and January is the coldest month with an average maximum the temperature of
25.7°C (Khatun et al. 2016). Due to the tropical monsoon climate, 80% of the rainfall in the study
areas occurs during the monsoon (June-October) season (BBS 2011). This Upazila is formed of
tertiary and quaternary sediments. Seven geological formations: Dihing and Dupi Tila undivided,
Bokabil, Bhuban, Tipam Sandstone, Valley Alluvium and Colluvium, Dihing, and Girujan, clay areseen in the study area (Haque et al. 2018). Among these geological formations, areas under Bhuban
and Bokabil formations are especially prone to landslides due to the alternating layers of sandstoneand shale (Haque et al. 2018). Moreover, anthropogenic activities and the resultant excessive rainfall
during the monsoon season exacerbate the situation (Islam 2008) In Bangladesh, land use is usually
influenced by climate, physiography, and levels of the land (Brammer 2002; Abedin et al. 2020). The
study area is dominated by vegetation cover followed by water bodies and bare land. Bare lands
being exposed for a long time are susceptible to slope failure because of erosion and weathering
process (Haque et al. 2018). As the study areas are frequently rugged, they can neither be used as
built-up nor agricultural areas. However, all the built-up cases, hill forest and vegetation coverprimarily changed to bare land and then to a built-up area (Ahmed and Dewan 2017; Abedin et al.
2020). Analysis of the land use and land cover changes in the study area over time exhibited anincrease in vegetation, bare land, and built-up area and a substantial decrease in water bodies(Abedin et al. 2020).
Approach of the Study
This chapter provides insight on how the research has been conducted. This chapter deals withdiscussions on research methodology, sampling techniques and data collection process. It also
addresses the research investigation by highlighting the discussion of semi-structured interviews. The
research has been conducted following a methodology with conformity to the scopes and objective of
the study. The methodology is described in detail as below in figure 2.

192

Figure 1: Study area map (Baghaichhari upazila)
Define Research Problem

Review Literature

Consultancy With Expert

Design Research

DirectObservation

Reconnaissance Visit

Expert Opinion
SurveyStudy Area Selection

Primary Data Collection

Data Collection

Data Analysis

Interpret & Report

Figure 2: Approach of the study

193

Literature andjournal Review
Newspaper sandonline articles

Secondary Data Collection

Data Acquisition
Primary Data
The primary data source of this research is the field survey conducted on landslide prone area of
Baghaichhari upazila. Field study was conducted in order to compile digital and hardcopy including
geospatial data, reports and all kind of background information relevant for the analysis, such as
occurrence dates of rainfall events, as well as landslides.
Secondary data
The remote sensing techniques provide a great possibility to analyze the environmental processes inlocal or global scale. Landsat images with their 30 m resolution are suitable among others for land
cover mapping and change monitoring.
Secondary data was collected for interpreting and analyzing the information to produce a landslide
susceptibility map. USGS, ALOS are the sources of Landsat image. Also, different necessary data
was collected from another sources. They are – BUET ZIDPAS- 2016; CDMP-11,2012; NASADATABASE,2019
Table 21: Data collection source
Information
DEM

Land-use map

Geology map

Geomorphology
map
Existing
Landslideinventories
Hydrology
Road network
Other Data

Baghaichhari upazila
In the middle filled with SRTM 30m pixel size. It was used
to get the layers of Slope Angle, Slope
Aspect, Elevation, and Flow Accumulation.
Shapefile (polygons). With 18 classes. Coverthe whole Upazila. It helps to include quarries, roads
and buildings.
Shapefile (polygons) with 5 units. No
structural information (faults, folds, etc.).
Cover the whole upazila. Re-digitized
DEM Interpretation was made. With a total of 6 units
Jpg image without
attributes. It was digitized as a point basedlandslide inventory, placing points on the
scarps of the landslides.
Shapefile (lines) with rivers and streams,
cover the whole upazila gathered from DEM using SWAT model.
Shapefile (lines), cover the whole upazila
Shapefile with road-cuts,
Shapefile with Cliffs. Interpreted from the
DEM.

