BNUS ANNUAL REPORT-2019

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

Mehedi Ahmed Ansary

April 2020
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CONTENTS
PART-I: CYCLONE FANI, 3RD MAY 20194
PART-II: CYCLONE EVACUATION ROUTE IDENTIFICATION USING GISFOR COASTAL COMMUNITIES IN BANGLADESH
24
PART-III: SOFT SOIL TREATMENT WITH PREFABRICATED VERTICALDRAIN (PVD) FOR THE CONSTRUCTION OF THE NEW RAILWAYEMBANKMENT OF KASHIANI-GOPALGANJ ROUTE IN BANGLADESH
44
PART-IV: INTER-ORGANIZATIONAL RESPONSE TO CYCLONE SIDR
(2007): A NETWORK BASED APPROACH
66
PART-V: EXPERIMENTAL INVESTIGATION ON BEHAVIOR OF
REINFORCED CONCRETE INTERIOR BEAM COLUMN JOINTSRETROFITTED WITH FIBER REINFORCED POLYMERS
91
PART-VI: A REVIEW ON FACTORS AFFECTING CYCLONEEVACUATION DECISION AND BEHAVIORAL RESPONSE
118
PART-VII: EXPERIMENTAL INVESTIGATION OF EMBANKMENT ON
SOFT SOIL UNDER CYCLIC LOADING: EFFECT OF INPUT
132
ACCELERATION
PART-VIII: DEVELOPMENT OF ZONATION FOR BANGLADESH BASED140
ON SOFT CLAY USING GIS
PART-IX: PREDICTING THE GROUND MOVEMENT ABOVE A TUNNELIN CHITTAGONG COASTAL AREA OF BANGLADESH UNDER SEISMICLOADING
147
PART-X: HUMAN BEHAVIOUR AND WORKPLACE SAFETY OF READYMADE GARMENT FACTORIES IN DHAKA DURING FIRE: A CURRENTSCENARIO
165
PART-XI: ASSESSMENT OF SEISMIC VULNERABILITY INDEX OFRAJUK AREA IN BANGLADESH USING MICROTREMOR
MEASUREMENTS
176
PART-XII: THREE DIMENSIONAL FINITE ELEMENT MODELING OF
CONTIGUOUS PILE WALL AS A SOIL RETAINING STRUCTURE ON
THE CONTEXT OF BANGLADESH
185
PART-XIII: BUILDING CODE IMPLEMENTATION AND ENFORCEMENTSTRATEGY FOR RAJUK197
TH
PART-XIV: CYCLONE BULBUL, 9 NOVEMBER, 20192042

PART-ICYCLONE FANI, 3RD MAY, 2019

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

Prepared By: Maruf Billah
Mehedi Ahmed Ansary3Introduction:
Bangladesh is one of the most disaster prone countries due to its geological position and climaticcondition. In every year the coastal part of Bangladesh has to face severe calamities like cyclone andstorm surge and cause various damage and causalities. Proper warning and management can reducethe impact but cannot be avoided completely. As Patuakhali is a coastal arever, it is more vulnerable
to climate change. Patuakhali is being affected almost every year by natural disasters like cyclones,
floods and storm surge due to climatic impact. In this year 2019, cyclone FANI affect the coastal partof Bangladesh basically Patuakhali and Barguna badly. The IMD began tracking a depression locatedwest of Sumatra on 26 April, classifying it as BOB 02. Later that day, the Joint Typhoon Warning
Center (JTWC) issued a Tropical Cyclone Formation Alert on the system (JTWC, 2019). Afterward,
the storm slowly coalesced while moving northward, and was upgraded to a deep depression at
00:00 UTC on 27 April (IMD report, 2019). At the same time, the JTWC began warning on thesystem, designating it 01B (JTWC, 2019). Six hours later, the IMD upgraded the system to a
cyclonic storm and gave it the name Fani. The India Meteorological Department tracked the storm
and issued numerous yellow warnings for much of the south-eastern portion of India when thecyclone started to intensify (CBS News, 2019; Wright, 2019). In preparation for the storm's impact,
the state government of Odisha evacuated over 1.2 million residents from vulnerable coastal areas
and moved them to higher ground and into cyclone shelters built a few miles inland. The authorities
deployed around a thousand emergency workers and 43,000 volunteers in this effort. It sent out 2.6
million text messages to warn of the storm in addition to using television, sirens and public-address
systems to communicate the message. About 7,000 kitchens were operated to feed evacuees in 9,000
storm shelters (Mohanty, 2019).
The Indian Navy readied naval ships and aircraft at Arakkonam and Visakhapatnam air-bases toprepare for the storm's aftermath and aid in reconnaissance, rescue and relief operations (The Hindu,
30 April 2019). The Odisha government staged "300 power boats, two helicopters and many chain
saws, to cut downed trees" for the purpose (Kumar, 2019).
Authorities in Bangladesh were ordered to open shelter areas as well in 19 coastal districts (Dhaka
Tribune, 1 May 2019). Bangladesh Navy deployed 32 naval ships to provide emergency relief andmedical assistance to the coastal areas in case of any emergencies (Dhaka Tribune. 4 May 2019).
More than 1.2 million people were evacuated in Bangladesh and moved to the cyclone shelters incoastal areas4

Villagers suffer as tidal water enters adjacent areas through the damage portions of dikes, polders
and embankments. Around 60 kilometers of flood control embankments in Patuakhali and Barguna
got badly damage due to tidal surge caused by impact of cyclone FANI. Moreover thousands ofpeople of different villages in those two districts have been suffering a lot as their dwelling, houses
and agriculture land remain inundate under saline water for several days due to the impact of recent
cyclone FANI. An emergency team was fielded by Bangladesh Network of Urban Safety (BNUS),
BUET to assess the huge polder damage, surge monitoring and assessment posed by Fani and toobserve and document the humanitarian conditions in the affected areas. The team focused on
Patuakhali and Barguna district as all the organizations involved in the survey team mainly workwithin Mirzaganj upazila, Lalua and Mohipur union, and hold special interest in the humanitarian
conditions and polders damage situation in the region. In this report several field visit, focus group
discussion and key informant interview has been conducted to evaluate the present scenario of
suffering due to cyclone attack and how people think on this matter of sufferings and management to
reduce it. Demographic representation of Mirjagonj upazila, Lalua union and Mohipur union.
The purpose of this report is to provide a preliminary understanding of the polder damages andhumanitarian conditions in the Fani affected areas of Khulna District. This report also assessed theneeds to support an effective and timely humanitarian response for the cyclone victims. There is alsoa preliminary analytical report added to this document which attempts to answer the question why
Cat-1 cyclone like Fani inflicted such a heavy damage in the coastal region of Patuakhali and
Barguna district.

Cyclone Fani Tracking Path:
Cyclone Fani made landfall near Puri, India, around 8 a.m. on Friday, after more than amillion people were evacuated. The fast moving storm lashed the coast with gusts of more than 120
m.p.h. Fani made landfall near north-west part of Bangladesh around 6.00 p.m. on Saturday. Four
category have been selected for cyclone Fani. Red color is the category 4, yellow is the category 1
and green is the tropical storm. Bangladesh faced tropical storm during cyclone Fani.

5

Source: (The New York Times, Asia Pacific; 03 may 2019)
Figure 1: Live tracking map: Cyclone Fani.

Threat of Storm Surge and sustained Wind:
Cyclones, sometimes associated with storm surge have been a cause of concern for Bangladesh.
From 1891–98, approximately 178 severe cyclones with wind speeds of more than 87 kilometers perhour (km/h) formed in the Bay of Bengal, causing extensive loss of life and destruction of property.
Estimated storm surge could reach more than 20 feet along the coast in India and Bangladesh.
Districts likely to be impacted by the storm. High wind speed is another root cause the formation of
cyclone. For cyclone fani estimated maximum wind speed was measured through kinetic analysis.
Where 30 m.p.h was the lowest speed and 150 m.p.h was the highest value.

6

Source: (Kinetic Analysis; The New York Times, Asia Pacific; 03 may 2019)
Figure 2: Estimated storm surge and maximum wind speed.

Methodology and structure of the report:
The assessment team was consisted of 6 members representing Bangladesh Network of Urban Safety(BNUS), BUET. Members of the assessment team visited the most vulnerable area like Mirzaganj
upazila, Lalua union, Mohipur union and assessed the situation between 20 May and 1 June 2019.
The assessment report is based on the following methodology:
1. Analysis and interpretation of primary dataArea survey by the survey team and collection of information from households with the helpof the affected communities

Observations and experience of each members of the survey team and interpretation andintegration of those observation and experiences

Interviews of the UP Chairman, members in affected areasPersonal interviews of the communities in the affected areasCommunity Focused Group Discussions (FGDs)
7



Key informative Interviews (KII) from different GO and NGO.

2. Analysis and interpretation of secondary dataBasic data from the GoB district authorities



Damage data from division, district and upazila administrations

Analysis and integration of published reports in national and local level dailies

Satellite imagery collected from Sentinal-1 and analyze the image through Arc-gis 10.1
software.

Description of the Study Area
Bangladesh is a densely-populated, low-lying, mainly riverine country located in South Asia with acoastline of 580 km (360 mi) on the northern littoral of the Bay of Bengal. The delta plain of
the Ganges (Padma), Brahmaputra (Jamuna), and Meghna Rivers and their tributaries occupy 79
percent of the country. Bangladesh has a tropical monsoon climate characterized by wide seasonalvariations in rainfall, high temperatures, and high humidity. Straddling the Tropic of Cancer,
Bangladesh has a tropical monsoon climate characterized by heavy seasonal rainfall, hightemperatures, and high humidity. Natural disasters such as floods and cyclones accompanied
by surges periodically affect the country. Most of the country is intensively farmed, with rice themain crop, grown in three seasons. Rapid urbanization is taking place with associated industrial andcommercial development. Patuakhali District (Barisal division) area of 3220.15 sq. km, located in
between 21°48´ and 22°36´ north latitudes and in between 90°08´ and 90°41´ east longitudes. It isbounded by Barisal district on the north, Bay of Bengal on the south, Bhola district on the east,
Barguna district on the west. The land of the district is composed of alluvial soil of the meghna basin
and of a number of small char lands. Total population is about 1460781 including male 739331,
female 721450; Muslim 1350968, Hindu 107893, Buddhist 391, Christian 1338 and others 191.
Water bodies are Andharmanik, Agunmukha, tentulia, Galachipa, Rajganj; Rabnabad channel arenotable (Banglapedia, 2006). In this study, six specific locations, one from each upazila are selected
and studied. So, six specific areas except Galachipa of the district of Patuakhali are the study area of
this research which is shown in Figure 3. Mirjagonj is one of the most vulnerable upazila under
Patuakhali district. Mirzaganj is located at 22.3625°N 90.2417°E .
According to the 1991 Bangladesh census, Mirzaganj had a population of 110,763. Males constituted
50.06% of the population, and females 49.94%. The population aged 18 or over was 56,446. Average
literacy rate is 42.1%; male 48.6%, female 35.6%. Another most vulnerable upazila is Kalapara
under Patuakhali district. Kalapara is located at 21.9861°N 90.2422°E . It has 31324 households and8total area 483.08 km2. The major rivers are the Andharmanik, Nilganj, and Dhankhali. As of
the 1991 Bangladesh census, Kalapara has a population of 174921. Males constitute 50.89% of the
population, and females 49.11%. This upazila’s eighteen up population is 82394. Kalapara has anaverage literacy rate of 34.9% (7+ years), and the national average of 32.4% literate.

Data Collection Method
Primary data collection
Deriving accurate information is highly dependent upon the survey method (Ahmed et al, 2011).
Reconnaissance survey was carried out to ascertain the primary idea about the locality, community
hazard related risk and impact on crop production and farmers perception about climatic event like
cyclone Fani and find out an approach of the study area.

Secondary data collection
Books, journals, report, internet documents, union praised office records, NGO office report, andrecords and published reports were used as secondary sources of data supporting or supplementing
the empirical findings of the study. Related web sites were also searched to collect latest information
about the research issue.

9Figure 3: The Study Area Map (Patuakhali, Mirzaganj and Kalapara upazila); (Source: Arc
GIS, 10.3).

10Focus Group Discussion
Focus Group Discussion was conducted on both male and female. It was helped to gather a wide
range of information in a short time (Kothari, 2011). The participants of the FGD have 6 to 10 people
engaging different occupations. The participants would be asked question about the rural condition,
which type of hazard related risk they face, for polders which type of problems and benefit they are
faced on community. Due to climate induced disaster which kind of approach are fruitful to enhance
the community resilience against this. Based on the objectives of the study, 20 Focus Group
Discussion (FGD) was conducted.

Figure 4 Focus Group Discussion in the study area.
Key Informants Interview
A key informant is an individual who has a great depth of knowledge about a specific field and canoffer perceptive information to the researcher (Kothari, 2011) relate to the research questions andproblem solving suggestions related to any problem. The key informants include sub-divisional
engineer (WDB), Patuakhali, NGO members, and community leaders, school teachers were selectedfrom three unions for KII to conduct a qualitative assessment. The interviews were conducted in anatural and relaxed situation and the interview process must be kept flexible and more open than the
structured interviewing with open questions (Kumar, 2011). Key informants were interviewed face toface and over cell phone using semi-structured questionnaire and were recorder and documented as
per procedure. Total 5 (five) key informant interviews were conducted.
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Figure 5 Key Informative Interview in the study area.
Results and Discussion:
Demographic and Socio-Economic Condition
It was needed to have a clear concept of demographic and socio-economic condition of respondents
to understand their actual various existing local risk factors impact on their livelihood and theirresilient mechanisms. This part considers the socio-demographic status of entire unions of the study
area.
Gender ratio
Total no of respondents is 120 and among 120 respondents 84% are male and 16% are female
farmer. Basically women organize the homestead gardening and in some case accompanying withmale farmers where economic status is poor. Women help in irrigating the vegetables garden during
Rabi season (mid-September to mid-march) by traditional method using bucket, jar etc.in some casewhere female headed house exist all of the responsibility goes for them. The respondent’s genderdivision is shown in Figure (02).
Information collected from male and female respondent was very much helpful to reveal thecommunity people’s perception about different hazard like cyclone Fani, knowledge and level ofunderstanding regarding the various existing local risk factors, impact on agriculture sector and theirresilient mechanisms (Ali, 2012) reported that the male and female farmer ratio of coastal area is
6:1.that means female are less encouraged in crop production. Researchers (Hossain and Roy, 2010)
found that female farmer engagement with homestead cultivation reduce the impact of hazard as they
are first responder. Again the association of male and female farmer into crop field increases the crop12

production by reducing the risk factor (Hossain and Roy, 2010), This study concluded that a large
number of female have included here but actually this happen due to less availability of female
farmer during survey time.
Age of the respondents
Respondent’s age ranged from 20 to 80 years. The age distribution of respondents is shown in Figure(03).

GenderFemale
16%

Male
84%

Male

FemaleFigure 6: Gender ratio of Mirzaganj, Lalua and Mohipur Union.

Respondent age number

Respondent Age Level
50

42

40

31
27

30

13

20

5

10

2

0

71-80
Years

above 80
Years

0
20-30
Years

31-40
Years

41-50
Years

51-60
Years

61-70
Years

Level of age

Figure 7: Respondent Age level of Mirzaganj, Lalua and Mohipur Union.

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The research shows that among 120 respondents 42 respondent’s age between 20-30years, 31 isrange of 31-40 years, 27 respondent’s age lies between 41-50 years where 13 respondent’s age is
between 51-60 years and only 5 respondents’ age is between above 61 years and at last only 2
respondent’s age is around 71-80 years. Respondent’s age shows a variant result as maximum people
73 have in less than 40 years age (Biswas et al. 2015a) reported that age distribution of farmer-17
farmer age is above 50 years at coastal region. Another study Billah, (2013) found that elderly
farmer’s distribution is only 17%.Respondents age have greater influence on risk perception andprediction of climatic hazard (Biswas et al, 2015a). Researcher (Biswas, 2015) found that farmer age
distribution between 40-60 years have greater influence on climate change conception.
Primary occupation
People’s occupation of this region is farming in a larger rate. In Figure it specify that about 49
respondents said that farming is their main source of income, day laborer is common types of
livelihood and 11 respondents indicate that. Fishing as a primary occupation is occupies by 21
respondents. Local business is very common in this locality. In this regard women plays key role.
Primary occupation of the respondents is shown in Figure 04.

Primary Occupation
Respondent Number

60

49

50
40
30

25

20
10

21
11

9

Day
Labourer

Teacher

50Business

FarmerFisherman Contractor

Occupation
Figure 8: Primary Occupation of Mirzaganj, Lalua and Mohipur Union.
Billah et al (2013) found that in coastal area the primary occupation of the people is non-farmactivities like fishing, day labor, small traders etc. Diversified livelihoods have greater influenceon adaptation to climate change (Khaled, 2009).

Housing pattern of the study area
The household condition of the study area is very poor. The pie chart (Figure 05) represents theexisting housing pattern of the study area. In the study it is found that most of the houses are14

katcha and the rest are semi-pucca and pucca types. In the pie chart it is indicated that the 46%
respondent’s house is kutcha, besides 36% household condition in that village is semi-pucca
type. The rest respondent’s house is pucca type. This poor housing condition makes this area ismore vulnerable to natural disasters. Recurrent natural disasters are occurred in this area for thisreason these types of houses are seriously affected by the violent cyclone, flood, irregular rainfall
etc. which increase community risk. During cyclone Fani most of the houses were severely
affected and some are partially affected due to the poor housing condition of this area.

Household Condition
Semi- Pucca
36%

Katcha
46%

Pucca
18%

Semi- Pucca

Pucca

KatchaFigure 9: Household condition of Mirzaganj, Lalua and Mohipur Union
Overview:
In Patuakhali and Barguna district 1 upazila and 2 unions have been hit by Fani. Reports say Around
60 km of flood control polders in Patuakhali and Barguna district got badly damaged due to tidal
surge caused by cyclone Fani Moreover, thousands of people are different villages in those districts
have been suffering a lots as their dwelling houses and vast tracts of agricultural lands remained
inundated due to the impact of this cyclone. Besides, tidal water keeps entering those villages
through the damaged portion of the polder since the cyclone hit the southern region. 14 killed and 45
injured due to lightning, falling trees and house collapses (Health Emergency Operations Centre andControl Room of the Directorate General of Health Services). Major affected areas are Mirzagonj
upazila, Mohipur union and Lalua union. Satellite data available on 05 May 2019 confirmed at least
40 percent of the district’s total populated area was submerged under 1-3 feet of water. About 60 km
in the polders around the district were reported through which water is freely flowing into the lowlying regions. The government officials reported about 60 km of embankment been damaged due tothe breaches. In terms of number of people affected Lalua tops the list with Chairpara, Nawapara,
Chowdhuripara, Nayakata, Munshipara, Chandupara, Hasnapara, Pashurparabunia to follow. In most15

of the area water is yet to recede, and with the full moon approaching, there is a possibility that waterlevel will hoist. Migration from the affected areas due to waterlogged conditions has been a featureof the post Fani scenario. Fani also features a severe damage of the earth-made structures (mostly
households and polders) and massive death of livestock. In this time of the rainy season, it is notpossible to repair polder properly as commented by the locals. There is a possibility of permanent
waterlogging in the area which is already forcing large scale migration and heaving the number ofhomeless and unemployed people. Social unrest is increasing throughout the area and harassments of
the relief workers and others are increasing rapidly.

Damage Information
Food security
Cyclone Fani had the worst impact on the local food security in situation Patuakhali and Barguna
Districts. Damage of 15,688 acres of standing crops has been reported officially (DAE Deputy
Director Hridoyeswar Dutta), the main crops damaged are dry season vegetables, sesame, pulses andBoro paddy. A complete official estimate of total agricultural land being damaged is not yet
available. However, information is available from the Upazila Agricultural Offices and local
journalists, UP Chairman, members and locals.
Local food markets in the Fani affected areas are not functioning well as many food markets are still
under water. The survey team observed a shortage of food in the markets which are still functioning
and the price range is quite high that that of usual. In Mirzagonj upazila only one food market was
found functioning on 20 May 2019 which is at the center of the upazila. Most of the shops were
found closed during the time of high tide, as the market place went under two feet of water. In Lalua
and Mohipur some scattered food shops were found functioning rather than in a market as a whole.
There is a serious scarcity of dry foods among the affected dwellers. Cooking fuels are short oralmost non-existing and dry places are rare in the affected areas. Lack of fuel is also restricting
people to cook foods that are sought from elsewhere. Md Joynal Abedin, Assistant Head Teacher,
Charkhali Hgh School said local administration is distributing rice among the affected people, butthey cannot cook due to lack of fuel, and because there are no high lands nearby.
Roads and Polders
Roads and Polders’ is one of the worst affected sectors in Fani. Of the 60 Kilometer damaged polder,around 30 Km is under Patuakhali Water Development Board (WDB), 10.5 Km under16

Figure 10: Damaged polders after cyclone Fani at the study area.
Kalapara WDB in Patuakhali and 18.5 Km Is in Barguna, according to the Patuakhali and Barguna
WDB offices. The local Sub- divisional Engineer of Patuakhali WDB Md. Shah-Alam said thatamong the 30 km polder, about 5.5 km was damaged fully and 24.5 km partly over this area.
Meanwhile, in Kalapara, around 10.5 kilometer polder was badly damaged and tidal water keeps
entering through the damaged portion in different villages. Kumar Sastik, acting executive engineerof Kalapara WDB, said in Barguna, due to lack of repair around 18 kilometre of the flood control
polder at 37 spots under threats.

The government has taken initiatives to repair the polders.

However, reports from mirzagonj, Lalua and Mohipur divulge that in those places most repairs
haven’t been successful due to high pressure of tidal prism and continuous water flow through thebreaches.
According to the people of Mirjagonj, Lalua and Mohipur union, polders were presented in that area.
Community people were highly benefited by these polders. As those polders has higher elevation sopeople were using this in many aspects, such as for communication purpose, shelter during flood
situation, prevent saline water intrusion and finally protect their crops. Although those unions had
polder but it didn’t cover all the areas. Thus various situations were occurred such as, salinity
intrusion, flooding and many seasonal crops were highly damaged.
Before the occurrences of FANI cyclone the polders of that area were moderate condition. People’s
opinion about the polder condition are represented in Figure (06).
17

Condition of polders before FANI cyclone
Weak
14%

Good enough29%

Moderate
57%
Good enoughModerate

Weak

Figure 11: Condition of polders before cyclone FANI at Mirzaganj, Lalua and Mohipur Union
Cyclone FANI had devastating impact on physical aspects over this area. Around 60 km of polders
over this area got badly damaged due to tidal surge caused by cyclone fani. From the Key
informative interview
The most affected villages are mehendiabad, Ranipur, Golkhali, Hazikhali and Charkhali, all along
the Payra river in Mirzagonj upazila.
Of the villages, Kamarpur, Nizampur, Puran Mahipur, Yusufpur, Monoharpur, Najibpur under
Mohipur union and Chairpara, Nawapara, Chowdhuripara, Nayakata, Munshipara, Chandupara,
Hasnapara, Pashurparabunia under Lalua union are most vulnerable.
Lalua union parishad chairman shawkat Hossain said alleast 16 villages under this union have beeninundated after a portion of the polder along the Ramnabad river broke down.

Surge Monitoring and Assessment using (SAR) from sentinel-1 at Mirzaganj, Lalua andMohipur union:
Patuakhali and Barguna districts face severe weaknesses and high-risk situations due to coastal
threats like erosion and storms and due to an increasing intensive occupation. Tropical storms events
can contribute to the occurrence of these situations, by causing storm surges with high water levels
and, consequently, episodes of waves overtopping and coastal flooding.
To assess the project location’s exposure to storm surge in the Historical/Current time-frame, aconservative screen of 10 meters is applied. That is, if the project elevation is 10 meters above thepresent maximum high tide level, then the project will not likely be exposed to storm surge.
18

The 10 meter elevation threshold indicated above is roughly the elevation of the greatest historicalstorm surge and wave run-up heights.
The highest storm surge in the past century occurred during the Great Bhola Cyclone of 1970 in the
Bay of Bengal, when a storm surge of 10.6 meters occurred during one of the highest high tides ofthe year.10 The highest storm surge on record in the United States was from Hurricane Katrina in
2005, measured at 8.4 meters above the normal astronomical tide level.
There are many sophisticated techniques for estimating maximum storm surge involving numericalmodeling. To assess whether the project will be exposed to future storm surge, the tool helps the user
to identify whether the project elevation is within 11 meters’ elevation of the present maximum hightide level. Storm surge height is likely to be increased by sea level rise. The 11 meter threshold isbased on a combination of the maximum present day surge values (10 meters) and the upper end ofsea level rise projections for 2050 (0.6 meters). (See “Historical/Current” section, above, and “SeaLevel Rise,” preceding page). More precise estimates of changes in the magnitude of future storm
surge are difficult to make and are relatively uncertain. There is high confidence that storm surge
extremes will increase with sea level rise, yet there is low confidence in region-specific projections
in storminess and storm surges.
For this report purpose, shows the surge inundation areas in Mirzaganj and Lalua union from
Patuakhali district. In the period of cyclone Fani the selected area were inundated through storm
surge. Deuli village from Mirzaganj upazila was severe affected by the inundated water. Many othervillages were seriously affected by surge water. On the other hand, in Lalua union most of thevillages were serious damaged by inundated water. Lalua was one of the most severe affected village
due to surge water. Through satellite image analysis from (Arc-gis 10.1) inundated villages are given
in figure (07, 08).

19

Figure 12: Inundated area due to surge water at Mirzaganj Upazila.

20

Figure 13: Inundated area due to surge water at Lalua Union.

Conclusions
It has been cleared from the assessment that not only from the cyclone FANI, most of the cyclones
affect the coastal part of Bangladesh has more or less influence on this particular area. When acyclone makes its landfall we all are concern about the hit area but most of them have the influence
through a large part of the coast. The focus of this study was to identify the effect of cyclone FANI
but we also found some information related to other cyclones from the past. However the polders inour coastal area to protect the surge water but sometimes it make the situation worse by retaining thesaline water when it overtops. Sea level rise and excessive river siltation are also exhilarating the21

surge height. Only proper maintenance can solve this types of problems which are not seen in mostof the area.
References:"Bangladesh Navy prepared to face Fani with 32 ships". Dhaka Tribune. 4 May 2019. Retrieved 4
May 2019.
"Bangladesh prepares for Fani as cyclonic storm draws near". Dhaka Tribune. 1 May 2019.
Retrieved 1 May 2019
"Cyclone Fani: Naval ships, aircraft on standby". The Hindu. 30 April 2019. Retrieved 30
April 2019.
AHMED, A. R., BHUIYA, M. A. A., REZA, Z. A. & HOSSAIN, M. Z. (2010) Methods ofStatistics. 4th Ed. North Seota: Manikganj
ALI M.M. (2012) Impacts of Climate Change on Cropping Pattern in Coastal Region of Bangladesh:
A Case Study of Sharankhola Upazila, Bagerhat, MS Thesis, and BRAC University, Bangladesh.
BISWAS, A.A., HASAN M., RAHMAN M.S., SATTAR, M.A., HOSSAIN M.A., FAISAL M.A.,(2015) Disaster Risk Identification in Agriculture Sector: Farmers perceptions and Mitigation
Practices in Faridpur. American Journal of Rural Development. Vol. 3(3). pp. 60-73.
BISWAS, A.A., ISLAM, SATTAR, M.A., MILI, JAHAN T., (2015) Community Based Risk
Assessment of Agriculture Sector in Sreerampur union of Bangladesh. Journal of Food Security, vol.
3, no. 5 (2015): pp.125-136.doi: 10.12691/jfs-3-5-2.
BISWAS, A.A., ZAMAN, M., SATTAR, M.A., ISLAM, M.S, HOSSAIN, M. A. & FAISAL, M.
(2015) Assessment of Disaster Impact on the Health of Women and Children. Journal of Health andEnvironmental Research. 3 (1). p. 19-28.
Cyclone Fani churns toward India, prompting evacuation of almost 1 million". CBS News. AFP. 3
May 2019. Retrieved 3 April 2019.
HOSSAIN M.S., ROY K., (2010) Community Based Risk Assessment and Adaptation to Climate
Change in the Coastal Wetlands of Bangladesh: A Case Study from Chenchuri Beel, Narail,
Bangladesh. Proceedings of Int. Conf. on Environmental Aspects of Bangladesh, Japan, Sept.
KHALED, M., (2009) Disaster Risk Management in Support of Community-based Adaptation to
Climate Change Impact in North Turkana District of Kenya. MS Thesis, Faculty of Natural andAgricultural Science, At the University of the Free State, South Africa.
KOTHARI, C. R., (2011) Research Methodology: Methods and techniques. 2nd Edition. New Delhi,
India: New age International publishers.
Kumar, Hari; Gettleman, Jeffrey; Yasir, Sameer (3 May 2019). "How Do You Save a Million People
From a Cyclone? Ask a Poor State in India". The New York Times. Retrieved 5 May 2019.
22

Mohanty, Debabrata; Bhattacharya, Snigdhendu (4 May 2019). "Fani leaves trail of devastation;
India, Bangladesh count losses". Hindustan Times. Retrieved 5 May 2019.
TC Advisory 1. India Meteorological Department (Report). 27 April 2019. Archived from theoriginal on 30 April 2019. Retrieved 30 April 2019.
Tropical Cyclone Formation Alert. Joint Typhoon Warning Center (Report). Naval Meteorology andOceanography Command. 26 April 2019. Archived from the original on 30 April 2019. Retrieved 30
April 2019.
Wright, Pam (2 May 2019). "Tropical Cyclone Fani Nears India as Country Works to Evacuate
Hundreds of Thousands". The Weather Channel. Retrieved 2 May 2019.