Satellite images were downloaded for analyzing the data.
194

Table 22: Image collection source
Image
Year
Satellite
SRTM
SRTM

2020
2020

Path/Row Acquisition Land
Date
Cloud
Cover
136/442020/02/18 0
136/442020/02/24 0

Scene
Cloud
Cover
0
0

GroundResolution
30*30
30*30

SourceALOS

Data-base preparation
All the information collected, was checked, selected, and modified in order to have it the mostcomplete possible, in the same GIS format, and in the same coordinate system. This process included
digitizing landslide inventories, modifying the existing maps (land-cover, geology, soils) in order toimprove them, and generation of new maps that could be useful on the analysis as flow
accumulation, slope angle, slope aspect, elevation, stream network, stream network distance, road
network distance, etc. Finally, all DEM derivative maps as well as distance maps, had to be classified
before using them in the model, to accomplish this, for Baghaichhari upazila using the point-basedlandslide inventories, it was analyzed how many landslides were per value of each factor map
The landslide susceptibility map quality depends on the quality and selection of the landslide
inventory and predisposing factors (Reichenbach, P.; Rossi, M.; Malamud, B.D. 2018). The location,
types, size, and causes of landslides are stored in a landslide inventory (Guzzetti, F). The selection ofpredisposing factors depends on the scope and availability of the data and the timeline and cost of the
project (Guzzetti, F 2002; Cardinali, M.; Galli, M. 2006; Rossi, M.; Valigi, D. 2009; Wang, Q.
2017). Method and mapped in full scale to determine most vulnerable area in the study area of thisresearch.
Landslide inventory
The landslide inventory was prepared using the combination of Google Earth and field mapping.
(Köppen, W.P. 2020; Guzzetti, F 2009) This inventory includes 24 major landslide locations from2000 to 2020 that were mapped in Google Earth and also collected information from field work.
Field mapping was used to map landslides that occurred inaccessible areas including near the roads
and settlements. In contrast, Google Earth mapping was used to map landslides in remote areas,
including forests.
Elevation
Elevation controls temperature and vegetation. Generally, the occurrences of landslides increase with
the increase of elevation before reaching a threshold elevation, where the landslide probability
reduces due to rock and soil characteristics and other geotechnical parameters (Guzzetti, F.; Guzzetti,
F 2009). For calculating elevation two Shuttle Radar Topography Mission (SRTM) images were195

downloaded from ALOS. Next step is to remove the background from those images. Then put thoseimages on ArcGIS and use mosaic to new raster tool. By reclassing on 5 classes make the elevation
information suitable for AHP calculation.

Figure 3: Methodology flowchart
Rainfall
The annual rainfall data from Chittagong Regional Stations of the Bangladesh Meteorological
Department (BMD) were used to analyzing rainfall data. Last 30 years (1989-2019) data were used
for analyzing annual rainfall data. Then put those data on ArcGIS and use idw tool to create a rainfall
prediction surface map. Annual average rainfall data was calculated by using pivot table in excel.

196

SlopeThe slope is another important predisposing factor. Landslides do not occur on a gentle slope, as theshear stress is low (Yilmaz, I. 2010). The steeper the slope is, the higher the probability of landslides(Chen, W. 2017) The slope raster was derived from the ASTER DEM in ArcGIS. By using special
analysis tools on ArcGIS create a slope map. The slope raster was then divided into five classes,<10⁰, 10-20⁰, 20-30⁰, 40-50⁰, >50⁰
Aspect
The aspect is the orientation of the slope (Calista, M.; Miccadei, E. 2019, Chen, W. 2017). The
disintegration and deterioration of materials on a slope depend on the aspect because it affects
rainfall and wind (Roy, J. 2019). The aspect raster was derived in ArcGIS and used special analysis
tools then was divide into nine classes.
Distance to roads
LGED road layers was taken for analyzing road data. Then road layer intersects by study area using
ArcGIS. Distance based factors were derived in ArcGIS and Create ring buffer for <10m, 10m, 20m,
50m. By using polygon to raster tool make the road lines.
Distance to stream
Distance based factors were derived in ArcGIS and SWAT model was used to create stream distance.
Then this study area was extracted by mask. Create ring buffer for <10m, 10m, 20m, 50m. By using
polygon to raster tool make the stream lines.
Lithology
Lithology is one of the most important predisposing factors of landslides ((Calista, M.; Miccadei, E.
2019, Chen, W. 2017). The slope stability depends on the strength of the rocks, which depends on
the local lithology. Bangladesh geological data downloaded from Participatory Agency Service
Agreement (PASA) under the Department of Energy, USA. Then use polygon to raster tool for
creating lithological map.
NDVI
The Normalized Difference Vegetation Index (NDVI) is an index widely used to represent
vegetation and plant health (Chen, W. 2019; Islam, M.A.; 2020; Huete, K.A.; 2002). Vegetation
coverage protects land from erosion and landslides because the root system binds to soil and protects
soil from wasting during rainfall (Chen, W. 2019) The NDVI was derived from Landsat 8 level 2
imagery of 11 March 2020 based on NDVI=(IR-R)/(IR+R)=((Band5-Band4))/((……………………………… 1
197

Band

5+Band4)),

where IR is the infrared band and R is the red band. The NDVI ranges from -1 to 1; moderate to high
NDVI (0.2–0.8) indicates vegetation and forests, a negative value usually indicates water bodies, anda low positive value (0.2 and below) represents bare land and urban areas (Chen, W. 2019; Pradhan
B 2014)
Analytic Hierarchy Process (AHP) method
Analytic Hierarchy Process (AHP) is used for producing the landslide susceptibility map. AHP
gained wide application in site selection, suitability analysis, regional planning, and landslide
susceptibility analysis (Ayalew et al., 2005)
For calculating in AHP, it is important to identify the static, variable & triggering factors oflandslides. Elevation, Rainfall, Slope, Aspect, Distance to road, Distance to stream, Lithology,
NDVI, Land cover are the parameters these were calculated for producing landslide susceptibility
map. In pair-wise comparison matrix, it’s compulsory to choose whether elevation is very much
more important, rather more important, and as important, and so on down to very much lessimportant, than rainfall. Same judgement goes for every parameter. Each of these judgments is
assigned a number on a scale. One common scale (adapted from Saaty) is the one shown in Table 4.
Table 23: Common scale of AHP method
Intensityof importance
1
3
5
7
9
2, 4, 6, 8
Reciprocals ofabove numbers

Definition

Explanation

Equal importanceSomewhat more
importantMuch
more
important
Very much more
importantAbsolutely more
important
Intermediate
valuesTwo factors contribute equally to the objective.
Experience and judgment slightly favor one over theother.
Experience and judgment strongly favor one over theother.
Experience and judgment very strongly favor one over
the other. Its importance is demonstrated in practice.
The evidence favoring one over the other is of thehighest possible validity.
When compromise is neededtheFor inverse comparison

List of random index value –198

Table 24: Random index value
N

2

3

4

5

6

7

8

9

RI (n)

0

0.58

0.90

1.12

1.24

1.32

1.41

1.45

Step 1Pair-wise comparison matrix – This is the first step for analytical hierarchy process. Pairwise
comparison generally is any process of comparing entities in pairs to judge which of each entity ispreferred, or has a greater amount of some quantitative property, or whether or not the two entities
are identical. In this matrix, it is compared whether rainfall has more importance than elevation inlandslide occurrence. Same comparison goes for every factors.
AHP pairwise comparison, Construct a pairwise comparison matrix.
..
..
A = ..
..
..
. . , …………………………. 2..
where n denotes the number of parameters and aij refers to the comparison of parameter i toparameter j with respect to each criterion. The nine-point scale, shown in Table 4, can be used todecide on which element is more important and by how much.

Step 2
AHP synthetization - Divide each entry (aij) in each column of matrix A by its column total. The
matrix now becomes a normalized pairwise comparison matrix,
.. ∑
∑
∑
∈

∈

A' = ∑ ∈
..
∑∈

∈

..
..
..

∑∈

..

∑∈

∑∈

..

, ……………………………………… 3∑∈

where R denotes the set of landslide susceptibility mapping requirements, that is, R ¼ {1; 2; . . .; n}.

Step 3
Compute the average of the entries in each row of matrix A' to yield column vector,
∑∈

⋮

C=

⋮
∑∈

Where
matrix.

… ∑
∈

∑∈

∑∈

,……..……………………………….. 4⋯ ∑∈denotes the relationship weightings between critical weight and normalize pair-wise
Step 4

AHP consistency verification Multiply each entry in column i of matrix A by c_1^1 k. Then, divide
the summation of values in row i by c_1^(1 ) k to yield another column vector,
199

⋯

C' =

⋮

=

⋮

,…….…………………………….. 5⋯

where C' refers to a weighted sum vector.
Step 5Compute the averages of values in vector to C' yield the maximum eigenvalue of matrix A,λmax =
Step 6∑∈, ..………………………………. 6

Compute the consistency index,
CI =, ………………………………….7

Step 7
Compute the consistency ratio,
CR =, ………………………………….8
where RI(n) is a random index of which the value is dependent on the value of n, shown in
Table 5. If CR is greater than 0.10, then go to step 1 again.
Result and Discussion
Landslide inventory
It is very important to determine the location and area of the landslide correctly when preparing thelandslide susceptibility maps. Landslide inventory maps can be prepared in different formats
according to the size of the area, the data obtained from the land, the quality of this data and the scaleof the study. These maps can be produced by gathering the information related with the landslides orby analyzing the data from remote sensing.
In this study, primary field survey was held to collect information about landslide of thisBaghaichhari upazila and also obtained information from Baghaichhari upazila office. Secondary
data is collected from ALOS as SRTM image (30*30) m
Landslide inventory helps to identify the previous landslide location which will help to prepare thelandslide susceptibility map. By this inventory map it can be known that which area is vulnerable tolandslide disasters by showing the historical landslides. In the inventory map of this study area
identifies 24 major landslides of Baghaichhari upazila. In which 20 landslides were occurred in Sajek
union. Sajek is the biggest union of Baghaichhari upazila and contains highest level of mountains.
Most of the major landslides determined had occurred as a result of the heavy rainfalls. Some of thelandslides occurring in previous years were not determined because dense plant cover had grownover the landslide areas in the short period of 1–2 years, and the landslide area had begun to beconverted to agricultural use.
200

Figure 4: Inventory map (Baghaichhari Upazila)
Elevation
A change in elevation can bring changes in geomorphology, vegetation, and rate of erosion in an area
and thus alters the landslide susceptibility (Chen, W. 2017). We derived elevation from SRTM,ALOS and divided them into six classes. They are- 0-84m, 85-140m, 140-200m, 200-300m, 300450m, 450-730m. Landslide susceptibility for each elevation is analyzed (figure-6) this analysis
shows that the higher the elevation (m), more susceptible for landslide. The highest elevation 450730m is very high susceptible for landslide hazard.