23

PART-IICYCLONE EVACUATION ROUTEIDENTIFICATION USING GIS FOR COASTALCOMMUNITIES IN BANGLADESHBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Sraboni Sarker
Maruf Billah
Mehedi Ahmed Ansary24Introduction
Different natural disasters, such as cyclones, droughts, and floods are increasing globally in recent
years (IPCC 2001a, b; Khan and Rahman 2007). Cyclones are considered as the world’s leading
natural disasters that even exceed the effects of earthquakes (Murty and El-Sabh 1992; Finkl 1994;
Dube et al., 1997; Zerger et al., 2002; Benavente et al., 2006).Bangladesh is the most vulnerable area
to cyclones because of its geographical location at the triangular shaped head of the Bay of Bengal(Neralla, 1992), due to its high population density and the absence of effective coastal protection
systems. In the pre-monsoon (April–May) or post-monsoon (October–November) seasons, at least
one major tropical cyclone hit the Bangladesh coastal region each year (Mooley 1980; Haque 1997).
Approximately one-tenth of global tropical cyclones occur in the Bay of Bengal (Choudhury, J. R.
1994). About one-sixth of the total cyclones developing in the Bay of Bengal make landfall on the
coastal parts of Bangladesh (Quadir and Iqbal, 2008).If a cyclone occurs in tropical regions, it is
known as a tropical cyclone. Cyclones occurring elsewhere are known as extra-tropical cyclones(Fritz et al., 2008; Fritz et al., 2007).
Existing literature shows that several devastating cyclones have stuck Bangladesh in 1822, 1876,
1961, 1965, 1970, 1991 and 2007 (Blaikie et al., 1994; Dube et al., 1997; GOB 2008). Among the 64
districts of Bangladesh, 19 are the located in the coastal areas. The population of this area is nearabout 36.8 million and more than half of the population (52%) are poor and about 41% is under theage of 15 (Islam, 2008). In spite of the poverty and being the most vulnerable to different kinds ofcoastal disasters, with their traditional indigenous knowledge and perceptions, these coastal people
are coping with different kinds of disasters and passing their lives with relentless struggle (Parvin etal., 2008). Two destructive cyclones occurred in 1970 and 1991, with more than 500000 and 140000
causalities, respectively. However, in the last 20 years, Bangladesh has been able to reduce deaths
and injuries from the cyclones. For example, the recent major cyclones like Sidr of 2007 and Aila of2009 caused 3406 and 190 deaths only, about a 100-fold reduction of lives compared to the deadliest
1970 and 1991 cyclones (Ali 1980; Haider et al., 1991; GOB 2008; Paul 2009a; Haque et al., 2012).
As controlling the occurrence of natural disasters is almost impossible, the loss of life and property
from these disasters can be minimized or prevented if proper precautions and preventive initiatives
are taken timely. For saving human lives the fictitious solutions is to build the habitation above thesurge level and construct them strong enough to resist high wind speed and the surges (Mallick et al.,2011). Nevertheless, the majority of coastal residents are living under poverty level in poorly
constructed houses that are vulnerable to cyclone (Paul and Dutt, 2010).Existing literatures shows
the importance of constructing raised platforms, reinforcement of homestead structures, overall
cyclone proof houses in reducing casualties (Irteja et al., 2017; Ahmed et al., 2012). Besides, focus
25

has been given on infrastructures stability and different infrastructures types to respond to cyclone
like temple shaped house and elevated houses (Paul and Routray, 2011).Locations and pattern ofsettlements are the most important factor to decrease vulnerability of the coastal people. Scatteredsettlements in the coastal areas are more susceptible to cyclone (Alam and Collins, 2010).In the
recent past, GIS and RS technology have increased the accessibility in the cyclone occurrence stage
in order to develop map and assess the vulnerability of lives and infrastructures of cyclone-prone
areas (Chisty et al., 2015; Rana et al., 2010).
The next solution is evacuation of the coastal people to the high ground above the surge level(Mallick et al., 2011). Evacuation is considered as the primary protective action against any largescale disasters like the cyclone. An early warning system plays a valuable role in the evacuation ofvulnerable people during the occurrence of the disasters along with minimizing the loss (Dhar andAnsary, 2012). The effective early warning system provided by the government in advance of
Cyclone Sidr, Aila, Mohasen, Komen, Roanu, Mora in 2007, 2009, 2013, 2015, 2016 and 2017enabled successful evacuation of coastal communities that resulted in fewer than expected deaths.
Besides the early warning systems, different activities like construction of cyclone shelters,
embankment, and timely evacuation have contributed to decreasing the death rates in the coastal
areas (Haque et al., 2012; Islam et al., 2010).In spite of the improvements in early warning systems,
pre-cyclone evacuation remains a challenge (Dhar and Ansary, 2012). Some factors like illiteracy,
lack of awareness, structural vulnerability and communication problems including poor road
networks are the obstacles that hamper the evacuation system. Instead of moving to cyclone shelters,
people in coastal areas still believe in a wait-and-see approach (Bern et al., 1993; Chowdhury et al.,
1993; Paul and Routray, 2013).On the other hand, access to road network, distance of cyclone
shelters from the homestead, and access to cyclone shelters are the factors that influence theevacuation decision (Paul, 2014).
Cyclone shelters play an important role during the evacuation. It has been estimated that around 1.5
million people have been evacuated by CPP (Cyclone Preparedness) volunteers to the multi-purpose
cyclone shelters (MPCS) when Cyclone Sidr hit the coastal parts of Bangladesh in 2007 (NatequeMahmood et al., 2014; Paul, 2009). Before the destructive cyclone of 2007, Bangladesh had only
1500 cyclone shelters in the coastal districts as the infrastructural support during cyclone. After Sidr,
Bangladesh Government has constructed around 2000 new cyclone shelters for the population of thecoastal belt (Das, 2017). The World Bank surveyed and further estimated that there is still a need for5500 new MPCS(Multi-Purpose Cyclone Shelter) facilities for the purpose of a disaster management
strategy (Nateque Mahmood et al., 2014; The World Bank, 2010; Islam et al., 2010).GIS-based map

26

has been produced to know about the coverage area of a shelter and need for new shelters (Mallick,2014).
Despite of completing these initiatives, the evacuation route to cyclone shelters from any community
remains a challenge and is not well defined. People living in the coastal areas are not aware of theappropriate evacuation plan and route during cyclones. They can’t take the decision during cyclones
in which shelter they should go and which shelter is within their reachable distance. It is common
that computing shortest paths over a network is an important task for evacuation. With thedevelopment of geographic information systems (GIS) technology, network and transportation
analyses within a GIS environment have become a common practice in many application areas.
Different factors like easy accessibility of the community people to the shelter with maximum
walking distance for an individual is restricted to 2.25 kilometers, covering the entire population of
the region by producing safe zones and evacuation zones, suitability of the shelters for othercommunity activities are needed to follow (Dalal, 2007;Gupta et al., 2017). There exists digitizedroad map including road class, distance and shortest optimum route (Devlin, 2008).Many network
flow model including logistics coordination model has been built for identifying optimal lane-based
evacuation routing plans in a complex road network by using mixed-integer programming solver(Cova and Justin, 2003; Murray and Mahmassani,2003; Yi and Ozdamar,2007). There exist gaps in
the above papers for identifying the shortest distance analysis which is needed during a evacuation
process. In the reviewed papers, shortest distance have been analyzed in a map for a vast area
without presenting numerical values of the distance from evacuation point to the shelters. Even how
many people could be evacuated from each point has not been defined. As Bangladesh is a cyclone
prone country, proper evacuation route is a need to reduce the number of affected people. This paper
presents an evacuation model for Mirjaganj union of Patuakhali, Bangladesh for identifying
evacuation route including the shortest distance analysis for each shelter.
Study Area
Figure 1(a) shows the past records of cyclones that hit Bangladesh. Among the Unions of Patuakhali
district, Mirzaganj Union is one of the most vulnerable unions to cyclone hazard. Area of Mirzaganj
Union is 34 square km, located in between 22°13' and 22°29' north latitudes and in between 90°08'
and 90°19' east longitudes. Total amount of road is near about 90 kilometers, among these paved
roads are 20 km, herring bond roads are 10 km and earthen roads are 70km. There are07secondary
schools, 23primary schools, and 16madrasa. Main source of income is agriculture with 8527 acres ofcultivable land. Due to the geographical location that union has been severely affected by 2007
cyclone Sidr and 2009 Aila. Figure 1(b) shows the location of the union.

27

Srimanta River
Payra River(a) (b)Figure1: (a) Cyclone Tracks in Bangladesh and (b) Study Area (Mirzaganj Union)

Methodology
The research is based on both secondary and primary data collected through focus group discussions
and a household questionnaire survey. As there are no databases accessible with the level of details
to be used in this particular study area for this research, the detailed area plan of the study location
has been mapped in Esri shape file format using GIS. By using 97% confidence interval, the total
sample size for the household questionnaire survey has been considered 830 out of 3200 households.
Samples are taken proportionately from four zones of Mirzaganj union.
After collecting the data, GIS and Microsoft excel have been used to analyze the collected primarydata. GIS has been used in developing maps, geographic data analysis, editing, data management,
and geo-processing activities. ArcGIS has been used in finding the closest shelter facility location,
origin-destination (O-D) distance generation and location allocation. A realistic evacuation modelwith the shortest route to shelters has been proposed in this study and has been validated using fielddata.
Result and Discussion
Mirzaganj union is bounded by the Payra River on the south and east, the Srimanta River on the west
as can be seen in Figure 1(b).According to the Jatiyo Batayon, there are 11 villages in this union.
Total population is 22100. Among them male is 11073 (50.10%), female is 11027 (49.89%). After
surveying 830 households, it has been found that average 7 people live each in household. The
28

literacy rate of the union is 70% including 13033 voters. Most of the people are farmers and small
shopkeepers.
Condition of the Existing Cyclone Shelters and Infrastructures
In Mirzaganj area there are only two usable cyclone shelters (Figure 2). The shelters are designed towithstand a wind speed of 260 km/hour and placed on a higher ground to avoid the effect of the
maximum expected surge level. Shelters have been constructed with the required facilities (Table 1).
The shelters buildings are primarily used as schools. Shelters are designed in such a way that bothhuman and livestock can take refuge there.

(a)

(b)

Figure 2: (a) Cyclone Shelter no 1; (b) Cyclone Shelter no 2.

Table 1: Details about Existing Cyclone Shelters
Different Attributes

Cyclone Shelter no 1

Cyclone Shelter no 2

Name

Vazna Monoharkhali Govt. Primary School

Vikakhali Govt. Primary School cum Cyclone

cum Cyclone Shelter

Shelter

5 minutes walking time from Mirzaganj ferry

5 minutes walking time from Vikakhali ferry

ghat

ghat

Date of completion

January, 2018

February, 2014

Type of Shelter

LGED type 3

Source of Fund

GoB and World Bank

Location

StructureThree storied buildingOpen ground-floor structure to avoid flooding from the storm surges

Open first floor with tube-well facilities is kept for cattle with ramp to move on it.

Second floor with room and bathroom facilities for human shelter

Capacity

1300

Total Room

In total 6 rooms, 4 large size rooms and 2 extra rooms with attached bathroom for pregnant
women and children.

29Facilities:•Total 4 bathrooms (2 attached)•Two motors for drinking water•Four tube-wells•4 water tanks capacity with 2000 liters•4 water tanks capacity with 7000 liters (especially in cyclone shelter no 1)•Solar system is installed•Rain water harvesting system (especially in cyclone shelter no 1)

Livestock Capacity

200

Source: Field Survey, May 2018.

The official capacity of each cyclone shelter is 1300 people. A large amount of people (about 19000)
have to stay at their own place due to lack of space in the shelters. That’s why a survey has been
conducted on the condition of households and their vulnerability to test if they can withstand cyclone
or not. The survey has been conducted through a semi-structured questionnaire to measure thevulnerability of the existing households.Table 2 presents the criteria used in the questionnaire toassess the vulnerability of the infrastructures. Scoring has been done depending on the criteria. There
are individual houses, schools, mosques and others structures of the assessed infrastructures, some ofwhich are shown in Figure 3.

(a)

(b)

(c)

Figure3: (a) Homestead; (b) Mosque and Madrasa and (c) School

Table 2: Infrastructure Vulnerability ScoringCriteria

Sub-Criteria

Score

Width of the house is perpendicular to nearest river’s length

Yes

1

No

4

Yes

1

No

4

Yes

1

No

4

Concrete

1

Tin

5

House is in the safe distance from the biggest trees
There is sufficient amount of trees around the house
Which type of house that is?

30

Size of house(floor area)
Plinth level
Ridge level
Wall‘J’ hook presence
Post of house

The thickness of tin
Window
Door

Square1

Rectangular or L shape

4

4′

1

Less than 4′

4

High

1

Low

4

Concrete

1

Wood

3

Tin

5

Yes

1

No

5

Concrete

1

Bamboo2

Wood

3

Above 0.35 mm

2

Less than 0.35 mm

5Size above 4′*4′2

Less than 4′*4′5Size above 3.6′*6.6′

1

Less than 3.6′*6.6′4*Analysis has been based on criteria collected from “Construction Manual on Shelters for Cyclone-Prone Coastal Areasof Bangladesh" proposed by Disaster Management Bureau (DMB) of the Ministry of and Disaster Management andRelief (MODMR) and the Bangladesh University of Engineering & Technology (BUET).

Figure 4(a) shows the vulnerability map of the studied union. Figure 4(b) indicates that among 3220
surveyed infrastructures 24 are with very low vulnerability (1%), 145 are with low vulnerability(4%), 1114 are with moderate vulnerability (35%), 1887 are with high vulnerability (59%) and 50
are with very high vulnerability (1%). It also shows that about 5% households are with low to very
low vulnerable to cyclone and 95% households are not strong enough to withstand cyclone.

Existing Road Network System
Network Analysis in GIS is based on graph theory and topology. It is often used to find solutions totransportation problems by using either vector or raster models to represent the real world. The
vector-based model appears to be more suited to analysis of precisely defined paths such as roads
and rivers (Bukholm, 2003). For a proper evacuation, a good road infrastructure is needed. Road
network in coastal villages are still in rudimentary stage and path to the cyclone shelters remain
unsafe prior to the landfall of cyclone because of excessive rainfall, high wind velocity, flooded
31

terrain and tidal waves. Most of the time, roads are damaged by storm surge and make an obstacle
for community people from going to safer places.
Figure 5(a) presents the map of road types of Mirzaganj Union. In this map three types of roads(earthen road, herring bond road, and paved road) have been shown. In Figure 5, three types of roads
and lengths of the particular roads are presented. Among those 72.47 kilometers are earthen road(71%), 24.49 kilometers are paved roads (24%) and 5.4 kilometers are herring bond roads (5%). It
can be clearly seen that half of the total road is of earthen type and is the most vulnerable during a
cyclone. For this reason during a cyclone earthen road may be a snag for community people for
evacuation. Besides width of roads play an important role during evacuation. Wider roads allow
more people to evacuate than congested roads.

(a)

(b)

Figure 4: (a) Map of vulnerability of infrastructures; (b) Infrastructures with various vulnerability ranges.

32

(b)

(a)

(c)

Figure 5: (a) Road types of the Union; (b) Length of Roads in meters; (c) Width of Roads in percentages.

Evacuation Route Analysis
It has been already mentioned that there is no delineated evacuation plan in this union. Whilewarning system of Bangladesh is improving day by day but evacuation remains as a challenge
because of lack of government plan. If there is a shortest route earmarked for every dense area,
people will be able to reach to the cyclone shelters properly. Before analyzing the shortest distance
we have considered cyclone shelters as inlet where people will take refuge and in every dense area
we assumed a point as outlet from where maximum people can gather and evacuate to the shelters. In
this study, based on the accessibility to shelters and distance from the main roads, 21 evacuation
points (outlets) are marked around the study area. These outlets are situated at or near to thesignificant roads.

(a)
(b)

Figure 6(a) shows the outlets in the densely populated areas from where maximum people can be
evacuated to the shelters and 6(b) shows the shortest distance from every outlet to the nearest shelter.
From every outlet to inlet shortest distance has been analyzed using Network Analysis Tool in
ArcGIS 10. As there are two inlets, the total outlets are divided into two areas as can be seen in
Figure 6. Green colored routes are presented as the shortest distances to cyclone shelter no 1 and redcolored routes are presented as the shortest distances to cyclone shelter no 2.
33

(a)

(b)

Figure 6: Outlets and Inlets in Mirzganj Union (Left), Shortest Distance Analysis of Mirzaganj Union (Right)

Figure 7 presents the shortest distances from the different outlets to the shelters. The shortest
distances for shelter no 1 are colored with green and the shortest distances for shelter 2 are coloredwith red. For shelter no 1, the shortest distance is 351 meters from outlet which is marked as CS1O3(which means, CS1 is Cyclone shelter 1 and O3 is outlet 3) and for shelters no 2, the shortest
distance is 518 meters from outlet which is marked as CS2O3.
In Figure 8 buffer zones have been developed keeping the outlets as the centers with 600 m radius toassume how many people are covered for evacuation. It shows that 16465 people (based on fieldsurvey) can be evacuated to shelters, which is70% of total population. Time is also an importantfactor for real time evacuation. The shortest distance along with the shortest time can be the bestsolution for assuming how much time it will take to move to the shelters through the shortest
distance from each outlet. However, from the field survey we have found that during cyclone
different types of factors like connectivity nodes and bent nodes may affect the travel time.

34

Shortest Length to Shelters (m)
5583

6000
4936
4374

Shortest Length

5000

4721
4092

3625

4000
2575

3000

3293
2715

2393
2136

2246
1812

2000 13451601
1000

1993
1636
1343
746

351

518

0

Outlets
Figure 7: Shortest length to Shelters

(a)

(b)

Figure8: Buffer Zones for Outlets (a), People departing from each Outlet in Bar Chart (b).

Figure 9shows the various bent nodes and connectivity nodes of the road network that cause delays.
The placement of bent nodes and connectivity nodes are put in those places from where people
would take turn to other roads and where roads have crossed each other.
In Table 3, we have developed a map of bent node network in which we have assumed 2 seconds
delay for the bent nodes and in the map of connectivity node network we have assumed 4 seconds
delay for the connectivity node for every shortest length to shelter. In Table 3, walking distance has
been assumed 3.6 kilometers per hour or 60 meters per minute.

35

(a)

(b)

Figure 9: Connectivity Nodes Map (a) and Bent Nodes Map (b)Table 3: Shortest Time Analysis considering factors
SL

OutletShortest

Walking

Shortest

Time

Time

No.

name

length (m)speed= 60

timeconsuming

consuming

Total timeshortest

m/min*

without

for bentfor

consumedtime (min)bentsnode (s)connectivity

due to thenode (s)bents (min)

(min)

Final

01

CS1O9

1345

60

22.40

15*2= 30

9*4= 36

1.1

24

02

CS1O5

1601

60

26.70

14*2= 28

5*4= 20

0.80

28

03

CS1O7

2575

60

42.91

13*2= 26

5*4= 20

0.77

44

04

CS1O3

351

60

5.85

21*2= 42

14*4= 56

1.63

8

05

CS1O15

2393

60

39.88

25*2=50

8*4= 32

1.37

41

06

CS1O11

2136

60

35.60

1*2= 2

6*4= 24

0.43

36

07

CS1O19

4936

60

82.26

17*2= 34

10*4= 40

1.23

84

08

CS1O21

4374

60

72.90

18*2= 36

17*4= 68

1.73

75

09

CS1O17

3625

60

60.41

18*2= 36

8*4= 32

1.13

62

10

CS1O13

4721

60

78.70

16*2= 32

14*4= 56

1.47

80

11

CS1O23

3293

60

54.88

18*2= 36

14*4= 56

1.53

56

12

CS1O1

5583

60

93.05

19*2= 38

9*4= 36

1.23

94

13

CS2O18

4092

60

68.20

26*2= 52

15*4= 60

1.87

70

14

CS2O16

2715

60

45.25

28*2= 56

15*4= 60

1.93

47

15

CS2O12

1812

60

30.20

25*2= 50

13*4= 52

1.7

32

16

CS2O14

2246

60

37.40

19*2= 38

19*4= 76

1.9

39

36

17

CS2O6

746

60

12.40

18*2= 36

7*4= 28

1.1

14

18

CS2O3

518

60

8.60

38*2= 76

17*4= 68

2.4

11

19

CS2O8

1343

60

22.38

22*2= 44

20*4= 80

2.1

25

20

CS2O10

1636

60

27.30

17*2= 34

12*4= 36

1.17

29

21

CS2O1

1993

60

33.20

34*2= 68

25*4= 1002.8

36*Walking speed 3.6 km/h or 60 m/min (Hasnat, 2016).

Figure 10 shows time to reach to the shelters from the outlets using the shortest routes consderingbents along the path for the two shelters.

(a)

(b)
Figure10: Shortest Time (minutes) from each Outlet

It has already been mentioned that 1300 people can take shelter in each cyclone shelter. In reality
about 4000 people take shelter in each of them and there is always overcrowding as reported by
LGED (2014). Maximum 8000 people can use two shelters. On the basis of these data, two
scenarios(with 1300 capacity and with 4000 capacity) have been developed to show how many
people can be safe from cyclone by taking asylum in the shelters separately.
Figure 11 shows population departure from different outlets for the shelters (having 1300 capacity).
In this figure,Green color represents the outlets from where people will be able to take refuge to theshelters since the time for evacuation from these outlets are the shortest. On the other hand, Redcolor represents the outlets from where people will be unable to take refuge to the shelters since thetime for evacuation from these outlets are relatively high that means the shelters will be fully
occupied before the people from these outlets reach the shelters.
Figure 11(a) shows the number of people departing from each outlet that cover the capacity (1300
people) of the cyclone shelter number one. It is observed that 1064 people can depart from outlet
CS1O3 and only 236 people can departfrom outlet CS1O9 to fully occupy shelter one. Similarly for
Figure11(b), it is observed that 665 people can depart from outlet CS2O3 and only 635 people candepart from outlet CS2O6 to fully occupy shelter number two.
37

(a)

(b)

Figure11: Population from outlets that cover (a) Cyclone Shelter number 1; (b) Cyclone Shelter number 2;whencapacity is 1300 people in per shelter.

Figure 12 shows population departure from different outlets for the shelters (having 4000 capacity).
Figure 12(a) shows the number of people departing from each outlet that cover the capacity (4000people) of the cyclone shelter number one. It is observed that 3500 people can depart from outlet
CS1O5, CS1O3, CS1O15, CS1O11 and only 500 people can departfrom outlet CS1O9 to fully
occupy shelter one. Similarly for Figure 12(b), it is observed that 3451 people can depart from outlet
CS2O1,CS2O3, CS2O6, CS2O8, CS2O10 and 549 people from CS2O12 to fully occupy shelter
numbertwo.

(a)

(b)

Figure12: Population from outlets that cover (a) Cyclone Shelter number 1; (b) Cyclone Shelter number 2; whencapacity is 4000people in per shelter.

Figure 13 show that having capacity of 1300 people in each cyclone shelters can cover only 12% of
the total population of the union. On the other hand, if the capacity is 4000 persons per shelter, it
may cover 36% of the total population union. From Figure 4, it has been seen that about 5% (169
infrastructures) of total infrastructures are of low and very low vulnerability that can also be used as
38

shelter during cyclones. These infrastructures include primary schools, mosque, homesteads (highplinth level with sturdy construction, as can be seen in Figure 3a).It has been assumed that average
50 persons can take shelter in these low vulnerable infrastructures covering 39% of the total
population. If the capacity of the cyclone shelter has been considered as 1300 then with low
vulnerable infrastructures, the capacity can be increased up to50% of the total population. And if we
consider maximum capacity of the shelters to be 4000, along with low vulnerable infrastructures thecapacity can be increased up to 75% the total population.
Population vs. Capacity
25000

22100

22100

22100

22100

22100

Population

20000
16500

Total
population

15000
11100
10000

8000

8500

75%
5000
0

2600

12%
Shelter Capacity
1300

36%
Shelter Capacity
4000

39%
Low Vulnerability
House Capacity

Capacity of Shelters & Infrastructures

50%

Peopletakingshelter

Low Vulnerability
Low Vulnerability
House Capacity with House Capacity withshelter Capaccity
shelter Capaccity
1300
4000

Figure13: Population demand versus capacity under various scenarios

Conclusions
For a developing country like Bangladesh, the death rate due to cyclone is reducing day by day. The
2007 Cyclone SIDR is not less devastating than the cyclone of 1991but the death toll has been
reduced due to proper early warning dissemination. It is a clear indication that we are improving
ourselves in disaster management sector. Hence, death rate could be reduced more if the evacuation
has been conducted in a scientific manner. For a proper evacuation, departure route need to be
developed and followed. Interview with people affected by cyclones has showed that the number ofcyclone shelters in the study area is inadequate. The household condition is also vulnerable. The
different existing road types have been considered for the development of evacuation route. Moreoutlets have been suggested in the dense areas from where more people may go to the shelters.
Analyzing the shortest distance and time from every outlet to shelters, it has been found that if we
consider the shelter capacity to be 1300, only 12% of the total population can be accommodated into
the shelters. If we consider, the shelter capacity to be 4000, only 36% of the total population can beaccommodated into the shelters. If we consider the low vulnerable infrastructures, 75% of the total
population can be accommodated. This study provides us an idea how we can manage our population39

to find out suitable nearby shelters. This study can be used as a role model for coastal belts of
Bangladesh for developing a detailed cyclone evacuation plan for the coastal community.

Reference
Ahamed, S., Rahman, M. M., & Faisal, M. A. (2012). Reducing cyclone impacts in the coastal areasof Bangladesh: a case study of Kalapara Upazila. Journal of Bangladesh Institute of Planners
ISSN, 2075, 9363.
Ahsan, R., Rahman, M., Islam, M.S. (2008). "Construction Manual on Shelters for Cyclone-Prone
Coastal Areas of Bangladesh", Engineers Without Boarders, Japan and BUET.
Alam, E., & Collins, A. E. (2010). Cyclone disaster vulnerability and response experiences in coastal
Bangladesh. Disasters, 34(4), 931-954.
Ali A (1980) Storm surges in the Bay of Bengal and their numerical modeling. SARC Report No.
125/80. Atomic Energy Commission, Dhaka
Benavente J, Del Rı´o L, Gracia FJ, Martı´nez-del-Pozo JA (2006) Coastal flooding hazard related tostorms and coastal evolution in Valdelagrana spit (Cadiz Bay Natural Park, SW Spain). Cont
Shelf Res 26(9):1061–1076
Bern, C., Sniezek, J., Mathbor, G. M., Siddiqi, M. S., Ronsmans, C., Chowdhury, A. M., ... & Noji,E. (1993). Risk factors for mortality in the Bangladesh cyclone of 1991. Bulletin of the World
Health Organization, 71(1), 73.
Blaikie P, Cannon T, Davis I, Wisner B (1994) At risk: natural hazards, people’s vulnerability, anddisasters. Routledge, London
Bukholm, I. R., Husdal, A., Nesland, J. M., Langerød, A., & Bukholm, G. (2003). Overexpression of
cyclin A overrides the effect of p53 alterations in breast cancer patients with long follow-up
time. Breast cancer research and treatment, 80(2), 199-206.
Chisty, K. U., Islam, M. K., & Samad, M. R. B. An Application of GIS for Cyclone Hazard
Modelling and Vulnerability Assessment in Coastal Area, a Case Study at Anawara Upazila inChittagong, Bangladesh.
Choudhury, J. R. (1994, November). Cyclone shelter and its multipurpose use. In Proceedings of the
IDNDR Aichi/Nagoya International Conference (pp. 1-4).
Chowdhury AM, Bhuyia AU, Choudhury AY, Sen R. The Bangladesh cyclone of 1991: why somany people died. Disasters 1993;17:291–304. doi:10.1111/j.1467-7717.1993.tb00503.x
PMID:20958772
Cova, T. J., & Johnson, J. P. (2003). A network flow model for lane-based evacuation routing.
Transportation research part A: Policy and Practice, 37(7), 579-604.
40

Dalal, J., Mohapatra, P. K., & Mitra, G. C. (2007). Locating cyclone shelters: a case. Disaster
Prevention and Management: An International Journal, 16(2), 235-244.
DAS, T: Challenges of cyclone evacuation. The Bangladesh Observer, 26th August, 2017 : 435
Devlin, G. J., McDonnell, K., & Ward, S. (2008). Timber haulage routing in Ireland: an analysis
using GIS and GPS. Journal of Transport Geography, 16(1), 63-72.
Dhar, A. S., & Ansary, M. A. (2012). Community-based evaluation for the development of asustainable disaster early warning system. J. of Emergency Management, 10.
Dube SK, Rao AD, Sinha PC, Murthy TS, Bahulayan N (1997) Storm surge in the Bay of Bengal
and Arabian Sea: the problem and its prediction. Mausam 48:283–304
Finkl CW Jr (1994) Coastal hazards perception, susceptibility and mitigation. J Coast Res. The
Coastal Education & Research Foundation (CERF), Special Issue No. 12, pp 1–372
Fritz, H. M., et al., (2007). “Hurricane Katrina storm surge distribution and field observations on theMississippi barrier islands.” Estuarine, Coastal Shelf Sci., 74(1–2), 12–20.
Fritz, H. M., et al., (2008). “Hurricane Katrina storm surge reconnaissance.” J. Geotech. Geoenviron.
Eng., 134(5), 644–656.
GOB (2008) Cyclone Sidr in Bangladesh: damage, loss, and needs assessment for disaster recovery
and reconstruction. A report prepared by the Government of Bangladesh Assisted by theInternational Development Community with Financial Support from the European Commission
Gupta, B., Gupta, S., Gupta, B., & Gupta, S. (2017) ICT Implementation-GIS Application for
Disaster Evacuation. International Journal, 3, 176-180.
Haider R, Rahman A, Huq S (1991) Cyclone’91: an environmental and perceptual study. Centre forAdvanced Studies, Bangladesh
Haque CE (1997) Hazards in a fickle environment: Bangladesh. Kluwer, The Netherlands
Haque, U., Hashizume, M., Kolivras, K. N., Overgaard, H. J., Das, B., & Yamamoto, T. (2012).
Reduced death rates from cyclones in Bangladesh: what more needs to be done?. Bulletin of theWorld Health Organization, 90, 150-156.
Hasnat,M.

M.

(2016).

Optimum

RouteModelingfor

Emergency

ResponseduringDisastrousSituation inDensely Populated Urban Area, MSc Engg. Thesis, Department of Civil
Engineering, BUET, Bangladesh.
IPCC (2001a) Climate change 2001: the scientific basis, contribution of working group-I to the IPCC
Third Assessment Report. In: Houghton et al (eds) Cambridge University Press
IPCC (2001b) Climate change 2001: impacts, adaptation and vulnerability, contribution to working
group-II to the Third Assessment Report. In: McCarthy et al (eds) Cambridge University Press

41

Irteja Hasan, Md. Sagirul Islam Majumder, Mohammad Kabirul Islam, Md. Mustafizur Rahman,
Nazmul Huq Hawlader, Israt Sultana. Assessment of Community Capacities against Cyclone
Hazard to Ensure Resilience in South Central Coastal Belt of Bangladesh. International Journalof Ecological Science and Environmental Engineering. Vol. 4, No. 1, 2017, pp. 1-14.
Islam RM (2008a) Towards institutionalization of global ICZM efforts. In: Krishnamurthy RR (ed)Integrated coastal zone management. Research Publishing Services, Singapore, p 23
Islam, A. S., Bala, S. K., Hussain, M. A., Hossain, M. A., & Rahman, M. M. (2010). Performance ofcoastal structures during Cyclone Sidr. Natural Hazards Review, 12(3), 111-116.
Khan MR, Rahman EM (2007) Partnership approach to disaster management in Bangladesh: acritical policy assessment. Nat Hazards 41(2):359–378
LGED, 2014, Environmental and Social Management Framework (Multi-Purpose Disaster Shelter
Project).
Mallick, B. (2014). Cyclone shelters and their locational suitability: An empirical analysis fromcoastal Bangladesh. Disasters, 38(3), 654-671.
Mallick, B., Rahaman, K. R., & Vogt, J. (2011). Coastal livelihood and physical infrastructure in
Bangladesh after cyclone Aila. Mitigation and Adaptation Strategies for Global Change, 16(6),629-648.
Mooley DA (1980) Severe cyclonic storms in the Bay of Bengal, 1877–1977. Mon Weather Rev
108:1647–1655
Murray-Tuite, P., & Mahmassani, H. (2003). Model of household trip-chain sequencing inemergency evacuation. Transportation Research Record: Journal of the Transportation Research
Board, (1831), 21-29.
Murty TS, El-Sabh M (1992) Mitigating the effects of storm surges generated by tropical cyclones—
a proposal. Nat Hazards 6(3):251–273
Mahmood, N. M., Prasad Dhakal, S., & Keast, R. (2014). The state of multi-purpose cyclone shelters
in Bangladesh. Facilities, 32(9/10), 522-532.
Parvin, G. A., Takahashi, F., & Shaw, R. (2008). Coastal hazards and community-coping methods in
Bangladesh. Journal of Coastal Conservation, 12(4), 181-193
Paul BK (2009a) Why relatively fewer people died? The case of Bangladesh’s cyclone Sidr. Nat
Hazards 50(2):289–304
Paul, S. K. (2014). Determinants of evacuation response to cyclone warning in coastal areas of
Bangladesh: a comparative study. Oriental Geographer, 55(1-2), 57-84.
Paul, S. K., & Routray, J. K. (2011). Household response to cyclone and induced surge in coastal
Bangladesh: coping strategies and explanatory variables. Natural Hazards, 57(2), 477-499.
42

Paul, S. K., & Routray, J. K. (2013). An analysis of the causes of non-responses to cyclone warnings
and the use of indigenous knowledge for cyclone forecasting in Bangladesh. In Climate change
and disaster risk management (pp. 15-39). Springer, Berlin, Heidelberg.
Paul, S.K., Rashid, H.M., Islam, S. and Hunt, L.M. (2010), “Cyclone evacuation in Bangladesh:tropical cyclones Gorky (1991) vs. Sidr (2007)”, Environmental Hazards, Vol. 9 No. 1, pp. 89101.
Quadir DA, Iqbal AM. Tropical cyclones: impacts on coastal livelihoods. Gland: International Union
for Conservation of Nature; 2008.
Rana, M. S., Gunasekara, K., Hazarika, M. K., Samarakoon, L., & Siddiquee, M. (2010). Applicationof remote sensing and GIS for cyclone disaster management in coastal area: A case study at
Barguna district, Bangladesh. International Archives of the Photogrammetry, Remote Sensing
and Spatial Information Science, 38(Part 8), 122-126.
The World Bank (2010), “Vulnerability of Bangladesh to cyclones in a changing climate: potential
damages and adaptation cost”, policy research working paper 5280, The World Bank,
Washington, DC.
Neralla VR. On the recurvature of tropical cyclones and the storm surge problem in Bangladesh. Nat
Hazards 1992;6:275–9. doi:10.1007/ BF00129512.
Yi, W., and Özdamar, L. (2007). A dynamic logistics coordination model for evacuation and support
in disaster response activities. European Journal of Operational Research, 179(3), 1177-1193.
Zerger A, Smith DI, Hunter GJ, Jones SD (2002) Riding the storm: a comparison of uncertainty
modeling techniques for storm surge risk management. Applied Geography, 22(3):307–330.

43

PART-III

SOFT SOIL TREATMENT WITH
PREFABRICATED VERTICAL DRAIN (PVD)
FOR THE CONSTRUCTION OF THE NEW
RAILWAY EMBANKMENT OF KASHIANIGOPALGANJ ROUTE IN BANGLADESHBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Ripon Hore
Sudipto Chakraborti
Mehedi Ahmed Ansary

44

1.

INTRODUCTION

Transportation plays a significant role in the progress of a country. In the construction of road,railway, airway, the alignment may have to be fixed through the soils which may not bear the trafficloads. In most cases, ground improvement is needed. In a broad sense, ground improvement refers to
the incorporation of different techniques employed for modifying the properties of a soil to improve
its engineering performance.
The soft Bangkok clay foundations at the site of the Second Bangkok International Airport(SBIA) have been improved using PVD (Bergado and Patawaran, 2000). Three full scale test
embankments have been constructed in stages on soft Bangkok clay with prefabricated verticaldrains (PVD) installed to 12 m depth in a square pattern. The rate and amount of settlement has been
predicted by Asaoka’s method proved to be in excellent agreement with the observed values.
Performance of an 80 km long road embankment (Bangkkok-Chonburi New Highway) has been
constructed on improved soft marine clay in Bangkok (Ruenkrairergsa et al. 2001). The site has been
evaluated during and after construction. PVDs has been associated with preloading and found to beeffective to accelerate the consolidation settlement on soft Bangkok clay. In general, soft clay
thickness has been varied from 6 m to 14 m. PVD spacing has been designed to be 1.2 m triangulargrid pattern with varied length at each contracted section. PVD have been installed for the areas
encountered with more than 6 m thick soft clay but have been limited to the maximum depth of 12 m
below the original ground.
Shen et al. (2005) has presented a case history of the performance of two full-scale test
embankments constructed on soft clay deposit in the eastern coastal region of China. One
embankment has been constructed on natural subsoil and the other has been constructed on PVD
improved subsoil. The soil profile consists of a thin weathered crust from about 1–1.5 m thickoverlying a silty clay deposit of approximately 4 m thick. The third layer is very soft clay (MC, in
China it is called mucky clay) with a thickness of approximately 10 m for the PVD-improved caseand 11.5 m for the unimproved case. Below the clay is a silty clay layer called mucky-silty clay
(MSC) approximately 4 m thick for PVD-improved case and 2.7 m for the unimproved case followed
by a medium to stiff silty clay layer from 3 to 5 m thickness extending down to 23 m depth, which isin turn underlain by a layer of loose clayey sand. The thickness of the soft clay deposit without PVDimprovement is 19 m and with PVD-improved case is 23 m. The PVDs have been installed to adepth of 19 m with spacing of 1.5 m in a triangular pattern. Field performance of the two
embankments has been analyzed using the finite element method. The analytical results show that
PVDs has increased the bulk vertical hydraulic conductivity of soft subsoil by about 30 times
compared to the original non-treated subsoil.
45

The vacuum-PVD system has been applied for soft Bangkok clay combining capped PVD withvacuum pressure and embankment loading whereby the PVD has been connected by HDPE tubes toa vacuum pipe (Saowapakpiboon et al., 2008). The soil profile at the project site (section EW-4, apart of the third runway of Suvarnabhumi International Airport, Thailand) can be divided into 8
sub-layers. It consists of a 2 m thick weathered clay layer overlying very soft layer which extends
from 2 m to 10 m depth. Underneath the soft clay layer, a 2 m thick medium clay layer has beenfound. The light brown stiff clay can be encountered at 15 m to 30 m depth. The groundwater levelhas been found at about .50 m depth. The vacuum PVD has been installed into 10 m depth withspacing of 0.85 m and arranged in a triangular pattern. The final settlement of 0.91 m to 1.21 m witha degree of consolidation of 66% to 80% has been achieved. The vacuum-PVD system has reduced
the time of consolidation by more than 50%.
The rail track of the Sandgate Rail Grade Separation Project (Sandgate between Maitland andNewcastle, in the Lower Hunter Valley of New South Wales, Australia) has been stabilised using
short PVDs in the soft subgrade soil (Indraratna et al., 2011). Site investigation included 6 boreholes,
14 piezocone (CPTU) tests, 2 in-situ vane shear tests and 2 test pits. Laboratory testing such as soil
index property testing, standard oedometer testing and vane shear testing were also performed. The
groundwater level is at the ground surface. The soil profile at the project site consists of softcompressible soil (4 m-30 m), soft residual clay, and shale bedrock. Unit weight of the soil has beenvaried from 14-16 kN/m3. Undrained shear strength has been varied from 10-40 kPa. The PVD has
been installed into 8 m depth with spacing of 2 m and arranged in a triangular pattern. The formation
has been separated into 3 distinct layers, namely ballast and fill, Soft soil-1 and Soft soil-2.
The Ballina Bypass route in Ballina (New South Wales, Australia) has been built to reduce local
traffic jams (Indraratna et al., 2012). The soil profile at the site consists of very thick clay (more than
40 m), soft silty clay layer (20 m thick, water content 80-120%) and a firm clay layer. Shear strength
has been varied between 5 and 40 kPa. To ensure ground stability and improvement, PVDs
combined with surcharge and vacuum preloading has been utilized to consolidate the soft soils
before the construction. A trial embankment has been built to the north of Ballina to evaluate theeffectiveness of the technique in this area.
The railway embankment along the alignment of the project for the construction of double line
track from Tongi to Bhairab Bazzar (including signaling on Dhaka- Chittagong main line) on soft
ground between chainage 29+300 m to 64+265 m has been completed recently (BRTC, 2013).
Depending on the soft ground treatment, 29 sections have been identified; among which in 13
sections no treatment is required, surcharge loading in 2 section, remove and replacement technique46

for 7 sections and PVD installation in 7 section (for 10.86 km) of the embankment is used. The PVD
has been installed with spacing of 1.8 m c/c and arranged in a square pattern.
North-South Expressway (NSEW) project at Dong Nai Province, Cai Mep International Terminal(CMIT) project at Baria-Vung Tau provine, and 3 test embankments (TS1, TS2 and TS3) at
Suvarnabhumi Bangkok International Airport (SBIA) project in these three projects of Thailand,
PVD with or without vacuum consolidation is used for soft soil improvement (Long, P.V. et al.
2013). In the NSEW project PVD has been installed with spacing of 0.9 m and arranged in atriangular pattern. In the CMIT project PVD has been installed with spacing of 0.9 m and 1.2 m
arranged in square pattern. In the SBIA project PVD has been installed into 12 m depth with spacingof 1.5 m, 1.2 m and 1.0 m.
Reddy et al. (2013) has proposed finite element modeling (FEM) for a portion of new road at
Kakkanad, Thankalam new road, India where ground improvement (1000m and road width isexpected to be 1.2m) has been done by preloading with PVD. Part of the road alignment goes
through low lying water logged area. The primary consolidation of soil will continue for a longer
period of time it is about 2.36 years from preliminary calculations which results in uneven settlement
and severe cracking of the pavement surface. These PVDs are placed at a distance of 1.3m spacing.
FEM analysis for the field conditions is done using PLAXIS. Using the FEM the flow of the porewater has been seen. After preloading the consolidation time has been reduced and the % reduction
in time after preloading is up to 30%. As the settlement of soil has been observed by preloading
hence the bearing capacity of the soil is improved.
Sari and Lastiasih (2014) has conducted analysis to determine the stability of the embankment byheight variation of highway embankment on soft soil. The fundamentals of preloading techniques
without and with PVD as ground improvement measures have been included. The analysis has been
performed by comparing the slope stability without PVD and with PVD where the soil subgrade
under embankment has been experiencing an increase in bearing capacity. The paper is devoted todetermine the influence in subgrade improvement on the stability of embankment using PVD.
Bo et al. (2015) has described selection of PVDs against the comprehensive specification and theselection of the PVD installation rig and accessories based on the in-situ ground conditions. The
quality management of PVD material installation in ground improvement works in land reclamation
projects is a critical task for designers, contractors and clients alike. Only if a good quality
management system is established, can the expected performance of the PVD improvement works inthe field be ensured. A case study of quality management of PVD materials in the mega Changi East
land reclamation Project in the Republic of Singapore is presented in this technical report. The

47

quality management of PVD works consisted of several processes, starting from selection of the typeof PVD, properties of the PVD materials and ultimate performance verification in the field.
PVD combined with staged construction of embankment has been considered as a common
technique to reduce both the time for full consolidation and the excess pore water pressure (Al-Soud,
2016). A 3D software has been used to analyze the embankment and the underlying soft soil which
has been chosen to be at Basra region. The numerical analysis has showed that the PVD is very
effective in accelerating the consolidation process and increasing the long term stability of theembankment. It is interesting to note that installing the PVD with spacing (1 m) leads to a rapid
dissipation of most of the excess pore water pressure at the end of each stage. The length of PVD
sheets can significantly accelerate the consolidation more than the spacing.
Recently, Bangladesh Railway (BR) has taken up a decision to construct a new 32.365 km longrailway track along Kashiani-Gopalgang-Tungipara section. Along this track the soft soil thickness
varies from 5 to 16 m. PVD in triangular pattern has been used to obtain quick consolidation
settlement due to soft soil. This paper aims to discuss on the soil profile of the Kashiani-Gopalganj
railway project, techniques used for soil improvement and presents analysis of the monitored
settlement data.
2.

PROJECT DESCRIPTION AND LOCATION

Bangladesh Railway has started construction work of a new BG track along Kashiani-Gopalganj
section. Construction of the 32.365 km railway embankment of Kashiani-Gopalganj resting on soft
soil required soil treatment, is the major challenge for this project. Project has started on 30.08.2015
and will be fully completed on 28-12-2018. The latitude and longitude of the site is between 23.20°N& 23°13′N and between 89.80°E & 89°42′E, respectively. The location of the site is presented in
Figure 1.The alignment of the site is shown in Figure 2.

Figure 1 Project locations on the map.

48

Figure 2 The alignment of Kashiani-Gopalganj section

3.

GEOLOGY OF THE SITE

The geology of the project site comprises of Deltaic silt (dsl) and Paludal Deposits (ppc).
Geologically the site comprises of light-grey to grey, fine sandy silt to clayey silt. Fine overbank
sediments deposited by distributaries in flood basins. Also this site consists of grey or bluish-grey
clay, black herbaceous peat, and yellowish-grey silt. Alternating beds of peat and peaty clay common
in bils and large structurally controlled depressions; peat is thickest in deeper parts.
4.

SUB-SOIL CONDITION AND GEOTECHNICAL PROPERTIES OF SOIL

The sub-surface investigation work includes execution of 52 borings extending to the depth of 15.0
m to 19.5 m, performance of the required field and laboratory tests, evaluation of the bearing
capacity and finally recommending for the safe and appropriate type of foundation suited to thesubsoil conditions. Boreholes have been drilled vertically using wash boring technique. Several
laboratory tests have been conducted at Geotechnical Engineering Laboratory of BUET on disturbed
and undisturbed samples of sub-soils of Kashiani-Gopalganj section on samples collected from 52
numbers of exploratory boreholes. The density and stiffness characteristics of the subsoil layers inthe boreholes have been measured by performing Standard Penetration Test (SPT). Figure 3 shows
all the borelogs along the alignment.
Thickness of soft compressible layers (silty clay, clayey Silt and fine silty sand) varies from 4.5
to 16.0 m as can be seen from Figure 3. Based on the provided elevations (RL) of existing groundand embankment top, the height of embankment fill has been found to vary from 0.9 m to 10.74 m.
Considering the variation of fill height and the thickness of soft compressible layer total length of49

track alignment has been divided into 18 sections for design of PVD spacing and stage constructionof embankments.
A detailed laboratory investigation has been carried out on disturbed and undisturbed tube
samples collected from the 52 boreholes. Liquid limit (LL), Plastic limit (PL) and Plasticity index(PI) of 25 samples retrieved from various boreholes have been determined. Besides Shrinkage limit(SL) of 12 samples and Linear shrinkage (LS) of 20 samples have been done. The values of liquid
LL of the samples varied between 31 and 94 with PI varying from 8 to 60. SL and LS of the samples
has been varied from 12% to 20% and 4% to 18% respectively. Specific gravity values of the solid
constituents of 20 samples have been determined. Values of specific gravity of the solid constituentsof sub-soil have been found to vary between 2.65 and 2.76. Organic matter content of 4 selected
samples of sub-soil has been determined. Organic matter content of the samples varied between 3.5%
and 42.7%. Unconfined compressive strength tests have been carried out on 7 undisturbed samples
obtained from different depths of various boreholes. On the basis of the value of undrained shear
strength, which is half of the unconfined compressive strength, three of the samples tested have beenfound to be very soft in consistency. The values of qu of these samples varied between 12 kN/m2 and
38 kN/m2 while the values of axial strain at failure (Ɛf) of these samples have been found to vary
between 2% and 6%. Three of the rest four samples were found to be soft to firm in consistency andvalues of qu of theses samples varied between 45 kN/m2 and 99 kN/m2. Values of Ɛf of these samples
have been found to vary between 5% and 15%. One of the samples has been found which is firm inconsistency and values of qu of two specimen of this sample are 129 kN/m2 and 159 kN/m2. The
values of Ɛf of these two specimens have been found 10% and 13%. Consolidated drained direct
shear test have been carried out on six undisturbed samples. The value of dry density for the six
samples has been varied from 6.64 kN/m3 to 15.41 kN/m3. The values of effective cohesion (c') andinternal friction (ɸ') has been varied from 0.0 kN/m2 to 16 kN/m2 and 17.2° to 43.7°. Moisturedensity relationships of three sub-soil samples obtained from pits have been determined.

50

Figure 3 Soil profile with SPT values in the 52
boreholes.
The moisture content and maximum dry density values of the samples tested varied from 20.7%
to 24.0% and 14.32 kN/m3 to 15.0 kN/m3 respectively. The values of swelling potential of laboratorycompacted pit samples has been varied from 2.0% to 4.0%. Swelling potential of twenty five sub-soil
samples has also been predicted using the expression reported by Chen (1975). The predicted value
51

has been found to vary from 0.5% to 39.0%. Swelling pressure of five undisturbed soil samples havebeen determined, where one samples is non-swelling type while swelling pressure of the rest four
samples varied from 3.4 kPa to 14.5 kPa.
By comparing the laboratory measured values of index and swelling properties of the sub-soil
samples with the various recommended criteria outlined, the relative degree of expansion of the
subsoil samples has been evaluated. Based on the values of liquid limit and plasticity index asrecommended by United States Army Engineers Waterways Experimental Station (Johnson andSnethen, 1978), the degree of expansion of samples of the samples, in general, is low to high. Basedon the values of linear shrinkage data of Bangladesh soil as recommended by Hossain (1983), thedegree of expansion of sub-soil samples is low to high. Based on the values of free swell as proposedby IS: 1948 (1970), the degree of expansion of the samples is low. Based on the values of free swell
index proposed by IS: 2911 (1980), the degree of expansion of undisturbed samples is low. Based on
the values of swelling pressure as proposed by Chen (1975), the degree of expansion of undisturbed
samples is low. Based on the values of swelling potential as recommended by Seed et al. (1962), thedegree of expansion of the laboratory compacted samples is medium. Based on the predicted valuesof swelling potential is recommended by Chen (1975), the degree of expansion of the samples, ingeneral, is low to high. On the basis of the above mentioned findings, it can be concluded that theoverall degree of expansion of the samples is low to high. Because of presence of sub-soil of highexpansion potential at different locations, modification of the sub-soil is essential to reduce thedegree of expansiveness. The upper soil layer up to depth of about 1 m, need to be stabilized before
placement of embankment fill. Although soil stabilization may be done to achieve various objectives,
the purpose of soil stabilization here is to reduce the shrinkage/expansion potential. Mixing of lime
about 4% to 5% by weight of dry in-situ soil may be adopted. Powdered quick lime (CaO) will haveto be spread over an area and then mixed with the soil by harrows. However, this will require
procurement of huge amount of lime. Alternatively granular material, such as fine sand or dredge
sand (50% to 60% of sand by weight of in-situ dry soil) may be mixed with the existing in-situ top
soil. After mixing the loosened material has to be leveled and compacted over which embankment
fill can be constructed in compacted layers. In order to assess the appropriate proportion of sand tobe mixed, a sample with relatively high linear shrinkage value has been mixed with fine sand (%finer No. 200 Sieve=4.9, Fineness Modulus, F.M. =1.17) in proportions of 20%, 40% and 60% of
dry weight of in-situ soil and linear shrinkage tests have been conducted on the three sand mixed
samples. It has been found that due to mixing of 60% of sand, the linear shrinkage has been reduced
from 18% to 12% i.e. a reduction of 33%. Therefore, it has been recommended that top 1 m of subsoil should be mixed with at least 60% by weight of fine sand (% finer No. 200 sieve ≤ 5, F.M. ≥ 1).
52

The values of compression index (Cc) of the samples have been varied between 0.1 and 0.67. Outof the 12 samples the values of Cc of nine samples are around 0.3 and above. The values of swell
index (Cs) have been varied between 0.03 and 0.14. Initial void ratio (eo) of the samples varied
between 0.76 and 1.69. Depending on the stress range, the values of coefficient of consolidation forvertical flow Cv of four samples has been varied between 1.3 m2/year and 30.0 m2/year while thevalues of coefficient of consolidation for horizontal flow (Ch) of eight samples has been varied
between 1.0 m2/year and 33.4 m2/year. Depending on the stress range and void ratio, the values ofcoefficient of vertical permeability (kv) of four samples has been varied 5.6×10-11 m/s to 8.3×10-9 m/s
while the values of coefficient of horizontal permeability (kh) of eight samples has been varied
5.3×10-11 m/s to 9.5×10-9 m/s.
5.

CONSOLIDATION SETTLEMENT

Total 32.365 km railway embankment has been divided into 18 segments according to the variationof the thickness of soft compressible layer. In the first 2.535 km thickness of soft compressible layer
is 4.5 m. 60 kPa surcharge pressure has been considered due to axle load. The considered unit
weights, initial void ratio (eo) and compression index (Cc) are 18 kN/m3, 1.25 and 0.35 respectively.
For design of subsoil improvement, average thickness of the soft compressible layer and averageheight of embankment and estimated settlements (both before and after the PVD using) along
different segments have been presented in Table 1.
6.

SOIL IMPROVEMENT

The project stretch of Kashiani-Gopalganj new Railway Line passes through mostly paddy fields andthe sub soil varies from soft silty Clay to clayey Silt up to 6m depth from OGL and beyond 6m, up to
10m depth there is very loose to loose fine sand with SPT (N) values ranging from 1 to 10. It clearly
indicates that the ground cannot bear loads arising from structure or even from embankment
formation. Further, the ground is going to experience substantial settlements due to its soft state ofcondition. The project alignment requires the construction of embankment with an average height of
5 to 8m all along the alignment. Since the soil underlying is found to be soft silty clay, ground
improvement by installation of Prefabricated Vertical Drains (PVD) also called as Band Drains/
Wick Drains followed by preloading has been proposed. In the foundation level of the railway
embankment, total 4,402,326 number of PVD has been used.
6.1 Design of PVD
Soil improvement works for eighteen segments of embankment have been considered based on theavailable geological profiles and the results of laboratory tests conducted under additional
geotechnical investigations. Design of soil improvement works involved some steps such as,
determination of maximum thickness of soft layer from the bore log data. Estimation of undrained53

shear strength of soft soil and the embankment fill. Values of undrained shear strength of soft soil
have been assumed to vary with depth. Stability analysis at different depths of a soft soil layer which
involve determination of the values of factor at different depths of the soft soil layer. A soil layer has
been assumed to be safe against bearing capacity failure when the calculated factor safety at each
depth of the soil layer is at least 1.1. If the stability of the sub-soil layer under the loading of fullembankment height and surcharge, if any is adequate, then single stage loading is decided.
Otherwise, double or multiple stages loading is proposed.
Settlements resulting from the long term consolidation of cohesive soils often create serious
problems in foundation engineering. In many cases, pre-compression of the foundation soils, byapplying a surcharge load prior to the placement of the final permanent load, has been used to reduce
settlement problems. As the consolidation process is governed by the rate of excess pore pressure
dissipation, shortening the length of pore water flow paths greatly reduces the consolidation time.
PVD are artificially created drainage paths installed for the purpose of shortening drainage paths.
Table 1 Thickness of soft compressible layer and final height of embankment and the settlements

3.
4.
5.
6.
7.
8.
9.

0.90

-

8

5.20

8.5

12

4.84

8.14

5

4.30

7.6

10

5.20

8.5

5

2.7

6.0

8

4.0

7.3

5

6.0

9.3

10

5.7

9

31.65
30.21
28.05
31.65
17.65

1057
1280
774
1193
704

22.85

54

-

-

-

1

1009

41

1

1230

48

1

724

28

1

1145

47

1.5

655

80

987

936
[2.56]

1.5

938

94

835

345
[0.94]

1.5

785

87

1219

1556

1.5

1177

123

34.85
33.65

956
[2.62]
2271[6.22]
336[0.92]
1547[4.23]
323[0.88]

Settlements
Using PVD, Sc(mm)
Required Timeusing PVD
(Days)

Embankment(m)
HeightIncluding
Surcharge
Load (m)Required Base
Width (m)Predicted
Settlements, Sc(mm)

Thickness ofSoft
Compressible
Layer(m) of
Final
Height
4.5

PVD Spacingin Triangular
Pattern(m)
Predicted

2.

0+500 to
2+535
2+535 to
5+000
5+000 to
7+000
7+000 to
8+000
8+000 to
9+575
9+575 to11+00011+000to
12+00012+000to
13+50013+500Required Time(Days)[Year]

1.

Chainage (km)Segment(mm) for different segments of Kashiani-Gopalganj embankment

10.
11.
12.
13.
14.
15.
16.
17.
18.

to
16+50016+500to
18+00018+000to
20+50020+500to
22+00022+000to
23+50023+500to
25+50025+500to
27+00027+000to
28+50028+500to
30+50030+500to
32+365[4.26]
32.85
5

5.5

8.8

6

6.84

10.14

818

343
[0.94]

1.5

770

84

968

523
[1.43]

1.5

918

89

1208

125
[0.35]

1.5

1158

116

805

341
[0.93]

1.5

756

84

1254

1568
[4.3]

1.5

1204

121

1046

729 [2]

1.5

998

111

1454

1618
[4.45]

1.0

1406

59

1929

4418
[12.1]

1.0

1881

89

1345

1932
[5.3]

1.5

1296

133

38.21
37.25
9

6.6

9.9
31.37

5

5.13

8.43

10

6.16

9.46

7

6.68

9.98

10

10.0

13.3

16

10.74

14.04

11

6.56

9.86

35.49
37.57
54.85
57.81
37.09

PVD are used to improve the ability of the soil to support construction and accelerate the rate ofsettlement. Most soil deposits have greater permeability in the horizontal direction than in thevertical direction. Therefore, PVD help in accelerating the rate of consolidation settlement. Becauseof their ease of installation, flexibility, reliability and lower cost, PVD have almost totally replaced
conventional sand drains for a majority of application. The center to center spacing (S) between
drains are S=D/1.05 (for triangular spacing) and D/1.13 (for rectangular spacing) respectively. Here
D is the diameter of cylinder of influence for the drains. In designing PVD, the value ofconsolidation co-efficient for horizontal (ch) flow can be estimated 2 to 3 times the value ofconsolidation co-efficient for vertical flow (cv). The values of ch are sometime considerably larger
than cv, particularly in sedimentary deposits of a continuous nature. For design purposes, it isreasonable to calculate the equivalent diameter (d=2(a+b)/π), where a is drain width and b is thedrain thickness (Hansbo, 1979).

55

Design of soil improvement works has been carried out for the seventeen soft ground segmentsof embankment from Kashiani to Gopalganj on the basis of detailed sub-soil investigation. Besides
laboratory test results on samples collected from 52 boreholes have been also considered. In
segment-I (chainage 0+550 to 2+535 km) sub-soil improvement is not to be considered. A summaryof the thickness of soft sub-soil layers at different segments of embankment and height ofembankment fill has been presented in Table 1. The values of different soil parameters, embankment
and PVD dimensions and criteria and criteria have been adopted for PVD design. Axle load, slide
slope, berm width and crest width of embankment are 60 kPa, 2 m, 2 m and 6.85 m respectively. The
value of undrained shear strength of embankment fill (Cm), undrained angle of internal friction (ɸm),
unit weight of fill (ɣ) and CT has been considered 0 kN/m2, 35°, 18 kN/m3 and 12 kN/m2 respectively.
Again the value of ΔCT and Dc has been considered 0. The values of initial void ratio of soft sub-soil
layer (eo), compression Index (Cc), coefficient of consolidation for vertical flow (cv), coefficient ofconsolidation for horizontal flow (ch) and well resistance (kh/qw =ratio of horizontal permeability ofthe soft sub-soil and discharge capacity of the PVD) has been adopted 1.25, 0.35, 7 m2/yr, 14 m2/yr
and 0.01 respectively. The width and thickness of PVD and allowable settlement of embankment
after improvement are 100 mm, 3.2 mm and 50 mm respectively. Desire degree of consolidation invarious stages has adopted as 80%-90%. The value of angle of friction (ɸ) of sub-soil for calculationof ΔCu after each stage loading is 15°. Minimum factor of safety (FS) for stability against foundation
failure for each stage loading has been considered as 1.1. Embankment height has been varied 4 m to
8 m and 8 m to 12 m for one and two number of berm on each side. In all the analysis, the soil has
been assumed to be normally consolidated due to the presence of very soft sub-soil deposits.
Additional surcharge height, equivalent to the design axle load of 60 kPa has been considered in
the design to reduce future potential settlements under service conditions. This additional surchargeheight is taken as 3.3 m (= 60 kPa/ 18 kN/m3) of fill height. Fill height in various stages isdetermined with consideration to ensuring a minimum factor of safety of 1.1 against stability failure.
Estimated total consideration settlement at center of embankment under design height and axle load
at various segments is found to vary from about 700 mm to 1929 mm. in the analyses it is consideredthat 50 mm of settlement of the embankment may be allowed to occur in future, and the rest amountof the total consolidation settlement shall have to be completed at the end of the sub-soil
improvement and before placement of the railway track. A summary of the analyses containing
estimated total settlement and required time before and after PVD has been used; PVD spacing etc.
has been presented in Table 1.
In segment-2 to segment-5 (Chainage 2+535 km to 9+575 km) PVD has already been installed at
1 m spacing in a triangular pattern, as such in the analysis for that stretch of the embankment, stage
56

construction and waiting time for 1 m PVD spacing has been assessed as shown in the Figure 5.
Figure 4 shows PVD layout plan at chainage 2+529 km to 2+600 km.