201

Figure 5: Elevation & Susceptibility

Figure 6: Elevation map (Baghaichhari Upazila)

202

SlopeThe main parameter of the slope stability analysis is the slope angle (Lee and Min, 2001). Because
the slope angle is directly related to the landslides, it is frequently used in preparing landslide
susceptibility maps (Clerici et al., 2002; Saha et al., 2002; Cevik and Topal, 2003; Ercanoglu et al.,
2004; Lee et al., 2004a; Lee, 2005; Yalcin, 2005). The slope map of the study area was divided into
six slope categories. ArcView 3.2 analysis was performed to discover in which slope group has thehigh susceptibility to landslide. The area percentage in each slope group class is presented in (figure7). The susceptibility rate for each slope class is presented in (figure-8) This figure indicates that thehigh susceptibility for landslides at a slope angle of less than 40°.

Figure 7:Slope area

Figure 8: Slope and Susceptibility

203

Figure 9: Slope (Baghaichhari Upazila)
Lithology
The main source of data related to the geomorphology of an area of land is determined by thelithologic properties of that land (Dai et al., 2001). The landslide phenomenon, a part of thegeomorphologic studies and research, is related to the lithological properties of the material of theland. In the study area, the rock outcrops had decomposed at different degrees from the effects ofphysical and chemical factors. The degree of lithological types was determined by using the ArcGIS
10.6 software. Firstly, this analysis shows the lithological types and areas covered by this lithologicaltype (figure - 12). As a result of the analysis performed according to the lithological types andlandslide inventory of different units, it was verified that approximately 80% of the landslides
occurred in Boka Bill formation and 20% of the landslide occurred in Bhuban formation (figure-10).
This analysis also shows that now some areas of Boka bill formation has the very high susceptibility(`15.72%) and other some areas of this formation have high susceptibility to landslide (30.50%)(table-6) According to the landslide inventory, maximum major landslides were occurred in the Boka
Bill formation. And also, this study shows that some areas which contains Boka Bill formation still
has the highest susceptibility of landslide in Baghaichhari upazila.

204

Table 7: Landslide susceptibility for different lithological types

lithological
Types
Dihing and Dupi
Tila Formation
Boka Bil
Formation
BhubanFormation
Tipam Sandstone
Valley Alluvium
and Colluvium
Lake

Very High
Susceptibility
0.00

High
Susceptibility
0.00

Moderate
Susceptibility
0.00

Low
Susceptibility
0.00

Very Low
Susceptibility
0.00

Inventory

15.72

35.97

37.37

10.9

0.00

79.17

10.18

28.48

30.61

30.44

0.30

20.83

0.06
0.00

1.08
0.00

3.19
0.00

81.18
9.43

14.49
90.57

0.00
0.00

0.00

0.00

0.00

0.00

100.000.00

Figure 10: Lithological types

Figure 11: Lithological types and inventory
205

0.00

Figure 12: Lithology map (Baghaichhari upazila)
Rainfall
The average precipitation is high in the study area compared to the normal range of precipitation.
Since the soil movements are influenced by numerous parameters, however, scientific and technicalstudies (e.g., (Xie, Esaki et al. 2004, Chang, Chiang et al. 2008, Pradhan and Lee 2010) suggest thatrainfall is one of the important triggering factors that causes landslides. The effect of rainfall
infiltration on slope could result in the changing soil suction and positive pore pressure, as well as
raise the soil’s unit weight, reducing anti-shear strength of rock and soil (Wilson and Dietrich 1987,
Premchitt, Brand et al. 1994, Iverson 2000, Hengxing, Chenghu et al. 2003). The main causes ofsuch phenomenon seem to be prolonged rainfall events of medium intensity or extreme intensity. 40
years (1979-2019) Rainfall data from Bangladesh Meteorological department (BMD) show that thelowest average annual rainfall (About 5870 mm/yr) in the study area is greater than the average
rainfall of the country (1030 mm/yr). Moreover, frequency of rainfall in the past decades evident the
influence of landslide by rainfall. However, rainfall distribution within the study area is unique.
Almost 60% area of the study is poured with more than 2500 mm rainfall annually, which seems aleading factor of landslide. Most of the landslide movements were reported in the study area during
monsoon rainfall. The rainfall in the Sajek union of the study area has higher precipitation than the206

rest of the areas. GIS data analysis indicates that the highest annual average rainfall about 7909
mm/yr and the lowest annual average rainfall about 5870 mm/yr. This rainfall rate is far higher than
the average rainfall in Bangladesh.