It has been found from analysis that three-stage loading with waiting times is necessary to ensure
foundation stability. Total waiting times for embankment loading in three stages are found to be in
the range of 28 days to 48 days.
For segment-6 to segment-15 (Chainage: 9+575 km to 27+000 km) and segment-18 (Chainage:
30+500 km to 32+365 km), it is found that construction of the embankment shall have to be done inthree stages to avoid stability failure. For these segments, minimum waiting times for the three stageof construction have been calculated for PVD spacing of 1.5 m and 2.0 m. It is found that total
waiting time for the three stages vary from 80 days to 133 days for PVD spacing of 1.5 m has beenpresented in Table 1. For PVD spacing of 2.0 m, the total waiting time for the three stages vary from
159 days to 259 days has been presented in Table 1.
In segment-16 (Chainage: 27+000 to 28+500 km) and segment-17 (Chainage: 28+500 to 30+500km), both embankment height and compressible soft layer thickness is large. Analysis show that forthese segments four stage construction is required with appropriate waiting time in each stage toensure foundation stability. For these segments, minimum waiting times for the four stages of
construction have been calculated for PVD spacing of 1.0 m and 1.5 m. It is found that total waitingtime for the four stages vary from 59 days to 89 days for PVD spacing of 1.0 m has been presented inTable 1. Whereas for PVD spacing of 1.5 m, the total waiting time for the four stages vary from 155
days to 221 days has been presented in Table 1.
It is to be noted that, in addition to the waiting times for different stages, additional duration isrequired to attain the fill heights in stages and removal of additional surcharge. While placing the fill,proper compaction has to be ensured up to the design height of the embankment in the respective
segment. The rest of the fill height can be placed without compaction. It is also to be noted that,
required base widths for placement of fill up to the surcharge height, with a side slope of 1 (V): 2 (H)is larger than the design base width of the embankment sections. The required base widths are shown57

in Table 1. The fill for surcharge loading and additional material on sides will have to be removed
after desired improvement has been occurred. Predicted settlements at the end of each stage load andwaiting time have shown in Table 1. However, actual waiting times for the different stages will begoverned by field monitoring of settlement using settlement places. In addition to settlement plates,alignment stakes shall have to be placed at specified locations to monitor any unusual lateraldisplacement of the foundation soil.
6.2 Construction of PVD
Prefabricated Vertical Drain (PVD) shall be installed with approved hydraulic stitcher (shows in
Figure 9 a. and b.) which will cause a minimum of disturbance to the soil during installation andmaintain the mandrel in a vertical position as Figure 9 c) shows the installed PVD without
disturbance to the soil. The PVD's shall be installed using a mandrel or sleeve that will be advanced
through the compressible soils to the required depth using constant load, or constant rate ofadvancement. There are some steps which have been followed for the improvement of sub-soils. The
existing ground surface has been prepared by stripping of top vegetation and grubbing and levelingat the desired elevation. After clearing the surface soil including vegetation and vegetation roots upto
a depth of 150 mm no separate payment shall for clearing of vegetation and vegetation roots fromsurface soil, a sand blanket of medium to coarse sand of 500 mm compacted thickness has been laid
over the top of compacted formation and should project at least 150 mm above NGL and 5.0 m on all
sides as shown in the Figure 9 d). The blanket has been spread over the entire area of treatment. In
case any pits are observed, the same shall also be filled with sand. The blanket has been laid in layersof 175 mm (maximum compacted thickness) and compacted to 95% Relative Density by means ofvibratory rollers as shown in the Figure 9 e).
In order to remove the water from consolidating system, proper drainage arrangement including
pits, channels and pumping arrangement has been provided. 200 mm of thick compacted layer ofsand (F.M. ≥ 1.0, % Finer No. 200 Sieve ≤ 5, Relative Density, Dr ≥ 70%) has been placed. First
layer of 200 mm thick compacted coarse sand layer (F.M. ≥ 2.2, % Finer No. 200 Sieve ≤ 3, Relative
Density, Dr ≥ 70%) has also been placed as shown in Figure 6. PVD have been installed at specified
spacing. In areas where wind is prevalent, fabric installation has been started at the upwind side of
the project and proceeds downwind. The leading edge of the fabric has been secured at all times withsandbags or other means sufficient to hold it down during high winds. Sandbags or rubber tires havebeen used as required to hold the fabric in position during installation. Tires shall not have exposed
steel cords or other sharp edges may snag or cut the fabric. Materials equipment or other items shallnot be dragged across the fabric or be allowed to slide down slopes on the fabric. Should the fabric
be damaged during any step of the installation, the damaged section has been repaired by covering it
58

with a piece of fabric. Installed Geo-textile has been covered by preloading fill. Surcharge shall havebeen used as preloading surcharge. The material shall be earth locally available and fit for earth
filling purposes. The earth shall have been spread in layers not more than 250 mm compacted
thickness. Earth-fill below designed formation level and up to 500mm above the designed formationlevel and shall be compacted up to 95% Proctor density/80% relative density as applicable.
Remaining height of Earth fill shall has been compacted to 80% Proctor density 60% relative
density. Now non-woven needle punched geotextile fabric has been placed over the first coarse sandlayer as shown in Figure 7. Second layer of 300 mm thick compacted coarse sand layer (F.M. ≥ 2.2,
% Finer No. 200 Sieve ≤ 3, Relative Density, Dr ≥ 70%) has also been placed as shown in Figure 6.

Figure 5 Figure shows the PVD grid and typical cross section of
PVD drains.

Figure 6: sectional elevation showing ground improvement byinstalling PVD at chainage-02+529-8+000

The settlement plates has been allocated with their base at the top of the second coarse sand layer
to monitor the vertical settlement of sub-soil. Then embankment fill has been positioned. Alignment
stakes at and beyond the embankment toes has been inserted to monitor the horizontal movement ofground.

Figure 7: Cross-section of embankment showing sub-soil
improvement scheme.

59Settlement of the fill has been monitored (record RL) at least twice a week. After the required
maintenance period of preloading and surcharging, the surcharge has been removed and constructionof the railway track has been started. Figure 7 shows the cross-section of embankment showing the
proposed sub-soil improvement scheme while Figure 8 shows plan and section of embankment
showing the locations of the settlement plates and alignment stakes.

Figure 8: Plan and section of embankment showing the locationsof settlement plates and alignment stakes.

Needle-punched geotextile has been used for PVD construction. The specification of non-woven
needle-punched geotextile is given here. The values of mass per unit area (ASTM D 5261), thickness(under a pressure of 2 kPa, ASTM D 5199), apparent/ Effective opening size (AOS/EOS, ASTM D
4751) and Vertical permeability at 20°C (UNDER 2 kPa normal stress, DIN) has been estimated as≥ 400 gm/m2, ≥ 3.5 mm, < 75 μm and ≥ 3.0×10-3 m/s respectively. The values of horizontal
permeability (ASTM D 4716), grab tensile strength (ASTM D 4632), grab tensile elongation (ASTM
D 4632), wide width tensile strength (ASTM D 4595), wide width tensile elongation (ASTM D
4595) and CBR puncture resistance (ASTM D 6241) at 20°C has been tested as ≥ 3.0×10-3 m/s, ≥1500 N, ≥ 85%, ≥ 20 kN/m, ≥ 80% and ≥ 3500 respectively. Figure 9 f) shows the completed rail
way track.
7.

MONITORING OF SETTLEMENTS

Ground settlement has been monitored using the settlement plates at seven chainage locations (up to
15 km) during consolidation under embankment load to validate the design assumption and to ensure
pre-consolidation before construction of PVD. Practically in the field it is a challenging and toughjob to manage the application of surcharge load which has been considered during design phase.
During implementation no surcharge (60 kPa) has been placed, only embankment load has been usedas preloads. The required time for settlement is much higher than the time estimated in the design60phase. Settlement monitoring has been started immediately after placement of the materials to theembankment height and continued until consolidation has been completed.

Figure 9: a) and b) shows the installation of PVD, c) shows theinstalled PVD, d) shows first layer of sand blanket spread over
the top, e) shows sand blanket is compacted with vibratoryroller and f) shows the completed railway track.

Settlement versus time curve of these chainage points have been presented at Figure 10. At
chainage point 3+800 km, 486 mm settlement in 256 days has been observed at the end of first stage
and 657 mm settlement has been observed at 376 days (which has been monitored until 525 days) at
the end of second stage. At chainage point 5+050 km, first and second stages have been completedwith 490 mm settlement in 220 days and 730 mm settlement in 360 days (which has been monitored
until 560 days), respectively.
At chainage point 5+300 km, first and second stages have been completed with 470 mm
settlement in 220 days and 731 mm settlement in 340 days (which has been monitored until 490
days), respectively. At chainage point 5+800 km, first and second stages have been completed with
475 mm settlement in 225 days and 644 mm settlement in 360 days (which has been monitored until
500 days), respectively. At chainage point 6+300 km, first and second stages have been completedwith 441 mm settlement in 225 days and 638 mm settlement in 365 days (which has been monitored
until 550 days), respectively.
At chainage point 8+300 km, first and second stages have been completed with 361 mm
settlement in 180 days and 626 mm settlement in 360 days (which has been monitored until 500
days), respectively. At chainage point 11+800 km, first and second stages have been completed with
399 mm settlement in 190 days and 524 mm settlement in 385 days (which has been monitored until
530 days), respectively.
Predicted and observed settlement with percentage of error in prediction for specific sevenchainage points have been presented at Table 2.

61

Table 2 Thickness of soft compressible layer, Predicted and Observed Settlement (mm) for different

Predictiot (mm)
inSettlemen

Observedt (mm)

Settlemen

Predicted
e LoadSurchargible
W layerou

compress(km)s of soft

Chainagechainage of Kashiani-Gopalganj embankment

3+800

7.5

5.4

629

657

4.5

5+050

4.5

5.9

683

730

6.9

5+300

10.5

5.3

721

731

1.38

5+800

3

5.6

529

644

21.8

6+300

3

5.4

521

638

22.4

8+300

4.5

4.8

621

626

0.81

11+800

3

4

459

524

14.16

At all the seven chainage points, predicted settlement without using surcharge is less than theobserved settlement. Currently, construction work of 15 km of railway embankment has been
completed and rest 17.365 km railway embankment is under construction stage.

8.

CONCLUSIONS

The application of prefabricated vertical drain (PVD) technique, as a soil stabilization method has
been presented in this paper. Regarding the time consumption aspects and value of required work,
soil strengthening by forming PVD is a more preferable technique. In this project 4,402,326 numberof PVD has been installed at 1 m to 1.5 m center to center spacing in triangular pattern up to a depthof 16 m. Initially, 32.365 km railway embankment has been divided into 18 segments. Predicted
settlement with using a surcharge load of 60 kPa has varied between 704 mm and 1929 mm. The
predicted time required for the above settlement has varied between 0.88 years to 12 years. During
implementation of the initial 15 km out of the total 32.365 km of the embankment, surcharge load
(60 kPa) has not been placed due to some construction related issue. For this reason, the measured
settlement time needed is higher than predicted during the design phase. At seven chainage locations
along the 15 km already constructed embankment, settlements have been monitored. The predicted
and observed settlements at those points are almost same. The concept of the soil improvement using
PVD beneath the railway embankment is not only cost-effective but also safe, reliable and timesaving as shown through the success of the project.

62

9.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the support from Bangladesh Railway for allowing them to
use the field and other relevant data of the New Railway Embankment of Kashiani-Gopalganj Routeproject.

10. REFERENCES
Al-Soud, M. S. (2016). Numerical Analysis of Prefabricated Vertical Drains Improved Soft Soil
beneath an Embankment During Staged Construction. Journal of Engineering and Development
Vol, 20(1). pp. 151-163.
Bergado, D. T., and Patawaran, M. A. B. (2000). Recent developments of ground improvement with
pvd on soft Bangkok clay. In Proc. Intl. seminar on Geotechnics in Kochi 2000.
BRTC (Bureau of Research, Testing and Consultation), (2013) "Report on Review of Design Reportfor the Treatment of Soft Soil Areas of Embankment for the Proposed Construction of Double
Line Track from Tongi to Bairab Bazar including Signalling on Dhaka-Chittagong Main Line",
Department of Civil Engineering, April 2013, Bangladesh University of Engineering andTechnology, Dhaka.
Bo, M. W., Arulrajah, A., Horpibulsuk, S., & Leong, M. (2015). Quality management ofprefabricated vertical drain materials in mega land reclamation projects: A case study. Soils andFoundations, 55(4), pp. 895-905.
BRTC (Bureau of Research, Testing and Consultation), (2016) "Report on Design of Foundation
Treatment, Preloading with Staged Construction and Monitoring Scheme for the Constructionof Railway Embankment from Chainage 0+550 km to 32+365 km of Kashiani-Gopalganj
Section", Department of Civil Engineering, Bangladesh University of Engineering andTechnology, Dhaka.
Chen, F. H. (1975). ''Foundation on Expansive Soils''. Development in Geotechnical Engineering
Vol. 12, First Edition, Elseveir Science Publishers B. V., The Netherlands.
Hansbo, S. (1979). Consolidation of Clay by Band Shaped Prefabricated Drains. GroundEngineering. 12 (5): pp. 16-25.
Hossain, M. (1983). Swelling properties of selected local soils. Unpublished M.Sc. Thesis, BUET,
Dhaka, Bangladesh, pp. 1-168.
Indraratna, B.,

Rujikiatkamjorn, C.,

Vinod, J. S.,

and Nimbalkar, S., (2011) "Physical andChemical Ground Improvement For Sustainable Transportation Infrastructure under Cyclic
Loads", Centre for Geomechanics and Railway Engineering, School of Civil, Mining and63

Environmental Engineering, University of Wollongong, Wollongong City, NSW 2522,
Australia, pp. 140-156.
Indraratna, B., Rujikiatkamjorn, C., Kelly, R., Buys, H., (2012) "Soft Soil Foundation Improved by
Vacuum and Surcharge Loading", Ground Improv. 165 (G12), pp. 87–96. Long, P.V., Bergado,
D.T., Nguyem, L.V. and Balasubramanium, A.S., (2013) "Design and Performance of SoftGround Improvement Using PVD With and Without Vacuum Consolidation", GeotechnicalEngineering Journal of the EAGS and AGSSEA, Vol. 44, No.4, December 2013, ISSN 00465828, pp. 36-51.
Ruenkrairergsa, T., Lin, P. C., and Sunantapongsak, S. (2001). Performance of PVD roadembankment on soft Bangkok clay. In Proceedings of The International Conference on Soil
Mechanics and Geotechnical Engineering (Vol. 3, pp. 2179-2182). Aa Balkema Publishers.
Reddy, S. K., Moosani and Divya, P.V. (2013). Improving Engineering Properties of Soft Soil Using
Preloading and Prefabricated Vertical Drains. International Journal of Research in Civil
Engineering, Architecture & Design Volume 1, Issue 2, ISSN Online: 2347-2855, Print: 23478284, pp. 08-12.
Seed, H. B., Woodward, R. J., Jr., and Lundgren, R., (1962) "Prediction of Swelling Potential for
Compacted Clays," Journal, Soil Mechanics and Foundations Division, American Society of
Civil Engineers (ASCE), Vol 88, No. SM3, June, pp. 53-87.
Shen, S. L., Chai, J. C., Hong, Z. S., and Cai, F. X. (2005). Analysis of field performance ofembankments on soft clay deposit with and without PVD-improvement. Geotextiles andGeomembranes, 23(6), pp. 463-485.
Saowapakpiboon, J., Bergado, D. T., Chai, J. C., Kovittayanon, N., and De Zwart, T. P. (2008).
Vacuum-PVD combination with embankment loading consolidation in soft Bangkok clay: acase study of the Suvarnabhumi Airport Project. In Geosynthetics in Civil and Environmental
Engineering. Springer, Berlin, Heidelberg, pp. 440-449.
Sari, P. T. K., and Lastiasih, Y. (2014). Slope Stability of Road Embankment on Soft Soil. Celebes
International Conference on Earth Science (CICES). http://www.uho.ac.id/cices2014. pp. 1-11.
Johnson, L. T., and Snethen, D. R. (1978). Prediction of potential heave of swelling soil.
Geotechnical Testing Journal, Vol. 1, No. 3, https://doi.org/10.1520/GTJ10382J. ISSN 01496115. pp. 117-124.

64

Figure 10 Settlement versus time curve at eight chainage points.

65

PART-IV

INTER-ORGANIZATIONAL RESPONSE TOCYCLONE SIDR (2007): A NETWORK BASEDAPPROACH
BANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Tasnim Tarannum Isaba
Mehedi Ahmed Ansary66

1.Introduction
Natural disasters such as cyclone, flood, earthquake etc. cause immense destruction every year
around the world. Apart from causalities, such events hamper the lifeline system and cause
widespread loss of life and property by damaging social and physical infrastructures [1]. In all cases,
a series of actions is required to address such catastrophic consequences and overcome the effects ofdestruction. While it may be impossible to completely prevent the occurrence of catastrophe,
properly planned measures starting from preparedness to response and rehabilitation can minimize
the anticipated loss [2].
Bangladesh is one of the most disaster-prone countries in the world due to its geographical location,soil characteristics, diversity of rivers and monsoon climate. It is not surprising that the country has along history of cyclones, landslides, tornadoes and is under the threat of earthquake [3]. With Bay of
Bengal at its southern border, Bangladesh is particularly vulnerable to tropical cyclones and from
1584 to 1991, about 53% of the world deaths from these cyclones have taken place in Bangladesh.
Studies regarding climate change argue that global warming can induce the occurrences of cyclone
and the situation in Bangladesh is likely to worsen [4].
Cyclone Sidr (a category IV storm on the Saffir-Simpson hurricane scale of I to V) is one of the mostdevastating cyclones in the history of Bangladesh. Being originated from a depression in Bay of
Bengal, the cyclone has swept through the southwestern coast of Bangladesh on 15 November, 2007
with a diameter of nearly 1000 km and sustained winds of up to 240 km per hour. According to
official reports, about 27 million people from 30 districts have been affected by the cyclone with adeath toll of 3,406 and estimated loss of $1.3 billion. Storm surge with a maximum height of 20 feetin certain areas has caused immense physical destruction, damage of crops, livestock and flooding of
low lying areas. Mainly the poverty-prone districts of the country have been affected by the cyclone[1,3,5]. Although the death toll is significantly less compared to previous cyclones that hit
Bangladesh, Sidr has caused extensive damage to properties. The scale and devastation of thecyclone have created dynamics that are impossible to be addressed by any single organization or
jurisdiction area. In addition to national assistance directed towards the affected people, the cyclone
has resulted in a global response with supports from various international donors and development
partners [3].
The term “network” is defined as- “group of individuals or organizations who, on a voluntary basis,exchange information and undertake joint activities and organize themselves in such a way that theirindividual autonomy remains intact”. The term is used to describe the multiple organizations
engaged in interactions involving multiple nodes [6]. After catastrophic events the formal structureof operation, including the functioning of existing support networks, communication networks and67

other infrastructures are often disrupted as seen from previous incidents of South-Asia Tsunami(2004), Hurricane Katrina (2005), the Great Japan Earthquake (2011) etc. [7]. At times of
emergencies, a collective action is required among public, private and non-profit organizations
towards mitigating the adverse consequences [8,9]. Emergency management organizations, bothgovernmental and non-governmental, look to one another for topical information and practicalguidance [10,11]. Inter-organizational networks in emergencies play a vital role in facilitating theflow of information across organizational boundaries [6]. Therefore, network analysis can be
conducted to determine the pattern of relationship among different organizations that interacted
within a disaster response system. Social network analysis is a well-developed area of organizationalsociology which provides tools and concepts for analyzing organizations as networks. The basic idea
is to consider the organizations as “nodes” and the relationship between them as “ties”, forming a
network-like structure [12,13].
In practice, natural disasters result in a number of complexities that may exceed what has been
anticipated by national policies and existing planning process. Careful examination of organizational
network following a disaster is vital to determine the discrepancies between disaster management
plans and actual performance under the stress of a catastrophe. Effective response and recovery
require well-coordinated interorganizational networks between government agencies at all levels andbetween the public and private sectors [6]. Inter-organizational network analysis after extreme events
may further assist in exploring ways to ensure the optimum utilization of the capabilities of these
networks [14]. Although existing disaster management policies of Bangladesh, including “Standing
Order on Disaster” have declared the responsibilities of each organization related to disastermanagement, the involvement of large number of non-governmental actors after a disaster may
create complexities beyond the comprehension of government [15,16]. The governmental report“Super Cyclone Sidr 2007: Impacts and Strategies for Interventions” has revealed a number oflessons stated as learned or observed from the response system of cyclone Sidr. Many of the lessons
are somehow related with interorganizational response issues [3].
Considering the vulnerability of Bangladesh to tropical cyclones and the future probability ofcyclones in the country, the inter-organizational relationships that emerge in response to cyclones
must be thought with great importance. Understanding the formation of response network and the
key organizations related to the network is urgent for the policy makers in order to develop a disastermanagement system that can better respond to the changing environment and leverage the efforts of
the organizations to collectively achieve the common goal [9,17].
2.Interorganizational Network Analysis in Catastrophic Events68Recently there has been growing literature on the importance of network analysis and its applicationin disaster research. Research works have addressed issues of communication networks during
disaster [18-21], importance of social networks on disaster relief management [22-23] and interorganizational coordination in disaster response systems [24-26].
The inter-organizational response to major catastrophic events has been recognized by many
researchers. Kapucu (2005) has explored the inter-organizational network in response to World
Trade Center attack (2001) and found that interactions among the organizations are limited and haveprimarily occurred between organizations of similar type from the same jurisdiction. FEMA and
New York City Government/Mayor have been identified as most influential in response operations[6]. Findings from Hurricane Katrina (2005) have revealed a moderate level of interaction among
the responding actors. The inter-organizational network of the hurricane involves smaller portion of
non-profit organizations while major share of response network belongs to the public organizations[14,27]. An inter-organizational network study on ChiChi Earthquake (1999) and Typhoon Marakot(2009) in Taiwan has revealed that, public organizations have possessed the central role in disaster
response system whereas non-profit and private organizations have worked with their own closegroups instead of connecting with the major response network. According to the study, major
proportion of the transactions has been involved in emergency response, damage assessment andfund-raising activities [9]. In case of response to Haiti earthquake (2010), there is large number ofisolates in the response network that are not connected to any other organizations. Water, Sanitationand Hygiene (WASH) and UNICEF, closely followed by World Food Programme (WFP) are among
the top key entities [28]. In Tohoku-Oki disasters of Japan (2011) it has been seen that, in contrast to
the plans which have designated primary responsibilities in disaster response to the publicauthorities, private and non-profit organizations have consisted nearly half of the organizations
responding to the massive event. The overall as well as separate centralization measures of the
networks are very low which indicates a highly-decentralized network of response organizations
during first three weeks of the disasters [29,30]. Social network analysis of Australian floods (20102011) has identified local authorities, political personalities, social media volunteers, media
reporters, and people from non-profit, humanitarian, and community associations as effective players
during the Queensland flood [31]. Results from a Twitter based emergency management-relatedorganizational analysis of Colorado Wildfire (2017) have denoted that the federal agencies like
FEMA, Centers for Disease Control and Prevention, Department of Homeland Security, etc. areamong the most important players in this network [7]. Although these studies may differ in terms of
their methodology and coverage, findings from the studies have signified the fact that networks

69

emerging in response to disasters consist of a large number of organizations varying in their scale,size, extent and scope.
This research discusses the identification of most influential actors in the context of networks oforganizations in response to cyclone Sidr (2007). Specifically, this paper examines the interactions
among organizations engaged in responding to cyclone Sidr. An important feature of the research isto revise the disaster management system in Bangladesh and determine the degree to which actual
condition corresponds to the stated policies while interacting with hazards. By exploring suchinteractions among organizations, this research would be able to recommend which aspects of theplanning process could be changed in order to enable communities to respond effectively tocatastrophic events.
3.Initial Condition of Disaster Risk Reduction in Bangladesh
Natural disasters cause great economic loss every year in Bangladesh. About 14% of GDP in
Bangladesh is vulnerable to natural disasters, while every year the country faces 1.8% of GDP loss
due to natural disasters [32]. Disaster management in Bangladesh has gone through significant
reform since independence in 1971. Since the devastating flood in 1988 and cyclone in 1991, theconventional disaster management model involving emergency response has been converted into a
more holistic model, which includes hazard identification and mitigation, community preparedness
and integrated response efforts [15,33].
Because of the unique geographical condition, cyclonic events are considered as regular catastrophic
phenomena along the coastal areas of Bangladesh, affecting at least 15 districts and hundreds of offshore islands. As seen from Table 1, from 1960 to 1965, five severe cyclones hit the coastal belt of
Bangladesh killing approximately 50,000 people [34].
Table 1: Major cyclone events in Bangladesh since 1960 (Source: GoB, 2013)

Year

Month

Human death

1960

October

5149

1961

May

11466

1963

May

11520

1965

May

19276

1965

December-

1966

October

850

1970

November

500000

1985

May

11069
70

1991

April

138000

1994

May

170

1997

May

126

2007

November

3363

2009

May

190

2013

May

17

Considering the emerging disaster risk in Bangladesh, the country is internationally committed to
Hyogo Framework for Action 2005-2015 (HFA) for pursuing an intergraded multi-hazard approachby placing disaster risk at the center of political priorities and policies. Bangladesh is also an active
member of South Asian Association for Regional Cooperation (SAARC) which adopted acomprehensive framework on disaster management at the 13th SAARC Summit held in Dhaka(2006-2015) titled as “Disaster Management in South Asia: A Comprehensive Regional Framework
for Action”. Strategic goals of the framework include mainstreaming of disaster risk reduction,
strengthening emergency response system and developing networks of relevant national, regional
and international organizations. Bangladesh has also contributed to the formation and adoption ofComprehensive Framework on Early Warning, Disaster Management and Disaster Prevention in2007 [15,35].
As implementation measure for both HFA and SAARC framework, Bangladesh has developed
National Plan for Disaster Management (NPDM) (2010-2015) which provides a simple model toFigure 1: Institutions of disaster management in
Bangladesh (Source: GoB, 2010)

guide disaster management and emergency response efforts in Bangladesh. Currently Disaster71

Management Act of 2012, SODs and other DRM-related laws and codes constitute the central of
Bangladesh national disaster management regulatory framework. Disaster Management Act (2012)provides the legal mandates for government agencies and non-government actors. Standing Order on
Disasters (SOD), first published in 1999 and revised in 2010, clearly outlines the responsibilities ofpublic representatives, ministries, agencies and non-governmental organizations related to disastermanagement [15,35].
4.Institutional Framework and Preparedness for Cyclone in Bangladesh
The consistent occurrences of tropical cyclones in Bangladesh made preparedness for cyclone anecessity for the country. The organizational framework dealing with cyclone hazard in Bangladesh
can be divided into two levels- national and local. Government has established an elaborate disastermanagement committee that ranges from National Disaster Management Council to Union Disaster
Management Committee as shown in Figure 1.
National Disaster Management Council (NDMC) is the highest committee to deal with crisis and is
responsible for policy making and formulation of strategies for disaster management. Ministry ofFood and Disaster Management (MoFDM) is the main national agency for disaster risk reduction. Its
focal office for disaster risk management is Department of Disaster Management (DDM). MoFDM
works with other ministries that have disaster management related mandates whereas DDM works
with local government bodies [15,35].

In April 1993, government has established Disaster

Management Bureau (DMB) which coordinates and overviews all activities related to disastermanagement from national to grass-root level, raise awareness regarding hazards and work in close
collaboration with district and upazila level authorities. As continuation of the paradigm shift process
in disaster management, Comprehensive Disaster Management Programme (CDMP) has been
established in November, 2003 for optimizations of long-term risk and strengthening operational
capacities for responding to emergencies [33]. In case of weather forecasting, Bangladesh
Meteorological Department (BMD) is responsible for the monitoring of sea condition and provisionof weather information to concerned authorities [15]. Armed Forces Division (AFD) plays a vitalrole in disaster management in Bangladesh. According to SOD, as per the requisition of MoFDM attimes of crisis, AFD coordinates the overall rescue and relief operation. Army, Navy and Air Force
together ensure early recovery, relief and rehabilitation work as decided by the government at timesof disaster [15,36].
At local level, Deputy Commissioner (DC), Upazila Nirbahi Officer (UNO) and Union Parishad(UP) Chairman are responsible for the operation and coordination of relief and recovery activities incyclone affected districts, upazila and union respectively. The Cyclone Preparedness Porgramme(CPP) established in 1972 is a good example of cyclone preparedness, warning dissemination and72

response system in Bangladesh. Awareness for cyclone preparedness activities emerged when
Bangladesh Red Crescent Society (BDRCS) requested the International Federation (IFRC) to support
the establishment of warning system for the people of coastal Bangladesh. In 1966, IFRC and Swiss
Red Cross initiated the implementation of a pilot scheme through the provision of warning
equipment such as transistors, sirens etc. and training of local personnel. Following the devastating
cyclone in 1970, IFRC and BDRCS conducted an extreme evaluation of CPP which lead to a newprogramme consisting of 20,310 volunteers 24 thanas. In 1973, CPP became a government
programme with joint venture of government and BDRCS [33, 37-39].
Currently the Cyclone Preparedness Programme (CPP) is organized in 258 unions of 30 thanas
divided into 2733 units of Bangladesh. CPP volunteers are trained in early warning dissemination,
evacuation, search and rescue, first aid and relief operations. Their capacity development process
also includes basic disaster management and leadership training. In 2007, CPP volunteers haveguided almost 1.5 million people to cyclone shelters when cyclone Sidr hit Bangladesh [39].
5. Response Process to Cyclone and Key Participants
In turbulent or complex situations, organizations frequently develop formal or informal relationships
in order to work together and respond to the environment [6]. Responders to disasters can generally
be classified into four groups: governmental agencies, volunteer organizations, private sector andindividual citizens. The central responsibility for planning and performing response operations
during extreme events is mainly assigned to governmental agencies (including military) at local, stateand federal levels. Volunteer or non-profit organizations, mainly engaged in basic human andmedical services, can range from local civic groups to large international organizations. Private
sector ranging from small business groups to multi-national corporations can make contributions
through donation or provision of professional services in specialized sectors such as information andlogistics management [40].
In Bangladesh, SOD includes the responsibilities assigned for governmental agencies including
ministries, Armed Forces Division (AFD), cabinet etc. at central level and district administrationcommittee at local level. Responsibilities are described for normal times, alert and warning stage,disaster stage and rehabilitation stage [15]. As soon as a depression is formed in Bay of Bengal,
BMD transmits cyclone warning signals in the form of special weather bulletins to CPP office at
Dhaka. BDRCS is also responsible for issuing warning notices to CPP and relevant Red CrescentUnit. This information is transmitted to 6 zonal offices and 30 thana level offices over HighFrequency (HF) radio, which is in turned passed to the unions through Very High Frequency (VHF)
radio. The union team leaders of CPP disseminate cyclone warnings along with volunteers in thevillages using megaphones, hand signs and public-address system. The team leaders also keep track73

of the approaching cyclone by listening to the national radio broadcast. When situation turns serious,
government calls for emergency evacuation and the volunteers implement the order by helping
people in taking shelter in cyclone shelters or other available safe places [37]. Divisional
Commissioner, DC, UNO and Disaster Management Committee (DMC) provide necessary directives
and Fire Service and Civil Defense of the affected areas are assigned to contact with local
administration for carrying emergency operations. In case of NGOs, SOD assigned them to assist
government agencies and work in liaison with DMB, DC, UNO and UP Chairman [15].
The brief review of national policies related to disaster risk reduction in Bangladesh reveal thefollowing important characteristics that may affect the response system of natural disastersFirstly, national policies especially the SOD have bestowed primary responsibilities of disastermanagement to public organizations. Both ministries and governmental agencies are held responsiblefor disaster preparedness, mitigation, response and recovery. However, personnel in publicorganizations often lack sufficient capacity to effectively deal with emergency situations. There aremany public organizations which lack proper awareness of SOD and their responsibilities during
natural disasters. Organizations at local level including CPP volunteers also lack proper resources
and training. There is no formal mechanism for non-profit organizations and private sector to assist
public organizations. Effective coordination and management of response efforts is hindered by suchdiscrepancy between legal responsibility and actual capacity of public organizations.
Secondly, the expected scenario in case of emergency response is based on a “top-bottom approach”.
Many of the key organizations including AFD has to work as per requisition of the central
government during emergency situations. In case of local administration, the higher order ofadministration is responsible to guide and coordinate the activities of lower level authorities
including NGOs. For example- District Disaster Management Committee (DMMC) is entitled tocoordinate the activities of union, pourashava and upazila DMC. Although in the DM Act local
disaster management committees have been given the provision of requesting the deputy
commissioner to declare emergency for a particular disaster affected area, the order has to come fromupper levels of administration. Poor information management among different levels of authorities
and lack of decentralization at local levels of governance hinder effective response during emergency
situations.
Lastly, the SOD is not prepared for any specific hazard. The stages of disaster management vary
distinctly among different disasters. SOD (1999) was prepared based on flood in 1988 and major
cyclone in 1991. Later earthquake has been included when SOD was updated in 2010. Tropical
cyclones, as regular natural hazards of Bangladesh, require separate sets of guidelines andorganizations for the vulnerable coastal regions. Listing the organizations solely responsible for74earthquake, cyclone, storm surge and flood may enable them to adapt to changing circumstances
more effectively
These structural characteristics of the response system, written into the policy documents, create
barriers in emergency response management of Bangladesh. Therefore, cyclone Sidr gave a valuable
opportunity to explore the actual organizational response system under the stress of a disaster.
6. Organizational Response to Cyclone Sidr (2007)
The performance of any disaster risk reduction plan can be verified when an actual incident ofdisaster occurs. Cyclone Sidr provided major test of SOD (1999) and the international commitmentof HFA (2005) and SAARC framework (2006).
The organizational response system that emerged following cyclone Sidr has been observed throughcontent analysis. From the news articles reported in The Daily Star newspaper, all the organizations
that participated in the response system through provision of search and rescue services, medicalcare, relief distribution, donation, shelter and other types of assistance for the affected persons havebeen identified. Content analysis has been conducted for a period of three weeks, starting from 15
November, 2007 to 7 December, 2007. Content analysis of news reports has identified 203
organizations that responded to cyclone Sidr and it does not claim to represent the entire response
network. Table 2 shows the composition of the response system as found from the analysis. Here city
based organizations denote those organizations that are mainly based on capital Dhaka or other major
cities. Local based organizations have been identified as organizations that are located at 30 districts
that have been directly affected by the cyclone. Organizations that operate worldwide or outside theterritory of Bangladesh have been mentioned as international.
Number of Organizations

40
35
30
25
20
15
10
5
15/11
16/11
17/11
18/11
19/11
20/11
21/11
22/11
23/11
24/11
25/11
26/11
27/11
28/11
29/11
30/11
1/12
2/12
3/12
4/12
5/12
6/12
7/12

0

Public
Sepcial interest

Date
PrivateTotal

Non profit

Figure 3: Entry of new organizations into the response system by funding source and date (Source: Data from The Daily Star)

75

Table 2: Frequency Distribution of Organizational Response System by Jurisdiction andSource of Funding, Cyclone Sidr, November 15–December 07, 2007
SpecialPublicN%

PrivateN%

Non-profitN%interest
N

%

Total
N

Internationa
l

%
45.3

43

21.18

11

5.41

38

18.71

0

0

92

2
30.0

National

33

16.25

15

7.38

6

2.95

7

3.44

61

5
13.3

City

9

4.43

7

3.44

11

5.41

0

0

27

0
11.3

Local

12

5.91

1

0.49

10

4.92

0

0

23

3

Total

97

47.78

34

16.74

65

32.01

7

3.44

203

100

(Source: Content analysis of news reports regarding cyclone Sidr from The Daily Star)
Appendix 1 contains the full list of organizations as found from the content analysis.
Table 2 documents 203 organizations in the response system, with the largest number, 97
organizations (47.78%) are identified as public organizations. The breakdown by jurisdictions showsthat the largest number of public organizations are from international level (43, or 21.18% of totalorganizations). National and international organizations have made up 34.02 % and 44.33% of the
public organizations respectively. In case of private sector, national organizations (44.12%) havedominated over international organizations (32.35%). The non-profit organizations have made up thesecond highest group of organizations (32.01% of total), with more than half (38, or 58.46%)
belonging to international organizations. It is noteworthy from the analysis that nearly half of thetotal organizations (45.32%) are from international level. Local organizations constitute the smallest
segment (11.33%) and majority of the local organizations are public (15.38%). The special interest
group has been identified as groups having political interest that didn’t fit any of the other three
categories.