Figure 13: Rainfall rate map (Baghaichhari upazila)
AspectLike slope, aspect is one of the important factors in preparing landslide susceptibility maps (Guzzetti
et al., 1999; Nagarajan et al., 2000; Saha et al., 2002; Cevik and Topal, 2003; Ercanoglu et al., 2004;
Lee et al., 2004a; Lee, 2005). Aspect related parameters such as exposure to sunlight, drying winds,
rainfall (degree of saturation), and discontinuities may control the occurrence of landslides (Dai etal., 2001; Cevik and Topal, 2003; Suzen and Doyuran, 2004; Komac, 2006). In this study, the aspect
map of the study area was produced to show the relationship between aspect and landslide. Aspect
regions are classified according to the aspect class as flat (−1°), north (0°–22.5°), northeast (22.5°–
67.5°), east (67.5°–112.5°), southeast (112.5°–157.5°), south (157.5°–202.5°), southwest (202.5°–
247.5°), west (247.5°–337.5°) Some analyses were performed using aspect and landslide inventory
maps to determine the distribution of landslides, according to the aspect class, and the percentage oflandslide susceptibilities in aspect class. (table - 7) This analysis also shows the area of each aspect
class in the study area. (figure-14)

207

Figure 14: Aspect Area
Table 8: Aspect and percentage of susceptibility

TYPE

Very High
Susceptibility

High
Moderate
Low
Very Low
Susceptibility Susceptibility Susceptibility Susceptibility

Flat

0.07

0.82

8.94

15.53

74.64

North

9.26

27.50

36.46

20.83

5.95

Northeast

9.15

29.71

32.83

22.66

5.66

East

8.46

32.71

29.94

23.79

5.09

Southeast

7.59

31.86

29.59

25.34

5.62

South

7.17

30.93

31.72

24.78

5.41

Southwest 10.38

32.52

32.41

20.21

4.48

West

12.56

32.14

33.90

17.18

4.22

Northwest 12.01

29.54

35.17

18.00

5.28

208

Figure 15: Susceptibility and Aspect

Figure 16: Aspect map (Baghaichhari Upazila)
Land coverFrom the analysis of GIS (Fig.-20), it is revealed that maximum part of the upazila is covered bydense hill forest. Only 2.02% of the total area is covered by water. Here, population density is lower
than other upazilas of Rangamati. Though this upazila has less population but because of densely hill
forest this upazila is highly vulnerable to landslide hazards. Baghaichhari upazila has 1.94%
settlement, 36.82% vegetation (garden, agricultural land, shrub field etc.) and rest of the area 59.22%
209

hilly forest area (figure- 18) landslide inventory shows that among major 24 landslides 17 landslides
occurred in hilly forest area and 5 landslides occurred in agricultural land. (figure - 17) From theanalysis it also showed that the percentage of landslide susceptibilities of each land use and land
cover factors. (table-8)

Figure 17: Landcover and Inventory

Figure 18: Landcover area
Table 9: Landcover and susceptibility

Landcover type
Very High Susceptibility
High Susceptibility
Moderate Susceptibility
Low Susceptibility
Very Low Susceptibility
Inventory

Waterbody
0.67
4.91
20.78
21.68
51.96
0.00

Settlement
1.85
5.44
45.11
22.84
24.76
4.17

210

Vegetation
7.71
25.91
34.98
21.57
9.82
25.00

Forest land
11.27
35.73
30.64
21.43
0.93
70.83

Figure 19: Landcover and Susceptibility

Figure 20: Landcover map (Baghaichhari Upazila)

211Normalized Difference Vegetation Index (NDVI)NDVI indicates the growth of vegetation and biomass of an area (Roy, J.; Saha, S. 2019) Generally,
the probability of the occurrence of the landslide on the naturally vegetated surface is lower than thebare lands (Chen, W. 2017) The NDVI was derived from Landsat 8 level 2 imagery of 11 March
2020 based on Equation 1:
NDVI = ((IR-R))/((IR+R))= ((Band 5-Band 4))/((Band 5+Band 4))
where IR is the infrared band and R is the red band. The NDVI ranges from -1 to 1; moderate to high
NDVI (0.2–0.8) indicates vegetation and forests, a negative value usually indicates water bodies, anda low positive value (0.2 and below) represents bare land and urban areas (Chen, W. 2019; Pradhan
B 2014). This study area’s NDVI ranges from {(-0.05) – 0.46} which indicates that this area consistsof vegetation, forests, settlements and also water bodies.