76

Results from content analysis doesn’t fully correspond to the SOD (1999) as only 34.02% of the
public organizations are from national level. In contrast to the national policies which havedesignated major responsibilities towards the public organizations, nearly half of total organizations(48.75%) has been represented by private and non-profit organizations. Undoubtedly national andpublic organizations required the support from international public, private and non-profit
organizations. The significant participation of international aid organizations in the response network
denotes the country’s dependency on international assistance. The lowest participation made by localorganizations signifies the lack of power and resources at the local level despite their importancebeing mentioned in SOD and other national policies.
The performance of the organizations can be more clearly defined by identifying the time at whichorganizations from different jurisdiction and sector entered into the response system. Figure 2 and 3
show the date of entry of organizations into the response system by jurisdiction and source offunding respectively.
At the day of landfall (15 November, 2007) the local organizations have responded quickly, followed
by international and national organizations. District and upazila administration, law enforcement
agencies and local NGOs have quickly entered into the response system as per requisition of upper
levels of administration. Among the national organizations, central government and ministries have
responded rapidly through an emergency meeting of National Disaster Management Council anddirected the district administrations. Significant international assistance came in 17 November, twodays after the cyclone made landfall. Seven days after the storm, city based organizations had theirpeak on 22 November, 2007. The entrance of new organizations into the response system has been
increasing for the first week after landfall and decreased after 22 November, 2007.
When the rate of entry is analyzed by source of funding, results show the early and intense responseby public and non-profit organizations, which has been continued throughout the first three weeks
after landfall. Organizations from private sector have only entered into the response system on 20
November, five days after the cyclone hit. Organizations with special or political interest haveentered into the response system at a slower rate and had their peak on the 15th day, 29 November,
2007.
The results obtained from this analysis can be validated from a post-disaster survey report preparedby Japan International Cooperation Agency (JICA). The report includes a survey of 200 households
from four Sidr-affected districts. Results from household survey and Focus Group Discussion (FGD)
indicate that firstly local organizations (especially upazila and district DMC), secondly government
officials from AFD followed by NGOs have extended their cooperation for emergency support andrescue operation [41].
777. Network Analysis
After identification of organizations, a matrix has been prepared depicting the interactions among
them for network analysis. The data has been entered into ORA, a network analysis softwaredeveloped by Center for Computational Analysis of Social and Organizational Systems (CASOS),
Carnegie Mellon University. Figure 4 shows the visual representation of interaction among 203
organizations responding to cyclone Sidr as prepared by ORA. Sources of funding are represented bycolors of the node and jurisdiction areas are shown by node shapes. The network map depicts thepresence of a large cluster located at the center of the response network. Many of the international,
national and city based organizations from public, private and non-profit sectors have channeled theirassistance through central government which can be seen from the diagram. Organizations withspecial political interest haven’t take part into the central network, rather they have worked alone.
Findings from the network analysis can be validated from the review of news reports. Immediately
after the cyclone, the Chief Advisor of contemporary Care Taker government has establishedemergency relief fund which collected donations from the donor agencies, from both national andFigure 4: Network diagram of the cyclone Sidr (2007) response system (Source: Data from The Daily Star and analysis from ORA)

international level. Therefore, maximum amount of donation has to be channelized through central
government in order to reach the affected people. However, some international/ public agencies havedisbursed their assistance through international NGOs. For example- German Red Cross inassociation with CARITAS, an international NGO, distributed the amount granted by German
government.
78

One key information that can be derived from the network map is the large number of isolates. 33
out of 203 organizations (16.26%) don’t have any interaction with other organizations, and are
isolated from the central network. Major portion of the isolated organizations are national and public(6 or 18.2%) organizations. Isolates are quite common in network structures, but such large number
of isolated organizations in response to a major disaster of the country is not typical. An organizationthat is disconnected from others will find it difficult to manage by itself as the lack of connections
will weaken its ability to learn from others’ actions [28]. Especially for public organizations who
have prime responsibility in emergency management, this lack of interaction denotes a major
discrepancy from the stated national policies.
The study has identified ten key organizations in the response system calculated by ORA, as shownin Figure 5. Key entities are those organizations which have the most ability to monitor information
flow and reach out to other organizations [30].
Among the ten key organizations, only two ofthem are national and public. Interestingly, eightout of ten organizations are from international
level, with five of them being international NGOs
and three from the public sector.
Figure 5: Key agents in the overall disaster response system(Source: Data from The Daily Star and analysis from ORA)

Given the severity of cyclone Sidr, the projected

destruction has been expected to be much higher than what has actually occurred. The Care Taker
government has taken the following measures to manage the response situation and reduce theamount of destruction(1) Government has made investment on an improved Early Warning System that served to alert
the population to the impending storm.
(2) Immediately after the cyclone making landfall, the National Disaster Management Council
has directed the local government, especially the deputy commissioners of 15 vulnerable
districts to take necessary preparations.
(3) In collaboration with international agencies, the Care Taker government has executed theimmediate relief effort. Donor funds allocated to the relief efforts have been managed in a
judicious manner by the government.
(4) Regular meetings have been held by the Advisory Council with national and international
donors to discuss the situation.
(5) Relief provided by donor agencies often require long time to distribute due to the lengthy
government bureaucratic processes. To handle this, the government has delineated the79

affected districts of coastal area to remove hassles and smooth out the relief distribution
process. The assistances provided by international donors have been directly received by atemporary center established at Barisal. The contact numbers of all DC of affected districts
have been provided to the donor agencies. Such delineation made it easier and quicker for the
local administrations to distribute relief material among the affected people.
AFD has contributed to the response operation by immediately launching search and rescue andrelief operations. They have monitored the weather condition in the pre-landfall period, executed
search and rescue operations, distributed relief materials and provided medical services to thevictims. While executing such functions, AFD has coordinated with central government andinternational agencies like US Army.
Among the international aid agencies, Japan government has provided relief goods under “Japan
Disaster Relief”. Alongside coordinating with government of Bangladesh and ministries, governmentof Japan has interacted with WHO, WFP and UNICEF. Another key organization, government of US
has coordinated through organizations like Save the Children, Oxfam, CARE and UN agencies.
IFRC has interacted with a number of international/public organizations, including government ofAustralia, Spain, Canada and later with BRCS for emergency response management.
The active participation of central government and AFD shows the focus and commitment national,
public institutions towards emergency disaster risk reduction in Bangladesh. It is also notable that,organizations having prior experiences with disaster situations and/or high disaster preparedness andinfrastructure dominate the list of key organizations. In contrast to the “top-to-bottom” disastermanagement system mentioned in SOD, the contemporary Care Taker government has put an effort
to strengthen the local government by delineating the relief channel process along coastal regions.
However, the low participation and absence of any local based agencies among the key entities
denote that local organizations still require adequate resources and mechanism to fully coordinate
with the central government. Despite the activeness of central government and AFD, the absence ofenough public organizations among the key organizations indicates the lack of efficiency among
public organizations to properly manage the emergency management process.
8. Centrality Measures
In this section, the nodes are analyzed based on their centrality. An analysis of network centrality
measures helps to identify the actors that shape the performance of the response network. Centrality
scores are assigned to nodes based on the principle that connections to high-score nodes contribute
more to the score of the particular node than connections to low-score nodes [30, 31].

80

Table 3 shows the top 10 organizations in the main component of cyclone Sidr ranked by order ofimportance on their centrality values named as – degree, betweenness, closeness, and eigenvector
centrality.
Table 3: Top ten organizations under different centrality measures in response to Sidr (2007)

Total degree centrality
1.

Bangladesh

6. German government

government
2. AFD

7. UNDP3. Japan government

8. WFP

4. British government

9. IFRC

5. US government

10. MoFDM

Out-degree centrality
1.

Bangladesh

6. German government

government
2. Japan government

7. Swiss government

3. British government

8. Denmark government

4. US government

9. UNICEF

5. Australia government

10. WFPIn-degree centrality
1.

Bangladesh

6. MoFDM

government
2. AFD

7. CARE

3. IFRC

8. UNO

4. WPB

9. WFP

5. NGO

10.

Agriculture

secretariat
Betweenness centrality
1.

Bangladesh

6. MoFDM

government
2. AFD

7. UNO

3. UNDP8. Ministry of Education

4. WFP

9. Ministries

5.German government

10. Thai businesswomen

Closeness centrality
1. British government

6. China government

2. Japan government

7. UNDP81

3. US government

8. Amir of Kuwait

4. Australia government

9. India government

5.

10. Italy government

Denmark

government
Eigenvector centrality
1.

Bangladesh

6. MoFDM

government
2. WFP

7. UNDP3. AFD

8. UNICEF

4. US government

9. ADB

5. Japan government

10.

Agricultural

secretariat

Degree centrality is the measure that evaluates actors in the network by the number of ties actors
possess [12]. An analysis of degree centrality shows the organizations that most frequently interactedwith other organizations in the network. Total degree centrality calculated by ORA shows thatgovernment of Bangladesh has the highest degree centrality, and can be regarded as the mostinfluential in the response network. A centrality degree analysis can be represented by two types ofscores- out degree and in degree.
Out-degree centrality represents the number of links or connections emanating from a node.
Organizations with high out degree centrality scores include government of Bangladesh, Japan,
Britain, US, and Australia. Bangladesh government has communicated with local administration,
MoFDM, local NGOs as well as with multiple international organizations such as UN to coordinate
the relief efforts. Other international and public organizations have distributed their donations
through a number of international NGOs.
In-degree centrality depicts the number of links directed to a node, or the number of connections
the node of interest receives from other nodes. The top organizations in terms of in degree centrality
include government of Bangladesh, AFD and IFRC. Government and AFD both have received
significant response from national and international donors through the emergency fund. IFRC has
received information from BDRCS and coordinated relief from international donors. Local NGOs
and local administration (UNO) have ranked fifth and eighth among 203 nodes respectively, which
denotes that the local organizations have received substantial assistance from other nodes in the formof order of evacuation, donations, relief materials etc. Therefore, strengthening local government canleverage this effect and have a greater impact on overall emergency management.

82

Betweenness centrality is another centrality measure which measures the extent to which aparticular node lies between the various other nodes of the network [42]. Greater betweenness
centrality of an actor also depicts that more actors are dependent on that actor to communicate withother actors [27]. In regards to betweenness centrality, the top organizations are- government of
Bangladesh, AFD, UNDP, WFP etc. Many of the donor countries have coordinated with Bangladesh
government and disbursed their assistances through international organizations like WFP and UNDP.
Being the shortest path between other nodes, these organizations have served as gatekeepers between
organizations and controlled the flow of response network.
Closeness centrality measures how close an actor is to other actors of the network [12]. Nodes withhigh values of closeness centrality are likely to receive information more quickly than others, as
there are less numbers of intermediaries to reach them [31]. The top organizations in terms ofcloseness centrality include government of Britain, US, Japan etc. Government of US hascoordinated the efforts of USAID, US State Department, US Army, US Mission, as well as
maintained connection with the central government of Bangladesh. Thus, it is considered as theleader in terms of closeness centrality.
Lastly, organizations that are connected to groups that have many connections themselves can be
identified using eigenvector centrality. Organizations that are connected to isolated nodes will havea lower score compared to those organizations that are connected to well-connected nodes [43].
Government of Bangladesh is the leading organization according to this criterion. This means that it
is connected to many other organizations who are well connected and thus is most likely to receive
new information.
Effective response operations require collaboration between various government levels and between
the government, private sector and non-profit sector [6]. In response to cyclone Sidr (2007), thedegree of connectedness among the nodes is calculated as 0.531. A connectedness score of 1
suggests that all actors are reachable to each other and deviation from 1 indicates the fragmentationof network [30]. The overall degree of centralization is 10.3%. The percentage of isolated nodes
discussed before and the degree of centralization value indicate that a large number of organizations
were not in communication with other organizations.
Another interesting finding from the response network is the identification of cliques. Cliques aresubset of organizations, often consisting of three or more organizations, that develops recurring
pattern of interaction in conduct of disaster operations [44]. Using ORA, 19 cliques were identified
from the larger network as shown in Table 4.

83

Table 4: Cliques identified within the disaster response network of cyclone Sidr: 15 November- 7
December, 2007
CliqueMembers

1

AFD, Bangladesh government, WFP,

2
3
4
5
6

AFD, Bangladesh government,
AFD, Bangladesh government,
AFD, Bangladesh government,
Bangladesh government, UN,
Bangladesh government, MoFDM,

7

Bangladesh government, NGO,

8
9

Bangladesh government, NGO,
Bangladesh government, US

10
11

Bangladesh government, WFP, US
Bangladesh government, Japan

12
13

Bangladesh government, Denmark,Bangladesh government, Youngone

14
15

CARITAS, World Vision, Tree Das
CARITAS, World Vision,

16

CARITAS, World Vision, Shiblee's

17

CARITAS, German government,

18
19

British government, CARE, German
NGO, Agriculture ministry,

Among the 19 identified cliques, 13 or 68.4% include Bangladesh government. The next largest
subset includes 12 or 63.2% of cliques with interactions among national and internationalorganizations. Only 5 or 26.3% of cliques has represented interactions among national and local level
organizations, a finding that confirms the gap between disaster management plans and practice. Twocliques have involved only non-profit agencies, working at the international and local level.
Following cyclone Sidr, government of Bangladesh proceeded to formulate early recovery action
plan in cooperation with UN agencies and other national and international organizations under thejoint leadership of DMB, MoFDM and UNDP Bangladesh. Rapid damage assessment has been
conducted in all Sidr affected districts and action plans have been linked to long-term reconstruction
activities. Besides, government has taken some additional coordination mechanism to enhance
emergency information sharing by establishing a formal link between MoFDM and AFD.
84

9. Conclusion and Future Work
Three conclusions can be drawn from the study to assist further improvement in the interorganizational emergency response management following catastrophic eventsFirstly, the political situation at times of cyclone Sidr (2007) has a direct impact on the emerging
response network. On January, 2007, the non-party interim Care Taker government took control
during the political crisis and continued until new elections were held in 29 December, 2008. All theleaders from leading political parties of Bangladesh have been imprisoned during that time.
Therefore, the network analysis of response organizations lacks any proper assistance from the major
political parties. Most of the organizations from special interest group have been isolated from thecentral network. Although there has been lack of extensive response and interactions among otherpublic organizations as seen from the results, the Care Taker government has undertaken strict
initiatives and sensibly coordinated response and relief efforts which is prevalent from the list ofboth key organizations and cliques.
Secondly, findings from the study have documented a higher degree of action at the central or
national level, with the central government providing assistance to the local agencies. But effective
emergency management must come bottom-up and local agencies should also have responsibilities.
Although local agencies are fast and responsive to the cyclone, they have been unable to coordinate
their activities as subgroups. According to the post-Sidr survey report of JICA (2008), DMC at
central, district and upazila level have played their role effectively but DMC at union level was
found inactive in most of the cases. About 80% people from the survey has revealed that they haveno perception about the Union Disaster Management Committee and some UP members don’t evenknow about the existence of such a committee in their union. Reports from various organizations andnews articles have also indicated favoritism by UP Chairmen and members in the relief distribution
process despite the strict surveillance of government and AFD. Results from the research haveshowed that although local administration has received information from upper levels, their
participation rate has been significantly low (11.33%) and some of them have been isolated from thecentral network. Creating central as well as local database showing the amount of received reliefmaterials and organizations acting in the specific area is vital to remove such obstacles when adisaster occurs. Ensuring effective communication channels among different levels of government,
preparing a comprehensive disaster management plan for coastal areas and initiating proper training
at local level including UP members and CPP volunteers are necessary to ensure coherence with the
SOD and other DM policies.
Thirdly, international community response constitutes a major part of emergency response todisasters in Bangladesh. Starting from relief to recovery and reconstruction, donor agencies around85the world have played a significant role in delivery of humanitarian assistance during cyclone Sidr.
The huge number of actors makes practice of coordination extremely challenging. According to
SOD, NGO Coordination Committee (NGOCC) is responsible to guide and monitor the activities ofNGOs. Broader and inclusive coordination is not fostered as there is no mechanism to incorporate
UN agencies and other donors. The Care Taker government has guided the coordination ofinternational assistance during Sidr. But the extent of international contribution highlights the needfor a coordination mechanism with international NGOs as well as other international stakeholders.
Lately the country experienced other cyclones including cyclone Aila (2009), cyclone Roanu (2016),
cyclone Mora (2017) which have showed a lot of mismanagement in their emergency response
activities. For example- findings from cyclone Aila have revealed that there was negligence in theprovision of services as well as bribery and misuse of resources in the post-disaster interventions[45]. The inter-organizational connectedness or degree of centralization which have been achieved
through the leadership of the Care Taker government in case of cyclone Sidr (2007), may differ
significantly for other disaster events due to variation in political situation under the leadership of atypical democratic government. Therefore, further network analysis can be conducted for otherdisaster events to generate a comparative analysis of inter-organizations network. Due to theincreasing frequency of cyclones in the recent years in Bangladesh, effective interorganizational
network at all levels of governance should receive due attention from the policy makers. Inability to
do so will adversely affect the emergency management process resulting in increased causalities anddestruction of properties after disastrous events.
Funding sources: This research did not receive any specific grant from funding agencies in the
public, commercial, or not-for-profit sectors.
References[1] Haque, A., & Jahan, S. (2016). Regional Impact of Cyclone Sidr in Bangladesh: A Multi-Sector
Analysis. International Journal of Disaster Risk Science, 7(3): 312-327.
[2] Dhar, A.S., & Ansary M.A. (2012). Community-based evaluation for the development of asustainable disaster early warning system. Journal of Emergency Management, 10(3).
[3] GoB (Government of the People’s Republic of Bangladesh). (2008). Cyclone Sidr in Bangladesh:
Damage, loss, and needs assessment for disaster recovery and reconstruction. Dhaka: GoB[4] Ali, A. (1999). Climate change impacts and adaptation assessment in Bangladesh. Climate
research, 12(2-3): 109-116.
[5] Paul, B. K. (2009). Hazard warnings and compliance with evacuation orders: the case of
Bangladesh’s Cyclone Sidr. The Natural Hazards Center, University of Colorado at Boulder. Quick
Response Rep (forthcoming).
86

[6] Kapucu, N. (2005). Interorganizational coordination in dynamic context: Networks in emergency
response management. Connections, 26(2): 33-48.
[7] Almquist, Z.W., Spiro, E.S., & Butts, C.T. (2017). Shifting Attention: Modeling Follower
Relationship Dynamics Among US Emergency Management-Related Organizations During a
Colorado Wildfire. In Social Network Analysis of Disaster Response, Recovery, and Adaptation, ed.
Jones, E.C. & Faas, A.J., 93-112. United States: Elsevier.
[8] Waugh Jr, W. L. (2004). Leveraging networks to meet national goals: FEMA and the state
construction networks. Collaboration: Using Partnerships and Networks: 273-320.
[9] Wang, W. J. (2012). The Formation of Interorganizational Networks in Extreme Events: A
Comparative Study of the 1999 ChiChi Earthquake and the 2009 Typhoon Morakot (Doctoral
dissertation, University of Pittsburgh).
[10] Comfort, L.K., Dunn, M., Johnson, D., Skertich, R.& Zagorecki, A. (2004). Coordination incomplex systems: increasing efficiency in disaster mitigation and response. International Journal
Emergency Management, 2(1–2): 62–80.
[11] Drabek, T. E., & McEntire, D. A. (2002). Emergent phenomena and multiorganizational
coordination in disasters: Lessons from the research literature. International Journal of Mass
Emergencies and Disasters, 20(2): 197-224.
[12] Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol 8).
Cambridge, UK: Cambridge University Press.
[13] Zhao, Y. (2013). Social networks and reduction of risk in disasters: an example of the
Wenchuan Earthquake. In Economic Stress, Human Capital, and Families in Asia, 171-182.
Netherlands: Springer.
[14] Comfort, L. K., & Haase, T. W. (2006). Communication, Coherence, and Collective Action the
Impact of Hurricane Katrina on Communications Infrastructure. Public Works management &policy, 10(4): 328-343.
[15] GoB (Government of the People’s Republic of Bangladesh). (2010). Standing Order on
Disaster. Dhaka: Ministry of Food and Disaster Management (MoFDM), Disaster Management andRelief Distribution and Disaster Management Bureau (DMB), GoB.
[16] Lassa, J. A. (2015). Post disaster governance, complexity and network theory: evidence fromAceh, Indonesia after the Indian Ocean Tsunami 2004. PLoS currents, 7.
[17] Gidron, B., Kramer, R. M., & Salamon, L.M. (1992). Government and the Third Sector:
Emerging Relationships in Welfare State. San Francisco: Jossey- Bass Publishers.

87[18] Kapucu, N. (2006). Interagency communication networks during emergencies boundary
spanners in multiagency coordination. The American Review of Public Administration, 36(2): 207225.
[19] Butts, C.T., Petrescu-Prahova, M., & Cross, B.R. (2007). Responder Communication Networks
in the World Trade Center disaster: Implications for Modeling of Communication within Emergency
Settings. Journal of Mathematical Sociology, 31(2):121–147.
[20] Smith, P. C., & Simpson, D. M. (2009). Technology and communications in an urban crisis: The
role of mobile communications systems in disasters. Journal of Urban Technology, 16(1): 133-149.
[21] Ran, Y. (2011). Considerations and suggestions on improvement of communication network
disaster countermeasures after the wenchuan earthquake. IEEE Communications Magazine, 49(1).
[22] White, C., Plotnick, L., Kushma, J., Hiltz, S. R., & Turoff, M. (2009). An online social networkfor emergency management. International Journal of Emergency Management, 6(3-4), 369-382.
[23] Hamra, J., Hossain, L., Owen, C., & Abbasi, A. (2012). Effects of networks on learning during
emergency events. Disaster Prevention and Management: An International Journal, 21(5): 584-598.
[24] Comfort, L. K., Ko, K., & Zagorecki, A. (2004). Coordination in rapidly evolving disaster
response systems: The role of information. American Behavioral Scientist, 48(3): 295-313.
[25] Comfort, L. K., & Kapucu, N. (2006). Inter-organizational coordination in extreme events: The
World Trade Center attacks, September 11, 2001. Natural hazards, 39(2), 309-327.
[26] Butts, C. T., Acton, R. M., & Marcum, C. S. (2012). Interorganizational Collaboration in the
Hurricane Katrina Response. Journal of Social Structure, 13.
[27] Kapucu, N., Arslan, T., & Collins, M. L. (2010). Examining intergovernmental andinterorganizational response to catastrophic disasters: Toward a network-centered approach.
Administration & Society, 42(2): 222-247.
[28] Scheinert, S., & Konstantinova, R. (2011). Attempting a Knowledge Commons in the Field: theResponse to the January 12th, 2010 Haitian Earthquake. Working Paper No. 1104. Centre forDisaster Management: University of Pittsburgh[29] Comfort, L.K., & Okada, A. (2011). Designing Collective Action to Build Community
Resilience: The Fukushima Nuclear Disaster, March 11, 2011. Wordpress. Retrieved 14 June, 2017,fromhttps://fukushimaforum.wordpress.com/workshops/sts-forum-on-the-2011-fukushima-east-japan-disaster/manuscripts/session-2/designing-collective-action-to-build-community-resilience-thefukushima-nuclear-disaster-march-11-2011/[30] Comfort, L. K., Okada, A., & Ertan, G. (2013). Networks of Action in Catastrophic Events: The
11 March 2011 Tohoku-Oki Disasters. Earthquake Spectra, 29(1): 387-402.

88

[31] Cheong, F., & Cheong, C. (2011). Social media data mining: A social network analysis of
tweets during the Australian 2010-2011 floods. In 15th Pacific Asia Conference on Information
Systems (PACIS) (pp. 1-16). Queensland University of Technology.
[32] Qayyum, M.A. (2016, March 10). Disaster management in Bangladesh: Reducing
vulnerabilities. The Daily Star. Retrieved 14 June 2017, from http://www.thedailystar.net/oped/politics/disaster-management-bangladesh-reducing-vulnerabilities-788653[33] Habib, A., Shahidullah, M., & Ahmed, D. (2012). The Bangladesh cyclone preparedness
program. A vital component of the nation’s multi-hazard early warning system. In Institutional
Partnerships in Multi-Hazard Early Warning Systems, 29-62. Berlin Heidelberg: Springer[34] GoB (Government of the People’s Republic of Bangladesh). (2013). Disaster Report 2013.
Dhaka: Department of Disaster Management, Ministry of Food and Disaster Management(MoFDM), GoB.
[35] World Bank and Earthquakes and Megacities Initiative (EMI). (2014). Legal and Institutional
Arrangements (LIA) Framework Guidebook. Bangladesh Urban Earthquake Resilience Project,
Dhaka.
[36] Armed Forces Division (AFD). (2013). Armed Forces Division in Disaster Management.
Retrieved 14 June 2017, from http://www.afd.gov.bd/index.php/activities/armed-forces-division-indisaster-management[37] Rashid, A.K.M. (1997). The Cyclone Preparedness Programme in Bangladesh. Regional
Course on Community Based Approaches in Disaster Management (CDM-1): Asian Disaster
Preparedness Center (ADPC), Bangkok, Thailand.
[38] Amin, M. (2012). Motivating volunteers: a case of cyclone preparedness programme volunteers
in Bangladesh (Doctoral dissertation, BRAC University).
[39] Irfanullah, H.M., & Miah, S.H. (2015, January 20). Capitalizing on the Cyclone Preparedness
Programme (CPP) to build community resilience in Bangladesh. Humanitarian Practice Network(HPN).

Retrieved 14 June 2017, from http://odihpn.org/blog/capitalising-on-the-cyclone-

preparedness-programme-cpp-to-build-community-resilience-in-bangladesh/[40] Holguín-Veras, J., Pérez, N., Ukkusuri, S., Wachtendorf, T., & Brown, B. (2007). Emergency
logistics issues affecting the response to Katrina: a synthesis and preliminary suggestions for
improvement. Transportation Research Record: Journal of the Transportation Research Board,(2022): 76-82.
[41] Japan International Cooperation Agency (JICA). (2008). Report on Survey on Disaster Relief
Activities in Bangladesh.