Figure 21: NDVI map (Baghaichhari Upazila)
Distance to stream
An important parameter that controls the stability of a slope is the saturation degree of the materialon the slope. The closeness of the slope to drainage structures is another important factor in terms ofstability. Streams may adversely affect stability by eroding the slopes or by saturating the lower partof material until resulting in water level increases (Gokceoglu and Aksoy, 1996; Dai et al., 2001;
Saha et al., 2002; Cevik and Topal, 2003; Yalcin, 2005). A thorough field investigation should be
carried out to determine the effects of streams on the slope. ArcGIS 10.6 is used for analyzing the212stream data. Four different buffer areas were created within the study area to determine the degree towhich the streams affected the slopes. Four buffer areas are <10m, 10-20m, 20-50m, >50m (Figure
22)

Figure 22:Distance to stream map (Baghaichhari Upazila)
Distance to roads
Similar to the effect of the distance to streams, landslides may occur on the road and on the side of
the slopes affected by roads (Pachauri and Pant, 1992; Pachauri et al., 1998; Ayalew and Yamagishi,
2005; Yalcin, 2005). Distance from the road networks is one of the most critical factors. A road
constructed beside slopes causes a decrease in the load on both the topography and on the heel ofslope. The undercutting of slopes during road construction and the vibration created by vehicles
damages the slope stability (Kanwal, S.; Atif, S.; Shafiq, M. 2017). As a result of an increase instress on the back of the slope because of changes in topography and decrease of load some tension
cracks may be created. On the slope of the hill that is balanced before the road is constructed,
instability may be observed because of negative effects such as water ingress. In fact, during the fieldstudies, landslides resulting from road construction work were detected. We used the Euclidean
distance tool in ArcGIS 10.6 to derive the distance of landslides from the targeted road feature. Fourdifferent buffer areas were created on the path of the road to determine the effect of the road on the213stability of slope. The landslide percentage distribution was determined according to the degrees of
distance to the road achieved by comparing the map of the distance to the road and the landslide
inventory. Four different road buffer areas are <10m, 10-20m, 20-50m, <50m (figure)

Figure 23:Distance to road map (Baghaichhari Upazila)
Analytical Hierarchy Process (AHP)
The Analytical Hierarchy Process is a theory of measurement for dealing with quantifiable andintangible criteria that has been applied to numerous areas, such as decision theory and conflict
resolution (Vargas, 1990). AHP is a multi-objective, multi-criteria decision-making approach whichenables the user to arrive at a scale of preference drawn from a set of alternatives. AHP gained wide
application in site selection, suitability analysis, regional planning, and landslide susceptibility
analysis (Ayalew et al., 2005). To apply this approach, it is necessary to break a complex
unstructured problem down into its component factors; arrange these factors in a hierarchic order;214

assign numerical values to subjective judgements on the relative importance of each factor; andsynthesize the judgements to determine the priorities to be assigned to these factors (Saaty andVargas, 2001). In the construction of a pair-wise comparison matrix, each factor is rated against
every other factor by assigning a relative dominant value between 1 and 9 to the intersecting cell(table 3) When the factor on the vertical axis is more important than the factor on the horizontal axis,
this value varies between 1 and 9. Conversely, the value varies between the reciprocals 1/2 and 1/9.
In these techniques, firstly, the effects of each parameter to the susceptibility of landslides relative toeach other were determined by dual evaluation in determining the preferences in the effects of theparameters to the landslide susceptibility maps. Normally, the determination of the values of theparameters relative to each other is a situation that depends on the choices of the decision-maker.
However, in this study, both the comparison of the parameters relative to each other and thedetermination of the decision alternatives, namely the effect values of the sub-criteria of theparameters (weight), were based on the comparison of landslide inventory maps constructed using
aerial photos with the other data layers. Consequently, the weight values were determined accurately
for the real land data in this study, spatial databases were used, obtained as a result of the field,
laboratory and office studies carried out to create landslide susceptibility maps. The analysis of data
layers converted to a raster data model was completed by determining their weights in terms of bothdata layers and sub-criteria, in consequence of the calculation carried out according to the AHP. As aresult of these analyses, the landslide susceptibility map was produced for the Ardesen region(susceptibility map). For all the models, where AHP was used, the CR (Consistency Ratio) was
calculated. The models with a CR greater than 0.1 were automatically rejected. With the AHP
method, the values of spatial factors weights were defined. Using a weighted linear sum procedure(Voogd, 1983) the acquired weights were used to calculate the landslide susceptibility models. In thismethod, elevation, slope, rainfall, lithology, NDVI and aspect are found to be important parameters
for the study area, whereas distance to road, distance to stream, land cover is low.