89

[42] Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks,
1(3):223-258.
[43] Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique
identification. Journal of Mathematical Sociology ,2(1): 113–120.
[44] Scott, J. (2000). Social network analysis. Thousand Oaks, CA: Sage Publications.
[45] Mahmud, T., & Prowse, M. (2012). Corruption in cyclone preparedness and relief efforts incoastal Bangladesh: Lessons for climate adaptation. Global Environmental Change, 22(4): 933-943.
[46] The Daily Star. (2011). Various news reports published between 16 November 2007 and 8
December 2007, Dhaka, Bangladesh

90PART-V

EXPERIMENTAL INVESTIGATION ON BEHAVIOR
OF REINFORCED CONCRETE INTERIOR BEAM
COLUMN JOINTS RETROFITTED WITH FIBER
REINFORCED POLYMERSBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Fahim Ahmed
Raquib Ahsan
Mehedi Ahmed Ansary91

1. Introduction
Reinforced concrete buildings constructed before 70’s were designed for gravity loads only and did
not show adequate seismic performances (Bai 2003, Sharma et al. 2010). Weaknesses in joints wereidentified as one of the main causes for poor seismic performance. To overcome this deficiencies
first guidelines for reinforced concrete beam-column (BC) joints were published in United States in1976 (ACI 352R-76) followed by New Zealand in 1982 (NZS 3101:1982). Therefore, buildings
constructed before 1976 may have significant deficiencies in the joint regions (Bai 2003, Murat et al.
2005). Due to poor design and detailing of the BC joints of these buildings, they may lead to a totalor partial collapse due to an earthquake (Sezen 2012, Zhao et al. 2009, Sezen et al. 2010, Bracci et al.
1992 from Prota et al. 2004). BC joints are subjected to large shear forces during seismic events;
therefore many experimental and analytical researches had been carried out in past few decades on
the behavior of RC BC joints under seismic condition (Mayfield et al. 1971, Ichinose 1991, Erduran
et al. 2003). Various international codes (ACI 318, 318M-08, ACI 352R-02, ACI ASCE, Committee
352, FEMA 273, BNBC 1993) of practices had also been evolved and undergone periodic revisions
to incorporate these research findings into practice.
In Bangladesh, many RC buildings had been constructed without seismic detailing in the BC joints
before or even after the inception of BNBC (1993). Change in the types of occupancy is very
common as residential buildings are frequently converted to commercial and industrial buildings;
number of floors is increased without proper design analysis. These buildings are vulnerable toseismic hazard and need to be strengthened. Conventional retrofitting methods are sometimes
difficult due to the nature of occupancy, importance of the structure, economic value of the nonoperational period and cost of the man and materials. FRPs, which have high strength to weight ratio,
are suggested and are used as retrofitting materials in many countries of the world as these materials
are easy to place, need minimum relocation of existing occupancy and retrofitting time is very shortcompared to other methods. Sharma et al. 2011, Sezen 2012, Tara Sen 2010, Pandelties et al. 2000,
Mahini and Ronagh 2007 strengthened the joints by FRP wrappings. Mukharjee and Joshi 2005 used
FRP plates to increase the shear and flexural strength of the beam. Akguzel and Pampanin 2010,
Sezen 2012, Mahini 2007 conducted research on exterior joints where access to the joints was
relatively easy and the existence of floor slab was not considered.
Al-Salloum et al. (2007a, 2007b) carried out experimental and analytical studies to see behavior of
FRP strengthened joints with or without mechanical anchorage and found that CFRP sheets epoxy
bonded to the joint region, and prevented against debonding

through mechanical anchorage;

prevents joint failure and failure is directed to the beams. He also deducted that, joint shear strength
92

increases with increased quantity of FRP and axial load due to joint confinement. Mukharjee et al.
(2002) carried out a study to investigate the behavior of RCC joint with and without adequate shear
reinforcement in the joint region. The specimens with adequate shear reinforcement and strengthened
with FRPs exhibited higher dissipation of energy and ultimate deformation than controlled
specimens. Non-ductile joints strengthened by FRP also exhibited better performance and theirperformance depends on the number of layers of FRP wrapping. Murshed and Ahmed (2011) carriedout seismic analysis on eight soft story structures which were retrofitted with FRP wraps. It was
found that seismic performance of the soft story structures can be improved by FRP wraps. Improvein the lateral strength was negligible due to wraps but ductility improvement was quite satisfactory.
Almusallam et al. 2007, Shiohara et al. 2010, Osamu et al. and Li et al. 2011 conducted research on
the behavior of RC interior beam-column joints. These studies were conducted on interior joints
which did not include transverse beams and slabs.
This study attempted to access the interior beam column joints with transverse beams on all foursides and monolithic floor slab. It also attempted to increase the shear strength of the joints byinserting CFRP plate into the beam-column joint, wrapping beam and columns by CFRP fabrics andinclusion of tie bar in the joint region. To investigate the behavior of the RC interior BC joints
retrofitted by FRP plates, fabrics and tie bars, incremental cyclic loading were provided withconstant axial load on to the test samples. Half scale models with transverse beams and slab had been
constructed before strengthening the joints. Following parameters had been considered to investigate
the behavior of the joints: (a) Interior joints having different concrete strength, (b) Interior joints
strengthened by wrapping beams and columns by CFRP fabrics, (c) Interior joints strengthened byinserting CFRP plates (d) Interior joints strengthened by inserting tie bar in the joint and replacing
concrete by micro-concrete. Total eight models had been constructed for the study as follows: (a)Two control models designated as Con 1 and Con 2, (b) Three Models strengthened by CFRP
Fabrics designated as Fabrics 1, Fabrics 2 and Fabrics 3, (c) Two models strengthened by CFRP
plate designated as Plate 1 and Plate2, (d) One model strengthened by additional tie bar and microconcrete mixture designated as MCS 1.
The outcome of the study will unveil the behavior of the strengthened RC interior BC joints andcompare the behavior with the joints which lack in shear reinforcement. The research will also
facilitate in developing methods of determining strength of retrofitted joints and identify suitableprocedures to retrofit interior BC joints by CFRPs.

93

2. Detailing of control and test models
The models had been selected considering a typical full scale six storied RC Frame Structured
Building. The building was analyzed as per BNBC (1993). An interior joint at the mid height of thestructure had been selected for the experimental program. Considering the existing laboratory set up
an half scale model had been finally selected. Total eight models were constructed for theexperiment. All the models had identical beams, columns and slab. The columns were 150x150 mm
in width and breadth.12 mm Φ and 8 mm Φ bar had been used as longitudinal reinforcement andtransverse reinforcement (tie bar) respectively. The dimensions of the longitudinal beam (B1) were
150x225 mm and those of the transverse beam (B2) were 150x150 mm. 12 mm Φ bar had been used
as longitudinal reinforcement. 8mm Φ bar was used as stirrups. There was no tie bar (Shear
reinforcement) in the BC joints. Neither beams nor the columns had any lap joint. Thickness of theslab was 75 mm having one layer of reinforcement in both X and Y directions. Tie and stirrups had
standard 90º hook as per BNBC (1993). Two control specimens had also been constructed.
Dimensions and detailing of the as built models are shown in Fig. 1. Concrete strength of thesamples are shown in Table 1.

Fig. 1 Dimensions and detailing of models
Table 1 Concrete strength of the samples
Mix NoCon-1Con-2
Plate-1
Plate-2

Mix NoAverageStrength
(MPa)
18.31
28.31
15.03
26.8

Fabrics-1
Fabrics-2
Fabrics-3
MCS-1

94

AverageStrength
(MPa)
21
24.53
33.77
18.0

3. Strengthening the Joints
The joints were retrofitted in three schemes. In first two schemes, joints were retrofitted by CFRP
Plate and Fabric and in the third scheme joint was retrofitted by introducing tie bars around thecolumn and replacing the concrete by micro-concrete.
3.1 First scheme: Strengthening by CFRP fabrics
Top and bottom Columns were wrapped up to 150 mm from the top of the slab and bottom of the
deeper beams respectively. Beams were wrapped up to 150 mm length in U shape from the face ofthe column. CFRP wraps is not recommended to use for inside corners and minimum 12 mm radius
should be provided for confinement by FRPs. The models had transverse beams at all four sides withslab on top. The beams and column sides were rounded to provide 12 mm radius curvature. The
surface of the beams and columns were smoothened by grinding machine and cleaned thoroughly bybrush before applying Primer. Hardener and Base (1:2 ratios) were mixed thoroughly for 3 minutes
before applying on the prepared surface. This epoxy primer was applied by using a brush and driedfor 24 hours before applying the saturant. The fabrics were cut into 150 mm in width and 300 mm
and 450 mm in length. The hardener and the base (1:2 ratios) of the saturant were mixed and applied
over the primed surface. CFRP fabric had been pressed on to the saturant applied area by hand first
and then was pressed by a surface roller to remove air bubbles. After 30 minutes another coat ofsaturant was applied over the carbon fabrics.
3.2 Second scheme: Strengthening by CFRP plates
In Second scheme, the joints had been strengthened by introducing FRP plate into the joint. The
concrete from beam at column face, up to a distance of 75 mm, were removed to insert the CFRP
plates. CFRP plate was cut into pieces of 300 mm in length. Each of the joints was strengthened byfour plates as shown in Fig. 4.1.15. Exposed surfaces were prepared by the grinding machine.
Hardener and Base (1:2 ratios) of epoxy adhesive had been mixed thoroughly before application. The
mixed adhesive was applied on the CFRP plates and pressed over the joint area to squeeze out themixed adhesive. Additional adhesive had been applied to remove any voids underneath the plate. The
joint concrete was replaced by the micro-concrete after three days of placing the plates and anothertwo CFRP plates were placed after seven days of casting by micro-concrete.
3.3 Third scheme: Strengthening by Tie bar and Micro-concrete
Concrete up to 75 mm from the face of the beam was removed from all sides of the corner joint
region. 8mm Φ tie bars each of 200 mm in length were welded around the column. Let micro
95

concrete was used for the casting. The details of strengthening the joints are shown through Fig.
4.1.24 to 4.1.27.

4. Experimental setup
The experiment had been carried out in Concrete and Strength of Materials Laboratory of BUET.
The models were placed on a steel base plate which had the arrangement of column seat. The baseplate was intended to allow column rotation. The base plate was fixed on a steel beam which was
fixed with the concrete floor.

A hydraulic jack was set to provide axial load on the top of thecolumn. Two sets of steel frame had been designed for this experiment. They were fixed at both sideof the column to arrest any horizontal movement of the column. Two manually operated hydraulicjacks were used to provide cyclic loading at the tip of the beams. Total five dial gauges were used tomeasure the deflection of the beam and columns. First two dial gauges were set near the tip and at
the beam-slab joint of the right beam where as one dial gauge was fixed at the beam-slab joint of theleft beam. Another two dial gauges were set at below the top of column and at 10 cm distance fromthe column-slab joint. Video extensometer was used to measure the rotation of the beam and column
joint. Video extensometer had been placed much closed to the joint due to space constrain andlocation of the steel frame.
The strength of BC joints are influenced by the effective confinement. Column axial load increase
the confinement. The samples were made from different concrete batches as such there concrete
strength had been different. To understand the behavior of all joints under identical condition 10% of
the column capacity (0.1fc´Ag) was provided as the axial load. Axial load had been constantthroughout the experiment. Axial loads and capacities of the control and strengthened specimens areshown in Table 2.
Table 2 Concrete strength of the samples
Model
No

Con1

Con2

Plate1

Plate2MCS1Fabrics 1

Fabrics2

Fabrics3

45

65

35

60

42

50

55

75

Axial
Load
(kN)
The static cyclic loading had been provided by two manually operated hydraulic jacks. The load
had been controlled by measuring the column drift of the top column. 0.25%, 0.50%, 1% and 2% of
the top column drift had been selected to control the load. 5 kN load had been applied while loading
and unloading. However, as the jacks were manually operated, unloading could not be maintained at
the same rate.
96

4. Load deflection response
4.1 Load deflection behavior of beam
Deflections of all the samples were measured by five dial gauges and Video Extensometer had
been used to measure the joint and column rotations. The test continued till the left dial gauges
reached optimum deflection or the jacks reached their maximum lift capacity. The load deflection
behavior of all the beams is similar within the elastics range and exhibits a linear relationship.
Beam of Plate 1 exhibited better ductility than the Con 1. The beam yielded at higher loading thanthe load corresponding to its plastics yield moment in both forward and reverse loading. Beam of
MCS 1 failed much closer to its theoretical yield strength. This may be due to the fact the columns
failed earlier than the beams and the test discontinued before determining the beam ultimate strength.
Beam of Fabrics 1 deflected less with higher loading in forward loading and showed ductile behavior
by deflecting high under the same applied load. Beam failed at higher load than its theoreticalstrength at the joint.

Fig. 2 Load deflection response of beam of Group A
In Group B, beam of Plate 2 deflected 73 % with 86% loading of the Con Beam 2 in forward loading
while it deflected 4% higher than the control beam with 80% excess load in reverse cycle. Beam of
Fabrics 2 deflected 96% with 25% higher loading of the beam of Con 2 in forward loading anddeflected same amount with 40% higher loading in reverse loading. Beam of Fabrics 3 deflected
27% as high as the control beam in forward loading while loading was 29% higher. In all the cases,
the beams yielded at higher loading than their theoretical yield strength.

97

Fig. 3 Load deflection response of beam of Group B
Load-Deflection behavior of beams is illustrated by hysteresis loops through Fig. 4.

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Fig. 4 Load deflection response of beam
By analyzing the hysteresis loops of the load-deflection, it is found that, within the elastic limit, all
the models have non-degrading curve. But in subsequent circles, the loops exhibit stiffness
degradation. The characteristics of the hysteresis loops can be analyzed by the slopes of the loading
cycles. In order to evaluate the stiffness of the beams, slope of the forward loading cycles aremeasured from the load-deflection curve of each cycle. The slope is determined by drawing tangent
on forward loading curve. It is found that, the slope gradually decreases as shown in Fig. 5.
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Fig. 5 Forward slope of load deflection cycle of beams.
Secant stiffness is defined as the ratio of the strength to the maximum displacement. Secant stiffnessof the beams for each load-deflection cycle is measured by considering the maximum load anddeflection of both forward and reverse loading and it is found that beam stiffness decreases in eachsubsequent cycle as shown in Fig. 6.

Fig. 6 Stiffness of beams.
4.2 Load deflection behavior of column
Load deflection response of columns of Group A is presented in the following Fig. 7. Column ofPlate 1 experienced enhanced column shear capacity. Column of Plate 1 experienced 6.5% excess
column shear force while deflecting 13% less in forward loading and 8% high column shear force
while deflecting 93% higher in reverse loading than the column of Con 1. Column of MCS 1
displayed similar behavior of the control model in reverse loading while it deflected 32% highagainst 17.81% excess loading than that of Con 1 in forward loading. Column of Fabrics 1 deflectedsame amount against 42.2% excess loading in forward cycle. It deflected 16% more than the columnof Con 1 against 46% higher loading. Column of Fabrics 1 experienced high column shear againstsame deflection compared to the column of Con 1. In all the cases, the columns experienced highshear than their theoretical yield strength except MCS 1 where in later case the column failed at its
yield limit.
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Fig. 7 Load deflection response of column of group A.
The load-deflection response of columns of Group B is shown through Fig. 8. Column of Plate 2
deflected 87% with 84 % loading of the column of Con 2 in forward loading cycle. It experienced
19% higher column shear force than that of Con 2 while deflected only 48.6% of the Column of Con
2 in reverse loading. In forward loading, Column of Fabrics 2 experienced 52% higher shear force
than that of Con 2 with the same deflection in forward loading. The column experienced maximum
84% shear force while deflecting 43.4% of the same column of Con 2 in forward loading. The same
column experienced column shear 22.7% higher than that of Column of Con 2 while deflecting 9.5%
more than the same column. Column of Fabrics 3 deflected 7.5% higher than column of Con
2against 13.3% excess loading in forward cycle but the same column deflected 5.7% higher against
19.1% higher loading in reverse loading cycle. In all the cases, the columns experienced higher shear
forces than their theoretical strength.

Fig. 8 Load deflection response of column of group B.

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The behavior of the columns can be analyzed by the load-deflection hysteresis loops plotted for eachcycle. Hysteresis loops for the columns are illustrated in the following Fig. 9.

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Fig. 9 Load deflection response of column.
Slope of the Load-Deflection cycle is determined to evaluate the magnitude of stiffness degradationof the column. The slope is determined as it was determined for beams. The slopes of the LoadDeflection Cycles of the columns of Group A and B are illustrated by Fig. 10 and 11.

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Fig. 10 Slope of load deflection curve o forward loading (Group A)

Fig. 11 Slope of load deflection curve o forward loading (Group B)
Column secant stiffness for each cycle is measured by considering maximum applied moment
against maximum deflection. The computed stiffnessesare illustrated by Fig. 12 and 13.

Fig. 12 Column stiffness (Group A)

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Fig. 13 Column stiffness (Group B)
The slopes and the secant stiffness of the column decreased with the commencement of cycles. The
column shear had been measured indirectly from the load applied at the tip of the beam. Upon
failure, beams were unable to transfer load on the column. This led to increased column shear
without deflection resulting in higher slope and stiffness in subsequent cycles. The rate of loosing
stiffness in subsequent cycles indicates the collapse behavior of the member. Percentage of initialstiffness of the column are shown Fig. 14 In Group A, Plate1 kept higher rate of initial stiffness
compared to Con 1 at higher stage of loading. Considering the maximum load provided in eachcycle, columns of MCS 1 and Fabrics 1 retained high initial stiffness compared to the column of Con
1. In Group B, considering the maximum load provided in each cycle, columns of Plate 2, Fabrics2
and 3 retained high initial stiffness compared to the column of Con 2.

Fig. 14 Percentage of initial column stiffness (Group A and Group B)
4.2 Load deflection behavior of column
Rotation experiences by the joints in each cycle depend on the magnitude of the applied moment.
Rotations of the beams and columns at the joint against corresponding loading (applied moment)
were measured and evaluated by Video Extensometer. Maximum load and corresponding rotation
had been found by analyzing the computer output.
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Applied maximum moment and corresponding beam joint rotations of Group A are shown in thefollowing Fig. 15 Beam joint of Con 2 rotated 53.4% more against 22.4% excess loading than that of
Con 1 in forward cycle whereas the same beam joint rotated further 75% against the same loading
compared to the joint of Con 1. M-Φ curve of both the joint indicates that the ductility increases with
the increased concrete strength. Rotational ductility of Con 1 was 3.45 whereas it was 6.45 for Con
2.

Fig. 15 Applied moment vs rotation of joint (Group A)
Joint of Plate 1 rotated 86% of the Con joint 1 while applied load was 16.1% higher in forward
loading. The same joint rotated 109% higher against 8% excess loading compared to the same
control joint. Joint of MCS 1 rotated additional 46.5 % when the applied moment was 17.8% higher
and it rotated further 89% against the same applied moment in reverse loading compared to the con 1
joint. The joint of Fabrics 2 rotated 32.3 % more against 46 % higher applied moment in forward
loading compared to Con 1 joint. The same joint rotated additional 45.5 % against 42.2 % excess
loading in comparison to Con joint 1.
Applied maximum moment and the corresponding rotation of the joints in each cycle of Group B are
illustrated in the following Fig. 16.

Fig. 16 Applied moment vs rotation of joint (Group B)

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Joint of Plate 2 rotated 46% of the joint of Con 2 in forward loading while it is subjected to 83% loadof the control joint. It rotated 70% of the control joint in reverse loading against 74% excess load of
the control joint 2. Joint of Fabrics 2 rotated the same against 52.5% excess load of the control joint
in reverse cycle. The same joint had 38% rotation of the control joint in forward loading while it was
subjected to 78% load of the control joint. Fabrics 3 joint rotated 75% of the control joint 2 against
11.78% excess loading in forward loading cycle. The rotation of the same joint was 21.4% higher
against 19% excess loading in reverse loading.
Hysteresis loops of Con2, Plate 2, MCS 1, and Fabrics 3 column and beam joints are shown throughFig. 17 to Fig. 32.

Fig. 17 Rotation of beam joint of con 2

Fig. 18 Rotation of column joint of con 2
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Fig. 19 Relative rotation of beam and column joint of con 2

Fig. 20 Difference of beam and column joint rotation of con 2

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Fig. 21 Rotation of beam joint of plate 2

Fig. 22 Rotation of column joint of plate 2

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Fig. 23 Relative rotation of column and beam joint of plate 2

Fig. 24 Difference in beam and column joint rotation of plate 2

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Fig. 25 Rotation of beam joint of fabrics 3

Fig. 26 Rotation of column joint of fabrics 3

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Fig. 27 Rotation of beam and column joint of fabrics 3

Fig. 28 Difference in beam and column joint rotation of fabrics 3

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Fig. 29 Rotation of beam joint of MCS 1

Fig. 30 Rotation of column joint of MCS 1

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Fig. 31 Relative rotation of beam and column joint of MCS 1

Fig. 32 Difference in beam and column joint rotation of MCS 1
It is found that the column and joint rotate equally for all the models with in the elastic limit but theirrelative rotations are different in subsequent cycles. The rotation of the column and joints are plotted
together to evaluate their relative rotation. The magnitude of maximum rotation experienced by thebeam joints in each cycle are illustrated through Fig. 33 and 34.

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Fig. 33: Rotation of beam joints in forward cycle (Group A)

Fig. 34: Rotation of joints in forward cycle (Group B)
Rotational stiffness means the moment required to cause unit rotation. High stiffness means highresistance to deflection. Secant Rotational Stiffness of the joint is computed by averaging the
maximum rotation against the applied maximum moment.
Rotational stiffness of Group A and B are illustrated through Fig. 35 and 36. The rotational stiffness
joint decreased with the commencement of the cycles for all samples in Group A and B. Plate 1
exhibited high rotational stiffness compared to the joint of Con 1considering the concrete strength.
Rotational stiffness of the joint of MCS 1 was less compared to stiffness of the joint of Con 1.

Fig. 35: Joint stiffness of Group A

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Fig. 36: Joint rotational stiffness of Group B
In Group B, rotational stiffness of the joint of Plate 2 was higher than that of the joint of Con 2 in all
cycles of loading. Rotational stiffness of the joint of Fabrics 2 was slightly less compared to therotational stiffness of the joint of Con 2 considering the concrete strength. Rotational stiffness of thejoint of Fabrics 3 is higher than that of the control joint considering the concrete strength for all thecycles.

Fig. 37: Percentage of initial rotational stiffness
It is normal for the structural member to lose some stiffness under cyclic loading but excessive lossof stiffness can lead to a collapse. Percentage of initial stiffness of the joints in subsequent cycles iscomputed to measure the rate of loss of stiffness from the initial value and the values are illustrated
in Fig. 37. The joints of Con1 and Con 2 retained 43% and 29% of the initial stiffness. Plate 1 and 2
retain 37% and 59% of the initial stiffness. Fabrics 1, 2 and 3 retain 29%, 60 % and 18.86% of theinitial stiffness.
5. Conclusions
Behavior of the interior joints under cyclic loading, both of the control specimens and the retrofitted
ones, were investigated based on the performed test result. Following conclusions are drawn basedon the experiment and analysis of the results:

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a. Diagonal cracks occurred under cyclic loading in interior joints which lack in shear
reinforcement. Diagonal cracks of such specimen traveled to transverse beams at higher stageof loading and caused the transverse beam to fail.
b. Ductility of beams, columns and joints increased with increased concrete strength. Rotational
ductility of the joints increased 2.5 to 2.75 times by increasing 54% of the concrete strength.
c. Stiffness of the joints decreased gradually under cyclic loading. But, Stiffness of the joint did
not degrade at the same rate for all the retrofitted samples.
d. The rotational capacity of the joints can be increased by strengthening the BC joints by CFRP
Plates. Rotational stiffness of these joints was higher than that of the control models.
Rotational stiffness of these joints also decreased gradually. From M-Φ curve, it is found that
BC joints strengthened by CFRP Plates show ductile behavior.
e. CFRP Plate is effective in resisting diagonal crack travelling to transverse beams. Diagonal
crack traveled up to the CFRP Plate and changed their direction. Beams from both themodels, failed at the Plate-Beam joints. It can be concluded that location of plastic hinge
may be altered by altering the length of the plate.
f. Moment capacity of the joints can be enhanced by additional tie bar in the joint and microconcrete depending on the initial concrete strength. Joint retrofitted by micro-concrete andadditional tie bar underwent large deformation before failure exhibiting ductile behavior of
the joint.
g. Joints can be strengthened by CFRP Fabrics. Moment capacities of the joints strengthened by
CFRP Fabrics were higher against the same rotations compared to the control models.
h. Joints strengthened by CFRP fabrics undergo greater deformation than the control joints.
Joints strengthened by CFRP fabrics exhibited better ductile behavior than the control
models.
i. The mode of failure may be shifted from column to beam by strengthening joints by CFRP
fabrics. Column shear and flexural capacity can be increased by CFRP wrapping.
j. Micro-concrete and concrete have good bonding strength as micro-concrete was used forstrengthening joints by CFRP Plates and additional tie bar. Micro-concrete and concrete
joints failed at higher stage of loading and associated with plate debonding.

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PART-VI

A REVIEW ON FACTORS AFFECTING CYCLONEEVACUATION DECISION AND BEHAVIORALRESPONSEBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Tanmay Das
Uttama barua
Mehedi Ahmed Ansary118

1. Introduction
Coastal areas are mostly threatened by natural disaster like cyclone which leads the resident of theareas towards evacuation to get out of high-risk areas as quickly and safely as possible. The
complication or success of evacuation process mostly depends on evacuation decision of theresidents and their behavioral response which is again dependent on or influenced by several otherfactors. To ensure successful cyclone evacuation and to reduce loss of lives it is necessary to
influence the residents to evacuate as quickly as possible. To increase efficiency in cyclone
evacuation and to influence people to evacuate safely at an event of cyclone, it is necessary tounderstand their behavior. Over the last few decades, substantial researches have been carried out
focusing on cyclone evacuation including interpretation of warning messages and risk by people, andconsequent protective actions they take (Dash & Gladwin, 2007). Most of the literatures related tocyclone evacuation decision and behavioral response have concentrated on understanding people onwhether they evacuate or not as well as the reason for such decision and response (Hasan et al.
2010). This review of literatures has focused on the most common and primary factors affecting
cyclone evacuation decision and behavioral response.
2. Factors Affecting Cyclone Evacuation Decision and Behavioral Response
In different literatures most common factors considered to study cyclone evacuation decision andbehavioral response are: household characteristics (location of residence, type of residence andfamily size, length of residence, family members’ education status, social capital), transportation
inadequacy, previous experience and risk perception, evacuation characteristics (type of evacuation
notice, source of information, place and time of evacuation, sense of place for evacuation shelter).
Table 1 summarizes the literatures reviewed in this research including context of the research,
research strategy, data collection procedure, factors considered and research limitations. In thefollowing sections, the factors affecting cyclone evacuation decision and behavioral response fromthe review of literatures are discussed as well as the limitations of those researches.
2.1Household characteristics
2.1.1Location of residence
Location of residence has been considered as an important factor in many researches influencing
evacuation decision after cyclone. Concept of residence location varied in different researches.
Hasan et al. (2013); Hasan et al. (2010); Sadri et al. (2013); Xu et al. (2016) have found thatgeographic location (region or state) of residence play important roles in evacuation decision making
has significant influence on household’s decision to evacuate or stay at home. The researchers havefound significant difference in evacuation decision by households living in Alabama, Mississippi,
Florida and Louisiana after the passing of Hurricane Ivan through the area of the west of Gulf
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Shores, U.S. in September 2004.Several studies have found that evacuation decision after cyclone
depends on proximity to hazard location (Dash & Gladwin, 2007; Huang et al., 2012; Lindell et al.,2005). Huang et al. (2012) have found that rate of evacuation lessened as the region’s distance fromthe coast and projected point of landfall increased after Hurricane Lilihit Louisiana parishes ofVermilion and Cameron and the Jefferson and Chambers, Texas counties of Orange, U.S. They havealso found strong correlation of evacuation decisions with geographic features, i.e. proximity tolakes, rivers and coast. Dash & Gladwin (2007) have found that in addition to proximity to hazard
location, geographic location, such as proximity to highways and exit routes play a significant role inunderstanding evacuation decision making. Thus, the factors related to location of residence
considered in different literatures for understanding cyclone evacuation decision include geographic
location (region or state); proximity to hazard location; proximity to lakes, rivers, and the coast; andproximity to highways and exit routes.
2.1.2 Type of residence and family size
In several researches, type of residence and number of family accommodated in the residence havebeen considered together to understand their cyclone evacuation decision(Xu et al., 2016). Lindell etal. (2005) have considered five types of residence in their research considering family size, structure
type and number of storey (i.e. separated single family, multifamily with one or two stories,
multifamily with three or more stories, mobile home, and other) to study evacuation decision after
Hurricane Lili in the Louisiana parishes of Vermilion and Cameron and the Jefferson and Chambers,
Texas counties of Orange, U.S. The researchers have found non-significant correlation between
residing in a mobile home and evacuation decision. That means, mobile home residences haveneither a direct nor anshared effect on evacuation decisions. Hasan et al. (2013); Hasan et al. (2010)
have considered three types of residence in their researchconsidering type and family size (i.e. single
family home, multiple family unit and mobile home) to understand evacuation decision byhouseholds after the hit of Hurricane Ivan to the region of the west of Gulf Shores, U.S. in
September 2004. In contrary to the findings of Lindell et al. (2005), the researchers have foundfamilies living in a mobile house had a higher probability to evacuatecompared to the households
living in other type of houses. Thus the factors related to type of residence and family size
considered in different literatures for understanding cyclone evacuation decision include family size
(single family, multi-family), structure type (detached, mobile or manufactured) and number ofstorey.
2.1.3 Length of residence:
Factors such length of residence appears to be less significant when studied separately; however,understanding how these factors impact other factors such as risk perception or social capital is120

important. The review suggest that these factors are fortified or weakened in the presence or absenceof each other and consideration should be given to such factors when planning communication
strategies, and emergency evacuation plans.
2.1.4 Family members’ education status
Different literatures have found significant influence of family members’ education status on cyclone
evacuation decision making process. From the literatures it has been seen that families having
members with post graduate or graduate degree have the higher probability of evacuation. It isfurther noticed that family members’ with low in education are more likely to take the risk of notevacuating if the evacuation notice is voluntary. However, in case mandatory evacuation notices thedecision of evacuation is quite uniform and has higher percentage of evacuees. Hasan et al. (2010)in
their research have showed that family members’ with high education status perceive the risk
cyclone more seriously, thus, have a higher percentage of evacuees.
2.1.5 Social capitalSocial capital has been considered as one of the major factors defining the cyclone evacuation
decision making process in different literatures. The fundamental consequences of resilient social
interactions are variable and the research proposes that social factors may have both positive andnegative influences on evacuation decisions. Patterns of social capital may include religious or ethnic
affiliations, community connections such as civic organizations, community involvement,
participation in school functions or interactions with friends and family. These social relations may
play a substantial role in apprising decisions during evacuation states and should be reflected when
designing research to inform future evacuation initiatives.Membership in religious or ethnic groups
is one example of social capital and may play animportant role in affecting decisions about
evacuation. These memberships may help to inform a decision by providing targeted information in anative language or to provide resources such as information about where, when and how other’s
within the affiliation are evacuating. They may also provide transportation, financial, or spiritual
backing during times of need and smooth communication that allows families or affiliation members
to stay in touch during this time of crisis. Specially, these links seem to increasereliance level among
members and there is generally acasual communication system by which information and resources
flow to pledge that all the members within the community are well taken care of (Litt, 2008). Theselines of communication are often critical and very effective in situations of emergency and cangreatly influence evacuation decisions (Airriess et al., 2008; Chamlee‐Wright & Storr, 2009;
Eisenman et al., 2007). Social capital is seen to be influenced by socio economic conditions of thehouseholds. Households of a particular society normally have almost same type of socio economic
condition. Actually on the merit of socio economic conditions social bindings are normally built.
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Hasan et al. (2013); Hasan et al. (2010); Huang et al. (2012); Xu et al. (2016) have considered households’ income status which eventually contributes to build social capital. Thus, social capital cannot
be overlooked when evaluating the level of risk. Information gets fabricated by social settings that
influence how individuals perceive the level of risk. As “potential”threats become realized threats,
and as abstract, vague ideas of potential damage become real, levels of danger may increase(Dash &Gladwin, 2007). For these reasons, decision making becomes more complex as decisions are in part
influenced by “Risk Perception”, which itself is affected by fabricated informationrepresenting thethreat as real.
2.2Transportation inadequacy
Lack of transportation is one of the major factors that cause failure in evacuation planning.
Households residing in the areas lack in transport facilities hardly have the choice of proper
evacuation at the time of cyclone. The statement is very much evident in underdeveloped countries(e.g. Bangladesh) where lack of transportation and consequent loss of lives and resources is acommon affair. However, transportation inadequacy was strongly associated with failure to evacuate
in areas of poverty in New Orleans during hurricane Katrina. This issue included a pure lack oftransportation as well as deficiency of financial resources to afford gas for the car (Baker, 1991;
Bourque et al., 2006; Brezina, 2008; Eisenman et al., 2007; Elder et al., 2007; Elliott & Pais, 2006;
Spence et al., 2007; West & Orr, 2007). It is also noted that in areas of extreme poverty in New
Orleans no provisions were made for the poor and thus left many people stranded with no way out
during hurricane Katrina and is attributed to higher risk of injury and death (Brezina, 2008; West &Orr, 2007). In the study of Brezina (2008), professional interviewers administered face to faceinterviews with 394 participants who evacuated after hurricane Katrina made landfall in New
Orleans and 247 evacuees who left prior to the storm. This sample was drawn from a pool of greaterthan 8000 residents who were housed in shelters following the storm. Of the 394 study participants
who evacuated after the storm, 298 participated in post storm, government assisted evacuation. Of
these 38 stated that they did not have transportation to leave and 45 stated that looking back, theywould not have been able to find a way to leave (Brezina, 2008).
2.3Previous experience and Risk Perception
Information about cyclone alone is not adequate to motivate evacuation action rather the informationmust be interpreted into a definite conception of awaiting danger associated with cyclone hit. Thus,
risk perception is one of the primary factors in understanding the evacuation decision making
process. The review findings reveal that normally when residents feel unsafe to stay in their homesduring the storm, they evacuate and those who feel safe tend to stay. Risk can be seen as a technicalnotion calculated based on the probability of events and the magnitude of specific consequences
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(Kasperson et al., 1988), others express it based on its social sense, characterized by worry, dread,
angst, concern, or anxiety (Jaeger, Webler, Rosa, & Renn, 2013) whereas other researchers view it as
more of a social notion that takes into account context and culture in the understanding of what isdangerous (Turner, 1979). In his 1987 article, risk perception (Covello et al., 1987) asserts that“whereas technologically sophisticated analysts employ risk assessment to evaluate hazards, themajority of citizens rely on intuitive risk judgments, typically called risk perception. However, thisperception may be reformed by many factors including community, racial or religious affiliations
and also by the interpretation of media messages or simply a misinterpretation of the possible
severity of the storm. Individuals perceive information by their own social lenses created by their
specific cultural context, and as a result, different people may well interpret the similar informationand messages differently (Dash & Gladwin, 2007).
Adeola (2009a, 2009b)study has revealed that most of the residents underestimated the impact of thestorm and never awaited a 15 ft. storm surge and breeches to the levees. In this case, the wrongnessof the perception of risk put great numbers of residents at danger needlessly and led to avoidable
death. In his study, Hasan et al. (2010) have suggested that perception of risk perception largely
depends on the trust worthiness of the source of information. Most of the risk perception problems
arise from complications in receiving and processing information. Interestingly, Gender serves as asignificant factor in the personalization of risk. Bateman and Edwards (2002) have revealed that theperception of risk for men and women is significantly different. The authors quoted that “...Women
were more likely to evacuate than men, in part, because compared to men, they perceive theirresidence to be at greater risk, and were more likely to have developed a household evacuation
plan...”.
Previous cyclone experience influences the risk perception process significantly. A community or anindividual having a bad evacuation experiences result in a lower probability of evacuation for futurestorms (Dash & Gladwin, 2007). In all studies, previous cyclone experience led to inaccurate risk
perception and optimism established on previous experience with riding out the storm and put more
residents in danger when they decided not to evacuate (Adeola, 2009a, 2009b; Bourque et al., 2006;
Elder et al., 2007). Horney et al. (2010) have explained in post cyclone research following hurricane
Isabel in North Carolina in September 2003, that older residents had a lower perception of risk that
led to their failure to evacuate because the last major hurricane to affect their region was Hurricane
Hazel in 1954. In a brief it can be said risk perception and previous cyclone experience plays acritical role in evacuation decision making. It is thus vital to recognize how people shift fromreceiving evacuation orders to decision of evacuation grounded on their level of risk perception.
2.4Evacuation characteristics
1232.4.1 Type of evacuation notice
Evacuation notice and its type is a very important factor on which households’ evacuation decision issignificantly dependent. It has been an important focus of many researches research on cyclone
evacuation decision (Dash & Gladwin, 2007). To understand evacuation decision by households after
the passing of Hurricane Ivan through the province of the west of Gulf Shores, U.S. in September
2004, Hasan et al. (2013); Hasan et al. (2010)have considered whether a cyclone evacuation notice
was received, as well as type of the evacuation notice received (mandatory or voluntary). The
researchers have found that households receiving evacuation notice are more likely to evacuate thanthose who do receive any order. Additionally they have found that households receiving mandatory
notice are more likely to evacuate than those receiving voluntary evacuation notice.To study
evacuation decision in Louisiana, U.S. after Hurricane Ivan in September 2004, Sadri et al. (2013)
have considered the time of evacuation notice received by households.The researchers have foundthat the timing of evacuation notice havesignificant influence on the evacuees’ mobilization time, i.e.
evacuees getting early evacuation notice get enough time for evacuation process andpreparation.Thus the factors related to type of evacuation notice considered in different literatures forunderstanding cyclone evacuation decision include reception of cyclone evacuation notice, type ofevacuation notice (mandatory or voluntary) and timing of evacuation noticereception.
2.4.2 Source of information
Several literatures included a discussion of the importance of households’ perception of the source of
information. The decision maker dimension emphases on the competency of decision makers toprocess and understand information (Dash & Gladwin, 2007). This above stated belief is basically
psychologically dominated and attempts to measure the cerebral capability of decision makers’.
During cyclone households can receive information from various sources (e.g. radio, TV, local
government officials, news media, and their community contacts including neighbors, civicorganizations, religious affiliations, etc). When households trust the source of the information they
are more likely to evacuate and having multiple channels of information may develop evacuation
decisions (Spence et al., 2007). Previous bad experience with a particular source of information
regarding cyclone evacuation reduces the probability of decision to evacuation. For example, toexamine the media credibility in a study conducted by the American Red Cross Disaster Relief
Operations Control for Columbia, SC following hurricane Katrina, in which participants who
evacuated to Columbia South Carolina were identified. The evacuees reported that they trusted
television media reporting of the size and strength of the cyclone because they could see it. Elder etal. (2007) stated “...It was right there in front of your eyes showing where it is coming and how fast
it’s coming...”.
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Households’ may have issues of trust regarding local officials or even the media as a potential sourceof information. This distrust is mainly based on previous experience and may be augmented whenevacuation for previous cyclones are proven superfluous (Baker, 1991). Several factors that
contribute for distrust local government officials were identified such as language barriers, socioeconomic status, and minority preference for a particular information source (Peguero, 2006; Spence
et al., 2007). This distrust may be a barrier to evacuation and lead to higher numbers of casualties
and injuries. Baker (1991)have indicated that households who did evacuate, at the warning of public
officials, did so because the information provided to them, convinced them of the danger or theywanted to be compliant or obedient.
2.4.3 Place and time of evacuation
After a cyclone, evacuation decision of people greatly depends on the place where they areevacuating and for how long (Baker, 1991). This is also dependent on the time of their evacuation. In
September 1988 Hurricane Gilbert struck in Cancun, Mexico. After getting evacuation notice,majority of the local people were not willing to evacuate from their home due to their concern for thesafety of their belongings. As a result, many of them were rescued afterwards by the army. Majorityof the evacuees found shelter in the homes of friends, neighbors, and relatives in Cancun, Mexico,and very few of them stayed away from their homes for more than seven days (Aguirre, 1991). At
the event of hurricane Katrina, vast majority of residents evacuated before the storm that were livingin the affected region (Burnside et al., 2007; Eisenman et al., 2007; Elliott & Pais, 2006). This
response was more common in the City of New Orleans compared to others due to their perceived
risk and prior planning (Brezina, 2008; Burnside et al., 2007; Elliott & Pais, 2006; Jacob et al., 2008;
Vu et al., 2009). Most of the evacuees took shelter in an apartment, hotel or temporary shelter or insomeone else’s home for more than one month after the storm due to late recovery of their home
(Adeola, 2009a, 2009b; Elliott & Pais, 2006; Jacob et al., 2008).
Hasan et al. (2013) have studied evacuation decision by households after the passage of Hurricane
Ivan through the region of the west of Gulf Shores, U.S. in September 2004. The researchers havefound that households waited longer for deciding on evacuation to public shelter than thoseevacuating to other types of shelters which reflect their preference to take the risk of remaining
longer at home than evacuating to the public shelter. Sadri et al. (2013) have studied evacuation
decision in Louisiana, U.S. after Hurricane Ivan in September 2004 and have found that households
are expected to evacuate early (in an hour or less) as opposed to evacuating later (more than 24 h)once they choose to evacuate due to their perceived risk and prior planning. Among them most of theevacuees who decided to evacuate to a public shelter took least amount time for evacuation

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compared to those evacuating to someplace safer. Thusit can be said that timing of evacuation ofpeople depends mostly on their perceived risk and prior planning as well as place of evacuation.
2.4.4 Sense of place for evacuation shelter
Sense of place plays a vital role in cyclone evacuation decision making as residents tend to move toplaces with environmental similarity to their home and community. Vu et al. (2009)in his study has
revealed that evacuees seek out refuge in an area with similar ethnic, cultural and religious
background where they might find acceptance and resources to help them during this time ofdisplacement. In his research he further found that the during hurricane Katrina 78% of VietnameseAmerican community in New Orleans evacuated to Houston during hurricane Katrina with theprimary decision based on the fact that Houston has the second largest Vietnamese-American
population in the United States. Findings of Chamlee‐Wright & Storr (2009) also support the above
outcome. The research showed that communities tried to stay in touch and made plans to return totheir homes and rebuild their communities as soon as possible following the storm.
3. Limitations in Researches Related to Cyclone Evacuation Decision
Several of the significant potential research limitations noted in this literature review (Table 1)
include cognitive biases, social response, sample size or distribution, characteristics of the sample
population considered, etc for research that does not accurately represent the population. It is
important to understand how these features affect the precision of information that is stated and to
think about ways to lessen these issues to improve research outcomes. Social response and cognitive
biases may appear when survey respondents have difficulty accurately recalling or articulating
responses to survey questions or when they provide answers that are inaccurate because of
dissonance (Eisenman et al., 2007; Elder et al., 2007). Dissonance may occur in research such as in astudy of African American’s evacuated after Katrina to Columbia, in which the respondentsselfreported the event before and after the hurricane (Elder et al., 2007). Bias may also take place whenevacuees answer survey questions in a way that they feel will please the surveyor. These biases may
be lessened when surveys are conducted immediately after the event, leaving less elapse of time forthe evacuees to fabricate their decision or to think about the results of their actions (Spence et al.,2007). In the Spence’s (2007) research following hurricane Katrina, the data was collected over an
extended period of time allowing some respondents to provide response hours after the event whileother information was gathered weeks after the event due to the time consuming nature of finding
and interviewing cyclone evacuees. The recall accuracy should be enhanced and bias lessened bycollecting data at a single time point as close to the event as possible (Spence et al., 2007).
One of the most significant factor emergency management planning is communication strategies.
Furthermore, interpretation of media messages may be more important than the number of messages
126

heard (Aguirre, 1991). It is vital to understand how demographic data coupled with variances insocial capital may affect how emergency media messages are interpreted (Aguirre, 1991). It is also
critical to understand the population represented in this research. Often post-cyclone survey
respondents are not representative of the entire population (Elder et al., 2007). In certain situations,the financially or physically restrained people are unable to evacuate due to issues with lack oftransportation, financial resources or physical issues to allow them to travel away from their homes.
In other situations, when post disaster surveys are completed immediately following a cyclone, only
the people with the financially advanced condition returned to their homes because those with lessfinancial resources may be unable to make repair to their property to allow them a safe place toreside. These disagreements may lead to a small sample size with racial or monetary bias due to aless representation of the poor (Elliott & Pais, 2006).
For example, in the study of Aguirre (1991) on hurricane Gilbert in Cancun, 431 interviews wereconducted of residents of Cancun one year following the cyclone to understand experiences ofresidents during and after the cyclone. The sample was derived by identifying 18 sampling cells andrandomly interviewing at least 25 people residing within those cells. The model identified that themost impoverished residents of Cancun were underrepresented in the sample. For thisunderrepresentation of the sample to be considered the assessed impact of the dangers of the study
was conservative (Aguirre, 1991). Thus, it is important to understand and note these potential biases
in the research. Also, researchers should adjust analysis to take into consideration the biases that may
affect the accurateness of the sample collected by surveys. Surveyors should develop skill to collect a
representative sample for proper outcome of the research.
4. Conclusion
As, the decision making process prior to cyclone hit is multifaceted phenomenon, in this paper themost common and primary factors that have been considered by the researchers to assess the cyclone
evacuation decision making process are considered. In this context, our major findings suggestthat
cyclone evacuation compliance is governed by a number of overlapping factors considered underthethemes of household characteristics (location of residence, type of residence and family size,
length of residence, family members’ education status, social capital), transportation inadequacy,
previous experience and risk perception, evacuation characteristics (type of evacuation notice, sourceof information, place and time of evacuation, sense of place for evacuation shelter).
From the review it has been found that the factors related to location of residence considered indifferent literatures for understanding cyclone evacuation decision include geographic location(region or state); proximity to hazard location; proximity to rivers, lakes and coast; and proximity tohighways and exit routes. The factors related to type of residence and family size considered in127different literatures for understanding cyclone evacuation decision include family size (single family,
multi-family), structure type (detached, mobile or manufactured) and number of storey. Families
having members with post graduate or graduate degree have the higher probability of evacuation.
Households residing in the areas lack in transport facilities hardly have the choice of proper
evacuation at the time of cyclone. Social capital plays a significant role in determining the decisionof evacuation. Evacuation decision making process largely depends on the risk perception. However,risk perception is affected by different factors. Few researches showed that in case of mandatory
evacuation notice the perception of risk is great thus, results in greater probability of evacuation.
Risk perception on the other hand is greatly dependent on previous cyclone experience as well as
their preparedness planning. Factors related to type of evacuation notice considered in different
literatures for understanding cyclone evacuation decision include whether a cyclone evacuation
notice was received or not, type of the notice received (mandatory or voluntary) and whether thehouseholds received an evacuation notice early enough or not.During cyclone households canreceive information from various sources (e.g. radio, TV, local government officials, news media,
and their community contacts including neighbors, civic organizations, religious affiliations, etc).
When households trust the source of the information they are more likely to evacuate and having
multiple channels of information may develop evacuation decisions. Timing of evacuation of people
depends mostly on their perceived risk and prior planning as well as place of evacuation.
Among different limitations mentioned in researches related to cyclone evacuation decision andbehavioral response most significant is the problem of recall.After a cyclone people face difficulty inrecalling their experiences throughout the evacuation process exactly as time elapse. In such cases,
researchers have to rely on the altered memories of the evacuees. Todeal with this matter, it isnecessary to undertake more systematic pre-event research focusing onevacuation needs.
Additionally, a systematic approach should be developed to get in cyclone hit areas for datacollection at a moment’s notice during an event or afterwards.
In spite of having research limitations the studies regarding the decision making process at the eve of
cyclone event give in depth understandingof the factors that determine the evacuation decision
making. This review will further guide the researchers studying cyclone evacuation decision andbehavioral response regarding consideration of factors for their researches. This will also guide thecorresponding authorities to prepare a data log to keep ready for collecting data after a cyclone eventfor further improvement of cyclone preparedness form lesson learned. This will enable decision
makers toward people centric cyclone preparedness considering their context as well as individual’sand household’s behavioral response.

128

References
Adeola, F. O. (2009a). Katrina Cataclysm: Does Duration of Residency and Prior Experience AffectImpacts, Evacuation, and Adaptation Behavior Among Survivors? Environment andBehavior, 41(4), 459-489.
Adeola, F. O. (2009b). Mental Health & Psychosocial Distress Sequelae of Katrina: An Empirical
Study of Survivors1. Human Ecology Review, 16(2), 195-210.
Aguirre, B. E. (1991). Evacuation in Cancun during hurricane Gilbert. International Journal of Mass
Emergencies and Disasters, 9(1), 31-45.
Airriess, C. A., Li, W., Leong, K. J., Chen, A. C.-C., & Keith, V. M. (2008). Church-based social
capital, networks and geographical scale: Katrina evacuation, relocation, and recovery in a
New Orleans Vietnamese American community. Geoforum, 39(3), 1333-1346.
Baker, E. J. (1991). Hurricane evacuation behavior. International Journal of Mass Emergencies andDisasters, 9(2), 287-310.
Bateman, J. M., & Edwards, B. (2002). Gender and evacuation: A closer look at why women are
more likely to evacuate for hurricanes. Natural Hazards Review, 3(3), 107-117.
Bourque, L. B., Siegel, J. M., Kano, M., & Wood, M. M. (2006). Weathering the storm: The impact
of hurricanes on physical and mental health. The Annals of the American Academy of
Political and Social Science, 604(1), 129-151.
Brezina, T. (2008). What went wrong in New Orleans? An examination of the welfare dependency
explanation. Social Problems, 55(1), 23-42.
Burnside, R., Miller, D. S., & Rivera, J. D. (2007). The impact of information and risk perception on
the hurricane evacuation decision-making of greater New Orleans residents. Sociological
Spectrum, 27(6), 727-740.
Chamlee‐Wright, E., & Storr, V. H. (2009). “There's no place like New Orleans”: Sense of place andcommunity recovery in the Ninth Ward after Hurricane Katrina. Journal of Urban Affairs,
31(5), 615-634.
Covello, V. T., von Winterfeldt, D., &Slovic, P. (1987). Communicating scientific information about
health and environmental risks: Problems and opportunities from a social and behavioral
perspective. In Uncertainty in risk assessment, risk management, and decision making (pp.
221-239). Springer US.
Dash, N., & Gladwin, H. (2007). Evacuation decision making and behavioral responses: Individual
and household. Natural Hazards Review, 8(3), 69-77.

129

Eisenman, D. P., Cordasco, K. M., Asch, S., Golden, J. F., & Glik, D. (2007). Disaster planning andrisk communication with vulnerable communities: lessons from Hurricane Katrina. American
journal of public health, 97(Supplement_1), S109-S115.
Elder, K., Xirasagar, S., Miller, N., Bowen, S. A., Glover, S., & Piper, C. (2007). African
Americans’ decisions not to evacuate New Orleans before Hurricane Katrina: A qualitative
study. American journal of public health, 97(Supplement_1), S124-S129.
Elliott, J. R., & Pais, J. (2006). Race, class, and Hurricane Katrina: Social differences in human
responses to disaster. Social Science Research, 35(2), 295-321.
Goodwin, B. S., & Donaho, J. C. (2010). Tropical storm and hurricane recovery and preparedness
strategies. ILAR journal, 51(2), 104-119.
Hasan, S., Mesa-Arango, R., & Ukkusuri, S. (2013). A random-parameter hazard-based model tounderstand household evacuation timing behavior. Transportation research part C: emerging
technologies, 27, 108-116.
Hasan, S., Ukkusuri, S., Gladwin, H., & Murray-Tuite, P. (2010). Behavioral model to understandhousehold-level hurricane evacuation decision making. Journal of Transportation
Engineering, 137(5), 341-348.
Horney, J., MacDonald, P., Van Willigen, M., Berke, P., & Kaufman, J. (2010). Factors associatedwith risk of evacuation failure from hurricane Isabel in North Carolina, 2003. International
Journal of Mass Emergencies and Disasters, 28(1), 33-58.
Huang, S.-K., Lindell, M. K., Prater, C. S., Wu, H.-C., & Siebeneck, L. K. (2012). Household
evacuation decision making in response to Hurricane Ike. Natural Hazards Review, 13(4),283-296.
Jacob, B., Mawson, A. R., Marinelle, P., & Guignard, J. C. (2008). Disaster mythology and fact:
Hurricane Katrina and social attachment. Public Health Reports, 123(5), 555-566.
Jaeger, C. C., Webler, T., Rosa, E. A., & Renn, O. (2013). Risk, uncertainty and rational action:
Routledge.
Kasperson, R. E., Renn, O., Slovic, P., Brown, H. S., Emel, J., Goble, R., . . . Ratick, S. (1988). The
social amplification of risk: A conceptual framework. Risk analysis, 8(2), 177-187.
Kusenbach, M., Simms, J. L., & Tobin, G. A. (2010). Disaster vulnerability and evacuation
readiness: coastal mobile home residents in Florida. Natural Hazards, 52(1), 79-95.
Lindell, M. K., Lu, J.-C., & Prater, C. S. (2005). Household decision making and evacuation in
response to Hurricane Lili. Natural Hazards Review, 6(4), 171-179.
Litt, J. (2008). Getting out or staying put: an African American women's network in evacuation from
Katrina. NWSA journal, 20(3), 32-48.
130Peguero, A. A. (2006). Latino disaster vulnerability: the dissemination of hurricane mitigation
information among Florida’s homeowners. Hispanic Journal of Behavioral Sciences, 28(1),5-22.
Sadri, A. M., Ukkusuri, S. V., & Murray-Tuite, P. (2013). A random parameter ordered probit modelto understand the mobilization time during hurricane evacuation. Transportation researchpart C: emerging technologies, 32, 21-30.
Smith, S. K., & McCarty, C. (2009). Fleeing the storm (s): An examination of evacuation behavior
during Florida’s 2004 hurricane season. Demography, 46(1), 127-145.
Spence, P. R., Lachlan, K. A., & Griffin, D. R. (2007). Crisis communication, race, and natural
disasters. Journal of Black Studies, 37(4), 539-554.
Stein, R. M., Dueñas‐Osorio, L., & Subramanian, D. (2010). Who evacuates when hurricanes
approach? The role of risk, information, and location. Social Science Quarterly, 91(3), 816834.
Taylor, J., Gillette, S., Hodgson, R., Downing, J., Burns, M., Chavez, D., & Hogan, J. (2007).
Informing the network: Improving communication with interface communities during
wildland fire. Human Ecology Review, 14(2), 198-211.
Turner, B. A. (1979). The social aetiology of disasters. Disasters, 3(1), 53-59.
Vu, L., VanLandingham, M. J., Do, M., & Bankston, C. L. (2009). Evacuation and return ofVietnamese New Orleanians affected by Hurricane Katrina. Organization & environment,
22(4), 422-436.
West, D. M., & Orr, M. (2007). Race, gender, and communications in natural disasters. Policy
Studies Journal, 35(4), 569-586.
Xu, K., Davidson, R. A., Nozick, L. K., Wachtendorf, T., & DeYoung, S. E. (2016). Hurricane
evacuation demand models with a focus on use for prediction in future events. Transportation
Research Part A: Policy and Practice, 87, 90-101.
Zottarelli, L. K. (2010). Broken Bond: An Exploration of Human Factors Associated with
Companion Animal Loss During Hurricane Katrina1. Sociological Forum, 25(1), 110-122.

131

PART-VII

EXPERIMENTAL INVESTIGATION OF
EMBANKMENT ON SOFT SOIL UNDER CYCLICLOADING: EFFECT OF INPUT ACCELERATIONBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Ripon Hore
Sudipta Chakraborti
AM Shuvon
Mehedi Ahmed Ansary132

INTRODUCTION
The soil-foundation composed of clayey soil has become the focus of earthquake engineering in
more cases. However, it is rather scarce to study the characteristic of pore water pressure in suchsoil-foundations with cohesive or clayey soil under earthquake action due to its clay content isthought of relatively high. Although such soil-foundations of higher clay content are not easily
susceptible to liquidity, a rise of excess pore water pressure triggered by earthquakes and a drop ofeffective stress may lead to the soil softening. Also, soil-foundation softening may cause damage tothe embankment. Today, there is no absolute conclusion on whether the softening effect of cohesiveor clayey soils under earthquake excitation should be considered and under what shake condition thedamage induced by excess pore water pressure may not be considered in analysis. Based on shaking
table tests, this paper attempts to investigate the changing characteristics of the amplification,
displacement and pore water pressure (due to input acceleration) in soft soil-foundations underharmonic wave. The soil-foundation in the test has been built with soft soils in Dhaka clay. The
physical mechanism responsible for variation behavior of the pore water pressure in the repeatedprocess has been studied. The results of experimental investigation have been expected to provide
some research basis for softening problems of clayey embankment under earthquake actions.
Methodology
Previous Research
Developments of model testing in earthquake geotechnical engineering, two aspects of model testing
have been given importance, namely rigid and laminar box. Design, development, calibration andperformance of this equipment are very important (Prasad S. K. et al., 2004). Latha M. G. et al.
(2006) has presented Shaking table studies which have been carried out on wrap-faced reinforced
soil retaining walls to gain insight into their behaviour under dynamic loads. Soft soil with the larger
void ratio, the higher water content and higher the saturation degree, the peak of excess pore waterpressure during the repeated loading process will not be significantly higher than that of the previous
cycle (Zhang, Z. et al., 2009). Moss, R. et al (2010) has proposed scale soil-structure models to
mimic the coupled seismic response of underground structures and surrounding/supporting soil
(termed soil-structural-interaction or SSI). Currently the seismic design of subways and other criticalunderground infrastructure rely on little to no empirical data for calibrating numerical simulations.
Srilatha et al. (2014) has also been conducted series of laboratory shaking table tests for observing
the performance of without reinforced and reinforced soil slopes through. Frequency of baseacceleration has been varied from 1 Hz to 7 Hz in different tests. Acceleration of base shaking has
been kept as 0.3 g in all the tests. A recent laboratory test has been conducted by Yazdandoust (2017)
to assess the behavior and performance of steel-strip reinforced-soil retaining walls during seismic
133

loading, a series of 1-g shaking table tests have been conducted on 0.9 m high reinforced-soil wallmodels with different strip lengths.
Current Investigation
Based on the aforementioned necessities the present research targets the seismic design of subway/
road way or railway embankment. A scale model testing platform has been developed for single
degree of freedom shaking table tests that mimics the dynamic free-field conditions of Dhaka clayeysoil where a sand retaining wall has been erected on clayey soil has been subjected to seismic
loading. A total of 90 shaking table test have been carried out on this model embankment. The effectof frequency, amplitude, surcharge, pore water pressure and displacement along the different
elevations (effect of input acceleration) has been observed.
Equipment and Materials
Shaking table: A computer-controlled servo-hydraulic single degree of freedom shaking tablefacility has been used to simulate the horizontal shaking action, associated with seismic and othervibration conditions. The testing platform is a square having 2×2 m2 dimension and approximate
payload capacity of 1000 kg, which has been made from steel plates. Shaking has been provided bya digitally controlled servo-hydraulic actuator with ±200 mm stroke and 30 kN force rating. The
shaking table can be operated over an acceleration range of 0.05g to 2g and frequency range 0.05 to
50 Hz with a maximum amplitude of ±200 mm. Maximum velocities are 0.3 m/s.
Laminar box: In this study, embankment with soft clay soil models has been constructed in alaminar box to reduce boundary effects as far as practicable. The laminar shear box developed at
BUET has composed of 24 hollow aluminum layers of frames. Each layer consists of an inner framewith inside dimensions of 915 mm × 1220 mm × 1220 mm. Laminar box mounted on the shake
table has been presented in Fig. 1(a).
Backfill material & Reinforcement: Dry sand (Specific gravity=2.64) has been used as the backfill
material. The sand has been classified as poorly graded sand (SP) according to the Unified Soil
Classification System. Maximum dry density of the sand is 18 kN/m3 and minimum dry density
observed in the loosest state is 15 kN/m3. A non woven polypropylene multifilament geotextile has
been used for reinforcing the sand in the tests.

Model Construction and testing procedure
Re constitute clay soil sample: In this research work Dhaka soft clay soil has been used. The liquid
limit of this soil has been found 40%. The water content of this soil sample has been found 23%. For
preparing the soil sample used 50% of water content (1.25 times of liquid limit). From the direct
shear test cohesion and friction have been found 14.8 KN/m2 and 10.03 respectively. After loading134

had been done, the water content of soil sample was 15% and unconfined compression strength (qu)was 20 kPa.

The constitute clay layer thickness of the soil sample is 18 inch. Preparation of reconstitute sample has been shown as Fig. 1(b). Two pore water pressure sensor: one is base level ofsample and another is 18 inch above the base level have been used in this soil sample. Two
acceleration sensors have been placed in the soil sample.

[Fig. 1]. Detail setting of Shake table test. (a) Laminar box mounted on shaking table (b) Placementof reconstitute soil sample
Testing Process: The model embankment has been constructed in a laminar box to a size of 915 mmx1220 mm in plan area and 1220 mm deep. Each geotextile layer has been wrapped at the facing fora length of 150 mm. To achieve uniform density, sand was placed in the laminar box using thepluviation (raining) technique (Hossain and Ansary, 2018). After the completion of all lifts up to fullheight of the wall (406 mm), surcharge in the form of three type concrete slabs (19/34/49 kg) havebeen applied to anchor the top wrapped geotextile. The facing formwork was removed carefully insequential lifts from bottom to top after the backfill layers (2 inch) and surcharge have been
completed. Fig. 2 shows the finished embankment for four-layer with 2 ft clay layer. A typical model
configuration showing the instrumentation for the test wall with four layers of reinforcement has
been shown in Fig. 3. Two pressure sensors; P1 and P2 have been placed inside the wall, in contact
with the measure horizontal displacement, three displacement sensors (LVDTs), L1, L2 and L3, havebeen positioned at elevations 32, 42 and 56 inch, respectively, along the facing for the tests with
four-layer configurations.

135

Results and Discussions
Results obtained from sixteen different shaking table tests from ninety combinations on embankment
with soft clay models have been discussed in this paper. The parameters varied in model tests arebase acceleration, frequency and surcharge pressure on the crest. The base acceleration has been kept
as 0.1g, 0.2g, 0.3g, 0.4 g and 0.5g in different tests. The frequency has been varied from 1 Hz to 15
Hz. The surcharge pressure on the embankment has been kept as 19, 34 and 49 kg. Soft clay layer
has been used (24 in) which unconfined compression strength is 20 kPa. Model has been constructedusing sand on the clay layer in equal lifts (Sv) of 4 and total wall height (H) is 16 in. The length (L)of the geotextile reinforcement at the interface of the sand layers has been kept the same in all tests
as 15 in. Model wall was subjected to 20 cycles of sinusoidal shaking. The average unit weight andrelative density achieved were within the ranges 18 kN/m3 and 60% respectively for the same heightof fall.