Pair-wise comparison matrix
From equation 2, the pair wise comparison matrix for analytical hierarchy process is calculated.
Table 25: Pair-wise comparison matrix
Types

ElevationElevation

1.00

Rainfall

0.50

RainfallSlope

Litholog
y

1.00
215

Distanc Distanc Lande
to e
to Cove
Road
Stream r

NDV
I

Aspec
t

0.50

2.00

1.00

Lithology 0.33

0.33

0.50

1.00

Distance
to Road

0.14

0.20

0.14

0.20

1.00

Distance
toStream

0.20

0.17

0.17

0.20

0.50

1.00

Land
Cover

0.17

0.17

0.20

0.14

3.00

0.50

1.00

NDVI

0.14

0.20

0.20

0.17

1.00

2.00

3.00

1.00

Aspect

0.17

0.14

0.17

0.25

5.00

2.00

2.00

2.00

Slope

1.00

Normalized pair-wise matrix
From equation 3, by AHP synthetization a normalized pair-wise matrix can be calculated.
Table 26: Normalized pair-wise matrix
Types

ElevationRainfallSlope

LithologyDistanc Distanc Lande
to e
to Cove
Road
Stream r

NDVI

AspectElevatio
n

0.32

0.32

0.41

0.30

0.20

0.17

0.19

0.25

0.23

Rainfall

0.16

0.16

0.10

0.30

0.14

0.20

0.19

0.18

0.27

Slope

0.16

0.32

0.21

0.20

0.20

0.20

0.15

0.18

0.23

Litholog
y

0.11

0.05

0.10

0.10

0.14

0.17

0.22

0.22

0.16

Distance
to Road

0.05

0.03

0.03

0.02

0.03

0.07

0.01

0.04

0.01

Distance
toStream

0.06

0.03

0.03

0.02

0.01

0.03

0.06

0.02

0.02

Land
Cover

0.05

0.03

0.04

0.01

0.09

0.02

0.03

0.01

0.02

NDVI

0.05

0.03

0.04

0.02

0.03

0.07

0.09

0.04

0.02

Aspect

0.05

0.02

0.03

0.03

0.14

0.07

0.06

0.07

0.04

216

Critical weight value
From equation 4, critical weight value for each parameter is calculated.
Table 27: Critical weight value
Parameters

ElevationRainfall

Slope

LithologyDistanc Distanc Land NDVI Aspecte
to e
to CoverRoad
StreamCriteria
Weights

0.266

0.190

0.207

0.140

0.031

0.032

0.033

0.042

0.058

AHP consistency verification
From using equation 5, weighted sum value for each parameter is calculated.
Table 28: AHP consistency verification
Parameters

ElevationRainfall

Slope

LithologyDistanc Distance Land
e
to to
Cover
Road
StreamNDVI Aspect

Weighted
Sum
Value

2.703

2.014

2.137

1.432

0.294

0.408

0.312

0.323

0.570

Maximum eigenvalue calculation
Now Compute the averages of values to yield the maximum eigenvalue of pairwise comparison
matrix. By using equation 6, λmax is calculated.
λmax = 9.9553
Consistency Index
Consistency Index (CI) is calculated from equation 7,
CI == 0.119412055
Here, λmax = 9.9553 and n refers to the number of parameters is used for this calculation. In thiscalculation we used 9 parameters such as, elevation, rainfall, slope, lithology, aspect, NDVI, distanceto stream, land cover and distance to road. So, in this calculation n = 9
Consistency Ratio
By using equation 8, consistency ratio is calculated.
CR == 0.082353141

217

Here, CI is calculated in previous step. There are 9 parameters so, n = 9 and RI refers to a random
index of which the value is dependent on the value of n, shown in Table 5. If CR is greater than
0.10, then go to step 1 again. The models with a CR greater than 0.1 were automatically rejected.
From this study, CR is calculated 0.0824. so, we can say that this model calculation went successful.
Areas susceptible for landslide hazard is calculated by using ArcGIS 10.6 software. (table-13) This
study shows that 9.55% area is very high susceptible, 30.88% high susceptible, 32.33% moderate
susceptible, 21.51% low susceptible and 5.72% very low susceptible to landslide hazard. (figure-24)
Landslide susceptibility map shows the susceptible areas. (figure-25) Sajek union is the highest
susceptible union. Landslide susceptibility map is created by performing AHP method on ArcGIS
10.6
Table 29: Susceptible areaType

Area_km

Very High Susceptibility

151.8777

High Susceptibility

490.9158

Moderate Susceptibility

513.8973

Low Susceptibility

341.9559

Very Low Susceptibility

90.8928

Figure 24: Landslide susceptibility
218

Figure 25:Llandslide susceptibility map (Baghaichhari Upazila)
Susceptibility analysis for roads and unions of Baghaichhari upazila
Hill track roads are steeper in inclination unlike roads in plain area. The roads are constructed
through hilly area cutting down the hillsides. As a result, the hill track roads have a common
property to have steep elevation and sharp bunking through such loose soiled hill paves. In this study
5 major vulnerable roads are taken under analysis. Five major roads are Baghai Hat - Sajek Road,
Dighinala (Bagaihat) - Marisha Road, Khagrachhari - Sajek Road, Old Longkar - New Juppai Road,
Sajeck Baghaihat FRB - Bhuachari via Machalong bazar. Among all the roads in Baghaichhari
upazila,

Old Longkar – New Juppai road and Baghai hat – Sajek road are most criticallyconstructed road with all the possible vulnerabilities. These roads have highest landslide
susceptibility than others. (table-15)
In recent time Sajek valley is being one of the most popular tourist spots and there is only one
possible road to go there. This road is fully constructed alongside with hilly areas. This road is
subjected to overload of tourist with their transports. As a result, the failure of this is highly
suspected.
219

Road length of vulnerable portion of these five major roads is calculated by using ArcGIS 10.6 (table
14). Landslide susceptibility roads are shown in the map which is also made by ArcGIS 10.6 (figure26). Susceptibility rate of these five roads is calculated and showed the percentage of very highsusceptibility to very low susceptibility. (table 15)
Table 14 - road length
Road Name

Road Length

Baghai Hat - Sajek Road

5.19346304

Dighinala(Bagaihat) - Marisha Road

19.38636941

Khagrachhari - Sajek Road

13.85070199

Old Longkar - New Juppai Road

7.418

Baghaihat FRB - Bhuachari via Machalong Bazar

29.08282667

Figure 6: Susceptible road
Roads

Very HighHigh

Moderate

Low

Very Low

Susceptibility Susceptibility Susceptibility susceptibility Susceptibility
Baghai hat
Sajek Road

– 16.89

55.97

27.13

0.00

0.00

Dighinala
6.89(Baghaihat)–
Marisha Road

66.04

10.23

10.79

6.05

Khagrachhari
Sajek Road

– 0.00

16.86

45.44

36.78

0.91

Old Longkar – 49.94
New Juppi Road

43.60

6.46

0.00

0.00

Baghai Hat FCB 8.04
– Bhuachari via
Machalong
Bazar

25.12

42.56

24.28

0.00

220

Figure 26: Susceptibility for 5 major roads
The study area, Baghaichhari Upazila is divided into Baghaichhari Municipality and eight unionparishads: Amtali, Baghaichhari, Bongoltali, Khedarmara, Marisha, Rupokari, Sajek, andSharoyatali. The union parishads are subdivided into 19 mauzas and 303 villages. (District Statistics
2011)
The susceptibility rate for these unions is calculated (table-16). This calculation shows that Sajek
union has the highest susceptibility of landslides. As Sajek is the biggest union in Baghaichhari
upazila and contains highest amount of hilly forest, maximum major landslide inventory is found in
the Sajek union. 11.76% areas of Sajek union are very high susceptible to landslide. Baghaichhari,
Bongotoli, Marisha and Sharoyatoli union also contains hilly areas and some major landslide
inventory was found there. Because of urbanization, hill cutting and heavy rainfall this unions (3.133.98) % areas pose very high susceptibility of landslide. but among all the unions, Sajek has thehighest susceptibility.

221

Conclusion
Understanding the processes that lead to land sliding and the effort for subsequent susceptibility
mapping provides fundamental knowledge about the evolution of landscapes, and lays the foundation
for hazard management and the creation of safety measures. Based on this idea, this study haspresented the results of a comprehensive research on landslide controlling parameters and landslide
susceptibility mapping in Baghaichhari upazila, Rangamati. In this region, landslides occurred
regularly after heavy rainfall. This is because; the topography and lithological materials areeminently suitable for the creation of landslides. It is known that the role of rainfall as a triggering
mechanism of landslides is strongly influenced by the landscape dynamic and geology. In
Baghaichhari upazila, rough landscapes and heavy rainfall are common, hill cutting often have thepotential for initiating slope failures. Susceptible stratigraphy and lithological too, contribute greatly
to the occurrence of landslides in the region. With conditions conducive to the development of slope
instability having existed in several areas, there was a demand to conduct landslide susceptibility
mapping. This study focused on the Baghaichhari upazila, the highest hill track region of country andalso contains highest level of rainfall. Sajek valley is situated in this upazila which is 1460m abovefrom sea level. This is mainly because of the problems inherited from landslide inventory maps andthe absence of universal guidelines to select causal factors. In this study, the landslide inventory map
was prepared in such a way that it included major landslides. Besides, it also considered thatlithology, slope gradient and land cover of materials as parameters important for susceptibility
mapping. There was an attempt to differentiate the concepts of landslide susceptibility mapping.
Various methods are available for landslide susceptibility mapping. In this study, AHP method isused for landslide susceptibility mapping. This study found that Sajek union is most susceptible tolandslides and old longkar to new juppi road is the most vulnerable road to landslides.
This study has several limitations. We did not include some critical soil-related factors like soil
permeability and soil moisture content due to data unavailability. Secondly, the geological map usedin this study had a very coarse resolution compared to other causal factors, and it was a very
simplified map as well. This study is performed with a few major landslide inventories. Futurestudies can include soil-related causal factors and assess whether they improve the accuracy of themaps. Also, future research could include as many as landslide inventories found there, this will help
to get improve result than this. The more representative inventory will help to show better landslide
susceptibility map of this study area. Moreover, this study recommends future research to evaluate
the applicability and performance of other advanced analytical hierarchy method for landslides
susceptibility mapping in the study area.

222

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