[Fig. 2].Setting of Setting of displacement [Fig. 3]. Schematic diagram of typical test
sensor.

wall configuration and instrumentation

Acceleration response: Fig. 4(a) and Fig. 4(b) compare the acceleration amplification profile along
the height of the wall for different configurations of wall and base motion after each test of 20 cyclesof sinusoidal motion. Here the elevation (z) has been represented in non-dimensional form after
normalizing by the full wall height (H). Maximum acceleration amplification has been observed at
the top of the wall in all the tests. This observation is in concurrence with the results of physical tests
reported by Telekes et al. (1994), El-Emam and Bathurst (2005) and Krishna et al (2007). Fig. 4(a)shows the acceleration amplifications along the height of the wall for different base accelerations of
0.1, 0.2, 0.3, 0.4 and 0.5g from ST2 (0.1g, 3 Hz and 19 kg) , ST8 (0.2g, 3Hz and 19 kg), ST14 (0.3g,
3Hz and 19 kg), ST20 (0.4g, 3 Hz and 19 kg) and ST26 (0.5g, 3Hz and 19 kg) model tests,
136

respectively, which have been conducted at 3 Hz frequency, 19 Kg surcharge. However, within therange of tests conducted, acceleration amplifications at the top of the wall for 0.1,0.2, 0.3, 0.4 and0.5g base accelerations are 1.06 to 1.22. Fig.4(b) shows the two sensor in clay soil sample layer fordifferent base accelerations of 0.1, 0.2, 0.3, 0.4 and 0.5g from ST2, ST8, ST14, ST20 and ST26
model tests, respectively, which were conducted at 3 Hz frequency, 19 Kg surcharge.
g_
1

0.75
0.7

Normalised Elevation, z/H

Normalised Elevation, z/H

0.65

0.8

0.6

0.4

ST2 0.1g 3Hz 19 kg 60%
ST8 0.2g 3Hz 19 kg 60%
ST14 0.3g 3Hz 19 kg 60%
ST20 0.4g 3Hz 19 kg 60%
ST26 0.5g 3Hz 19 kg 60%

0.2

0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.1g
0.2g
0.3g
0.4g
0.5g

0.15
0.1
0.05
0

0
0

0.5

1

1.5

2

2.5

3

1

1.025

b)

1.05

1.075

1.1

1.125

1.15

1.175

1.2

1.225

1.25

Acceleration Amplificationa)[Fig. 4]: a) Effect of base acceleration on acceleration amplification b) Effect of base acceleration on
Acceleration Amplification

acceleration amplification (Clay layer)
Face displacement response: Horizontal face displacements along the height of the wall have beenmonitored using three LVDTs positioned as shown in Fig. 3. Fig. 5 presents the displaced face
profiles from various tests after 20 cycles of sinusoidal motion. Here elevation (z) and horizontal
displacements (h) have been presented in non-dimensional form after normalizing them by the totalheight of the wall (H). Fig. 5 shows normalized displacement profiles of the facing after 20 cycles
for different base accelerations of 0.1, 0.2, 0.3, 0.4 and 0.5g from tests ST1 (0.1g, 1Hz and 19 kg),
ST7 (0.2g, 1Hz and 19 kg), ST13 (0.3g, 1Hz and 19 kg), ST19 (0.4g, 1Hz and 19 kg) and ST25
(0.5g, 1Hz and 19 kg) respectively. The normalized displacements are relatively high at the higher
base accelerations. A maximum horizontal displacement of 2.02% of the total wall height, H, for
0.5g have been observed compared with 0.01% for 0.1g base acceleration. The displacements
obtained in the present study are in close agreement with the results presented by Sakaguchi et al.
(1992) and Krishna et al (2007) corresponding to the accelerations and frequency levels used in thepresent study.
Pore Pressure response: Fig. 6 presents typical pore pressure variations obtained from the model
tests. Fig. 6 shows variations of the pore pressure from model tests ST66 (0.1g, 15Hz and 49 kg),
ST72 (0.2g, 15Hz and 49 kg), ST78 (0.3g, 15Hz and 49 kg), ST84 (0.4g, 15Hz and 49 kg) and ST90
(0.5g, 15Hz and 49 kg) with of base acceleration 0.1, 0.2, 0.3, 0.4 and 0.5g respectively for 15Hz
137

frequencies and surcharge load 49 kg. It is observed that pore water pressure has been increased withelevation. The highest pore water pressure is 0.38 kPa at base acceleration 0.5g. The highest porewater pressure for model tests ST66, ST72, ST78, ST84 and ST90 is 0.06, 0.09, 0.17, 0.26 and 0.38
kPa respectively.
Conclusions
The seismic response of geotextile-reinforced embankment on soft clayey soil has been investigatedby conducting shaking table tests on model test. A model test with variations in acceleration andfrequency of base motion, surcharge pressure on the crest and pore water pressure (effect of input
acceleration) have been described along with the model preparation, testing methodology and results.
It has been observed that the seismic response of the embankment has significantly been affected by
the variations of the base motion, surcharge pressure on the crest and pore water pressure. In fact,
accelerations have been amplified at higher elevations with low surcharge pressures. The
acceleration amplification response with change in base shaking frequency clearly indicates the roleof the fundamental frequency on the response of the system. In some case the face deformations arehigh with high base accelerations. Pore water pressure is high at higher elevations with low
surcharge pressures and high base acceleration.

0.9

0.8

0.8

0.1g
0.2g
0.3g
0.4g
0.5g

0.7

0.75

0.6

0.7

Elevation,z/H

Normalised Elevation, z/H

0.85

0.65
0.6
0.55
0.5

0.4

0.3
15 HZ 49 kg
0.1g
0.2g
0.3g
0.4g
0.5g

0.2

0.45
0.4
0.35
1.86

0.5

0.1
1.88

1.9

1.92

1.94

1.96

1.98

Normalised face Displacement, h/H (%)

2

2.02

0
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Pore Water Pressure(Kpa)

[

Fig.

5]:

EffectofAcceleration

on [Fig. 6]: Variations of pore water pressure
with respect to Elevation (Effect of basedisplacement profile

acceleration)

Acknowledgments
This research has been supported by Geotechnical and concrete Lab, BUET and this support is
gratefully acknowledged.

138

REFERENCES
1. El-Emam, M. & Bathurst, R. J. (2005). Facing contribution to seismic response of reduced-scale
reinforced soil walls. Geosynthetics International, 12, No. 5, 215–238.
2. Krishna, A. M. &Latha, G.M. (2007) Seismic response of wrap-faced reinforced soil retaining
wall models using shaking table tests Geosynthetics International, 2007, 14, No. 6
3. Hossain, M. Z., &Ansary, M. A. (2018). Development of a portable traveling pluviator device
and its performance to prepare uniform sand specimens. Innovative Infrastructure Solutions, 3(1),53.
4. Moss R. E., Crosariol, V. &Kuo, S (2010) Shake Table Testing to Quantify Seismic SoilStructure-Interaction of Underground Structures, Fifth International Conference on Recent
Advances in Geotechnical Earthquake Engineering and Soil Dynamics, May 24th to 29th 2010.
5. Prasad, S. K., Towhata, I., Chandradhara, G. P. &Nanjundaswamy, P. (2004) Shaking table tests
inearthquake

geotechnicalEngineering,

SPECIALSECTION:GEOTECHNICSAND

EARTHQUAKE HAZARDS, CURRENT SCIENCE, VOL. 87, NO. 10, 25 NOVEMBER 2004.
6. Sakaguchi, M., Muramatsu, M. &Nagura, K. (1992). A discussion on reinforced embankment
structures having high earthquake resistance. Proceedings of the International Symposium on
Earth Reinforcement Practice, IS-Kyushu ’92, Fukuoka, Japan, Vol. 1, pp. 287–292.
7. Srilatha, N., Madhavi Latha, G. and Puttappa, C.G. (2014). Shaking Table Studies on Geotextile
Reinforced Soil Slopes, Golden Jubilee Conference of the IGS Bangalore Chapter, GeoInnovations, 30-31 October 2014, pp.1-8
8. Telekes, G., Sugimoto, M. &Agawa, S. (1994). Shaking table tests on reinforced embankment
models. Proceedings of the 13th International Conference on Soil Mechanics and Foundation
Engineering, New Delhi, India, Vol. 2, pp. 649–654.
9. Yazdandoust, M. (2017). Investigation on the seismic performance of steel-strip reinforced-soil
retaining walls using shaking table test. Soil Dynamics and Earthquake Engineering, 97, Elsevier,pp.216-232.
10. Zhang, Z., Cho,C., Pan, Q. & Lu, X. (2009) Experimental Investigation on Excess Pore Water
Pressure in Soft Soil-Foundations under Minor Shocks.World Academy of Science, Engineeringand Technology International Journal of Civil and Environmental Engineering Vol:3, No:2,
2009.

139

PART-VIII

DEVELOPMENT OF ZONATION FORBANGLADESH BASED ON SOFT CLAY USING GISBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Ripon Hore
MR Arefin
Mehedi Ahmed Ansary140

INTRODUCTION
Soft clay soil layer identification is very significant for selecting the different soil improvement
techniques. So presenting the subsoil characteristics in one platform is major challenge. Basically
soft clay soil is a finely-grained natural rock or soil material that combines one or more clay minerals
with possible traces of quartz (SiO2), metal oxides (Al2O3, MgO etc.) and organic matter. Besidethese soft clay deposits are mostly composed of phyllosilicate minerals containing variable amountsof water trapped in the mineral structure. Construction on soft clay soil may cause failure thestructure. So before construction, implementation the soil improvement techniques has beennecessary.
Methodology
Previous Research
Kamal and Midorikawa (2004) delineated the geomorphology of Dhaka city area, differentiating the
ground of the city into seventeen geomorphic units using aerial photographs. These geomorphic units
represent the soil conditions. They also classified the fill-sites into four classes based on the
thickness of fills. Ansary, M.A. and Rashid,M.A.(2000) has conducted to analysis the mitigation of
the disaster due to future shocks is an essential consideration for effective land use and proper townplanning. This is the first study initiated in Bangladesh as part of the micro-zonation investigations.
Alam, J. and Islam, S. (2009) has described the understanding the geological setting of Bangladesh isimportant for foundation design as well as to assess the Earthquake hazards. A brief description of
the geology, stratification and subsoil investigation evidences of soil formations of Bangladesh has
been presented in this paper. Hossain et. at. (2003) has evaluated sub-soil characteristics andliquefaction potential of Mirpur DOHS area. To characterize soil deposit eight bore holes were
drilled at the project site. Moisture content, specific gravity, Atterberg limits, grain size distribution,
unconfined compressive strength, density and shear strength parameters of the collected samples
have been determined in the laboratory. Rahman (2004) updated the seismic micro-zonation maps
for liquefaction as well as site amplification due to earthquake.
Current Investigation
All these research paper described the sub soil characteristic at specific location of Bangladesh.
Basically seismic or liquefaction micro zonation map have been produced. In these research papers
an attempt has been conducted to produce proposed zonation map base on soft clay soil using GIS
Software 2010 which is effectively useful to selecting proper soil improvement techniques.
Geology of Bangladesh:
The Geology of Bangladesh has been affected by the country's location, as Bangladesh is mainly ariverine country. It is the eastern two-thirds of the Ganges and Brahmaputra river delta plain141stretching to the north from the Bay of Bengal. There are two small areas of slightly higher land in
the north-centre and north-west composed of old alluvium called the Madhupur Tract and the Barind
Tract, and steep, folded, hill ranges of older (Tertiary) rocks along the eastern border. The down
warping of the basement rocks under central and southern Bangladesh result from the pressure of
sediments that have been accumulating since the Cretaceous period, mostly a large quantity ofcarbonate. In the Late Eocene epoch the conditions in the Bay of Bengal changed and these deposits
ceased. In the late Cretaceous, the Indian Plate collided with the Eurasian Plate, after the Indian and
African Plate split to form the Indian Ocean during the Triassic. The tectonic collision in the
Cretaceous separated the Gulf of Assam and the Gulf of Burma, while narrow channels from theocean fed water into the two gulfs. As ridges formed in the Tethys Ocean on the other side of the
Shillong and Dinajpur Shields, small island arcs formed locally. The Tura sandstone, Sylhet
limestone and Kopili shale were all deposited during an intermediate shall marine depositional
environment in between these tectonic changes. A renewed period of collision and tectonic activity
began with the Himalayan orogeny in the Eocene. As tectonic activity slowed somewhat, a riverdelta environment deposited of the other significant Cenozoic sedimentary units. Renewed tectonism
in the Pliocene formed the Dinajpur Shield into a graben, relative to the horst of the Shillong Plateau
and Mikir Hills. The Pliocene orogeny led into the Pleistocene ice age, resulting in regional
extinction of many large mammals. A final period of uplift raised the red clay table lad 30 to 100
feet, along with St. Martin's Island in the Pleistocene. Surface rocks and stratigraphy in Bangladesh
have been formed during the Cenozoic and rock units date to all epochs of the Cenozoic except for
the Oligocene, which has been poorly preserved.
Sub Soil Condition
The sub-surface investigation work includes execution of 1000 borings, performance of the required
field tests. Boreholes have been drilled vertically using wash boring technique. The density andstiffness characteristics of the subsoil layers in the boreholes have been measured by performingStandard Penetration Test (SPT). The bore log along the alignment has been shown by Fig. 1. The
sub soil investigation has been performed by Soil and Foundation Consultants. Thickness of soft clay
layers varies from 1 to 20 m. The most common index properties for estimating liquefaction strength
is the N-value obtained from the standard penetration test. The SPT consists of driving a thickwalled sampler into the granular soil deposit. The measured SPT N-value (blows per foot) is definedas the penetration resistance of the soil, which equals the sum of the number of bows required todrive the SPT sampler over the depth interval of 15 to 45 cm. Driving the standard split-barrel
sampler of dimensions. a distance of 460 mm into the soil at the bottom of the boring and counting

142the number of blows to drive the sampler the last 305 mm to obtain the N number. Using a 63.5 kg
driving mass (or hammer) falling "free" from a height of 760 mm.

Dhaka Soil

Barisal soil

Comilla soil

Rangpur Soil

Mymenshing soil

Rajshahi Soil

Khulna soil

Sylhet soil

143

Fig. 1.Sub Soil information
Results and Discussions
Geographical information System (GIS)
GIS application in Geotechnical purpose and the spatial distribution is new dimension for data
analysis for proper structural suitability study. First step for the GIS application is data processing
for GIS input. Data processing belonging the modification of the location of the lithology andanalysis the data. Classify the soft layer at particular interval depending on SPT (SPT value 1 to 5).
Two map named SPT distribution and lithological thickness has been shown in Fig 2. After
processing the data locations has been input into the GIS interface. Sub soil information has beenshown in Table 1. Soft clay soil thickness layer, clay layer thickness, N value rang and soil type havebeen input in the table. Latitude, Longitude and location have also been shown in the table. This
table has been imported to GIS interface.
Table 1: Sample Information of GIS input
Thickness Thickness

N

N

N

value

value

value

Soil

LAT.

LONG. Layer (m) (m)Range (Max.) (Min.) type

Location

92.00

24.83

0-18

18.00

1-5

5.00

1.00

Clay

Sylhet

92.15

24.89

0-7

7.00

1-4

4.00

1.00

Clay

Sylhet

91.89

24.91

0-4

4.00

1-5

5.00

1.00

Clay

Sylhet

91.98

24.91

2-5

3.00

1-5

5.00

1.00

Clay

Sylhet

92.10

25.16

4-6

2.00

3-5

5.00

3.00

Clay

Sylhet

91.80

24.98

0-6

6.00

3-5

5.00

3.00

Clay

Sylhet

91.93

24.95

2-7

5.00

1-5

5.00

1.00

Clay

Sylhet

91.75

24.85

0-3

3.00

1

1.00

1.00

Clay

Sylhet

91.72

24.88

0-4

4.00

1-4

4.00

1.00

Clay

Sylhet

92.15

24.88

0-4

4.00

1-4

4.00

1.00

Clay

Sylhet

91.85

24.91

2-4

2.00

4

4.00

4.00

Clay

Sylhet144

Fig. 2: SPT Location Map
In this research total 1000 boreholes have been conducted for SPT test around Bangladesh. Fromthese boreholes 470 boreholes have been selected for soft clay soil layer formation. Three GIS maps
have been created by Arc GIS 2010. One for soil parameters information map and second is zonation
map for SPT N value from 1 to 5. Another is zonation map for the soft clay layer thickness. Soilparameters information map has been shown in Fig. 3. From the map different sub soil parameters
have been found by clicking the specific location of Bangladesh. Total 470 bore hole (soft clay soil
layer) has been presented in the GIS interface map. On the other hand zonation map for SPT N value
has been shown in Fig. 3. Soft clay layer thickness has been shown in Fig. 4.

Fig. 3: Zonation map based on SPT value

145

CONCLUSIONS
Availability of soft clay is high in southern part of the country and in the riverside locations. The softcay soil thickness is varying from 1 to 20 meters. This paper has proposed three GIS based map
which will be useful for selecting soft ground soil improvement techniques in the different regions of
the country.

REFERENCES
1. 1. Alam, J.and Islam,S. (2009) “Geological Aspects of Soil Formation of Bangladesh”
Bangladesh Geotechnical Conference 2009 (BGC-2009), Dhaka, Bangladesh.
2. 2. Ansary, M.A. and Rashid,M.A.(2000), Generation of liquefaction potential map for Dhaka,Bangladesh, proceeding of 8th ASCE speciality conference on probabilistic Mechanics andStructural and Reliability, paper no.PMC 200-061.
3. 3. Hossain, S.M.Z., Alam, M.S. and Islam, M.S., (2003). Geotechnical characterization andliquefaction potential of sub-soil of Mirpur DOHS, Undergraduate thesis, Department of Civil
Engineering, Military Institute of Science and Technology, Dhaka,Bangladesh.
4. 4. Jamiolkowski, M.(2003), “Soil Parameters Relevant to Bored PileDesign from Laboratory and
In-Situ Tests,” Deep Foundations on Bored and Auger Piles, Millpress, Rotterdam,The
Netherlands, pp. 83–100.
5. 5. Kamal and Midorikawa (2004) ,GIS-based geomorphological mapping using remote sensing
data and supplementary geoinformation. A case study of the Dhaka city area, Bangladesh,
December 2004International Journal of Applied Earth Observation and Geoinformation
6(2):111–125.
6. 6. Rashid, A.(2000), “Seismic Microzonation of Dhaka City based on site amplification andliquefaction”, M. Engg. Thesis, Department of Civil Engineering, BUET,Dhaka ,Bangladesh.
7. Rahman, M.G.(2004),“Seismic Damage Scenario for Dhaka City” , M.Sc. Engg. Thesis,
Department of Civil Engineering, BUET, Dhaka, Bangladesh.

146

PART-IX

PREDICTING THE GROUND MOVEMENT ABOVE
A TUNNEL IN CHITTAGONG COASTAL AREA OFBANGLADESH UNDER SEISMIC LOADINGBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: MF Haque
Mehedi Ahmed Ansary

147

1

INTRODUCTION
Developing of underground space, tunneling is the most important issue in civil engineering.

Tunnel is being constructed to densely populated areas as well as for long transportation
infrastructures. Construction of tunnel induces ground movements which may affect existing
structures. Ground movement is related with damage rate of tunnel. Generally, damage rate oftunnels are lower than surface structures under seismic loading. To prevent damages of tunnels,
accurate assessment is necessary to predict tunneling-induced ground movements. Groundmovement above a tunnel in sand has been reported by Mair and Taylor (1997), Vorster et al. (2005),
Marshall et al. (2012) etc. Tunneling effects on pipelines and buildings have been investigated insand by several authors (Klar et al., 2007, 2015; Farrell et al., 2014; Giardina et al., 2015; Franza &Marshall, 2017; Ritter et al., 2017). Relative depth of tunnel (An/Dn) and diameter of tunnel (Dn) aremain factor to handle ground movement above a tunnel in sand with proper construction techniqueand relative density. The goal of this paper is to predict ground movement above a tunnel in sand in
Patenga, Chittagong, Bangladesh. In this paper, circular type tunnel has been considered whichmoves along a straight line in sand layer. Variable parameters in these paper are relative depth oftunnel, diameter of tunnel and tunnel length with constant relative density and construction method(EPB – TBM). Numerically, ground settlements are observed with the variation of variable
parameters under static as well as seismic loading. Linear analysis has been performed to predictground movement above a tunnel. Finally, appropriate placement of tunnel in sand layer has been
performed through numerical analysis.

Figure 1: Under construction of tunnel at Potenga, Chittagong, Bangladesh
148

2

LOCAL GEOLOGY AND GEOTECHNICAL PARAMETERS
According to GSB (1990), the local geology consists of mainly beach and dune sand. Thissand contains light to whitish-grey sand which is medium to fine, well sorted, and subrounded;this also contains concretions, shell fragments, heavy minerals, and rare clasts which include
small mud-flat deposits. Unconformably overlies Late Tertiary formations. Several bore logs
have been carried out at the project site. A typical bore log has been presented in Figure 2. The
soil profile mainly consists of softy silty clay up to a depth of 3 to 5 m, which is underlain byfine sand.
B LOWS ONS P OON P ER
6"

DES C R IP TION OF
M ATER IALSS TANDAR D P ENETR ATION R ES IS TANC E(S P T)
B LOWSP ER 0.30m / 1ft

6" 6" 6"
10

Light grey very soft silty
3.0
CLAY high plastic.

U-1
D-2

3.0

D-3

4.5

Grey very loose silty FINE
SAND trace mica.

D-5
D-6

10.5

Grey loose to medium dense
silty FINE SAND trace mica.

50

1 0 1 12 3 5 83 4 7 11
4 4 6 10
3 3 5 83 4 7 11D-8

4 6 7 13

D-9
D-10

40

1 0 1 1
1.5

D-4

D-7

30

1 1 1 2

100 mm (4") 

D-1

20

15.0

5 8 12 20

Figure 2: Typical borehole log at Potenga, Chittagong, Bangladesh
3

MODIFIED EMPIRICAL AND ANALYTICAL FORMULAS
Underground space technology such as tunneling has become very popular and demanding. In

urban areas, people wants alternative mode of transportation for excellent facilities. Engineer musttake note and gain high knowledge about tunneling technology. Protection needs of surface
structures as well as underground facilities from ground movements due to construction of tunnelingas well as seismic shaking after construction exist.
3.1 Modified Empirical Formula of Vertical Settlement
Vertical ground settlement is the major important issue during tunneling as well as seismic
shaking and also long term behavior of tunnel. For practical purpose, important issue is the
maximum settlement due to tunneling. The authors have modified Mair’s (1993) maximum groundsettlement formula with the consideration of 0.5 percent uniform volume loss (Mair and Taylor,1997) for sand and 35 percent of K factor (Mair and Taylor, 1997) as shown below.

149

,

0.0045.

1

Where, D, H, z, V(z),max. are represented by inner diameter of tunnel (m), vertical distance (m) fromfree surface to tunnel center line, any vertical distance (m) from free surface and maximum verticalsettlement (m). For surface settlement, z is equal to zero.
3.2 Modified Empirical Formula of Lateral Settlement
Lateral ground settlement affects surface structures and underground facilities similar to verticalsettlement of ground. Maximum lateral settlement of ground has great role in professional field.
O’Reilly and New (1981) gives an empirical formula to predict lateral surface settlement. The
authors have modified this formula with the consideration of Atkinson and Potts (1977) equationfrom the centrifuge tests for tunneling in dry sands which is shown below:
,

.

,

.

2

4

Maximum lateral ground settlement (m) and distance (m) from free surface to tunnel crown are
expressed as U(x),max. and A in equation (2).
3.3 Modified Empirical Formula of Longitudinal Settlement
Most of tunneling analysis (Franza et al., 2018, Pescara et al., 2011, Franza and Marshall 2015)
has been carried out based on plain strain consideration which has not included longitudinalsettlement profile of tunnel. A few researches have been expressed in longitudinal settlement above atunnel based on field observations. Attewell and Woodman (1982) gives an empirical relation topredict longitudinal ground settlement above a tunnel. Authors have modified this relation as shown
below:
,

,

.

.

3

Where U(y),max. is expressed as maximum longitudinal ground settlement (m) and unit length consider
along the longitudinal direction of tunnel due to plain strain consideration.
3.4 Modified Empirical Formula of Lateral Settlement Under Seismic Loading (USL)
A lot of seismic analysis has been performed for surface structures. A few data are available toexpress damage of tunnels after an earthquake before 1970’s. Pescara, (2011) gives an empirical
relation to predict lateral displacement above a tunnel under seismic shaking by using Eurocode 8.
The authors have modified Pescara, (2011) formulae as shown below:
,

4

.

150

Where Δ(x),max., a1, g, S, C, k, Cd, f and Cs are represented as maximum lateral settlement (mm),seismic coefficient such as 0.15 (Bangladesh National Building Code (BNBC) 2006), gravitational
acceleration (9.81m/s2), soil factor (1.15) based on Eurocode 8 which equivalent to BNBC 2006,
factor of ground motion (0.9) (Power et.al 1996), ratio of peak ground velocity to peak groundacceleration (142) (Power et.al 1996), total depth (27.5m) of upper layer and tunnel layer, modified
factor (10) based on present author modified analytical formulae and apparent propagation velocityof S-wave: several authors [O’Rourke and Liu, 1999; Power et al., 1996; Paolucci and Pitilakis,
2007] have suggested values between 1 and 5 km/s.
3.5 Modified Analytical Formula of Vertical Settlement
Verruijt and Booker (1996) have showed a closed form general solutions for the calculation of thevertical ground settlement under uniform radial ground loss. Authors have modified these formula
for long term as well as seismic loading. Long term ground deformation has been taken as 1mm
based on observations by Rankin, (1988) and Loganathan et.al (1998). Maximum ground settlement
for long term and seismic loading has been expressed by equations (5) and (6) which have been
provided below, respectively.
0.005

.

0.001

0.01

1
2

0.002

.

Where,

0.01

0.005

= z – H,= z + H,

5

1

=

,

=

1

6

, R = D/2, m = 1/(1 - 2υ), k = υ(1 – υ). υ is the

Poisson’s ratio of soil. Long term ground deformation due to ovalization is equal to zero underseismic loading.
3.6 Modified Analytical Formula of Lateral Settlement
Verruijt and Booker (1996) have also showed a closed form general solutions for the calculationof the lateral ground settlement under uniform radial ground loss. Authors have also modified theseformula based on Hunt (2004) and Zhou (2014) considering lateral distance from tunnel axis for longterm and seismic loading which is shown below:

151

,

.

0.00125R

A

D
r
z

A

D
r

D R

1
r

2mzz
r

0.001R A D H z
m 1
r

mz A

0.0025 Am.

Dr

0.0000625R,

A

0.00125R

A

D
r

A

A

16kz

D

D
16r

A
4r

D 16kz48z

0.0025 A
m

r

D

D R

7

1
r

2mzz
r

8

Where, equation (7) represents as the maximum lateral settlement for long term loading and equation(8) represents as the maximum lateral settlement for seismic loading.
4

NUMERICAL ANALYSIS
Numerical analysis is necessary to evaluate behavior of underground structures under seismic

loading. Material properties have been determined from two types of tri-axial tests such as
consolidated drained and consolidated un-drained. Models have been created for analysis in software
PLAXIS 3D based on the Potenga, Chittagong site conditions. Two types of parameters are used inthis model such as fixed and variable. Fixed parameters are soil volume, material properties of soil
and tunnel lining, borehole location etc. Variable parameters are tunnel length, diameter and relative
depth. Mohr-Coulomb material model has been considered for soil and linear elastic model consider
for tunnel lining in long and short term behavior. Non-porous drainage type has been considered forlining segment. Bulk unit weight of silty clay, fine sand and lining segments are 16.4 kN/m3, 18.6
kN/m3 and 24 kN/m3 for long term behavior and for short term behavior these values are 17 kN/m3,
19.03 kN/m3. Saturated unit weight of short and long term behavior of sands, clays are 20 kN/m3, 20
kN/m3, 19 kN/m3 and 18.90 kN/m3. Modulus of elasticity of clay, sand and lining segments are 14.4
MPa, 26.1 MPa and 28 GPa for long term behavior and 22.5 MPa, 40.8 MPa for short term loading.
Cohesion of clay has 30 kPa for both long as well as short term loading. Internal friction angle ofsands and clays are 290, 230 for short term loading and 360, 230 for short term behavior. Dilatancy
angle of sand is 60 for long term behavior only. Poisson’s ratio of sands, precast segment and clays
are 0.25, 0.20 and 0.31 for both cases. Face pressure, grouting pressure and jacking force of tunnel

152are 90 kN/m2 with axial increment 14 kN/m2/m, 100 kN/m2 with axial increments of 20 kN/m2/mand 600 kN/m2. 0.5% uniform surface contraction has to be taken with axial increment of 0.05%.
In seismic analysis, free field boundary conditions have been taken along lateral direction
because of seismic loading has to be applied in this direction. Software automatically considers othervalues which are necessary to analyze this model. Most of the earthquakes have a frequency range
such as 8 to 12 Hz. Material damping has been considered as 5%. Length of Earth Pressure Balance
Tunnel Boring Machine (EPB-TBM) considered is 12m. Maximum number of steps and maximum
number of iteration are 250 and 60 in analysis for long and short term loading. Initial length of tunnel
is 25m and tunnel advancement of 2m along its own axis is considered. Three stage of advancementof tunnel have been considered for analysis. Variable diameters of tunnel are 10.5m, 10.8m and11.0m. Depths from free surface to top surface of tunnel are 4.5m, 5.5m, 6.5m, 7.5m, 8.5m.
Thickness of pre-cast segment considered is 0.5m. The finite element model is shown in Figure 3.

Figure 3: Finite element model based on Potenga soil conditions at Chittagong

153

Figure 4: Seismic data at Potenga, Chittagong.
When model geometry, material properties and other relevant data are fully defined then modelhas to be divided into finite elements in order to perform finite element calculations. This type ofcomposition of finite element method is called mesh. Fine mesh will be used to obtain accurate
numerical results. Half portion of the tunnel has been considered because of symmetry. 10-node
tetrahedrons soil element have been taken for analysis. Every phase consists of a particular loadingor construction stage. Analysis phases are initial, plastic and seismic. In plastic phase of calculation,
drained behavior has been taken and un-drained (A) option has been taken for seismic loading.
Seismic data has been input in the software based on a real seismic event in Chittagong as shown in
Figure 4. Plastic calculation is related with the elastic-plastic deformation analysis. In a normal
plastic calculation, the stiffness matrix is based on the original un-deformed geometry. Loading canbe defined in various sense during plastic calculation mode such as changing the load combination,
stress state, weight, strength or stiffness of elements, activated by the changing the load andgeometry configuration or pore pressure distribution by means of staged construction. For meshing,
the number of cores is set to 256 for this model. Software performs seismic analysis after thecompletion of modeling and static analysis. Seismic analysis is the complex type of dynamic analysis
which contains non-periodic loading history. The applied seismic load is the product of the input
value of the defined seismic load and corresponding seismic load multiplier. Activation of theseismic load or seismic prescribed displacement, free field boundary conditions can be defined for aseismic analysis. Meshing of the model is presented in Figure 5. Meshing effect on model has beenshown in Table 1.

154

Table 1: Meshing effect on model due to tunneling
Tunnel Length,y1 (m)
27

Ratio, A1/D1
0.4286

Maximum total vertical settlement, Vzt in mm
CoarseMedium

Fine

44.7

44.5

44.5

Figure 5: Un-deformed fine meshing of the research model
Seismic loadings are imposed for a short time but its impact upon the structures or soil is high. Highfrequency of seismic loading can easily pass through the small elements of these model. Finemeshing which yields best analysis is applied under seismic loading.
5

RESULTS AND DISCUSSIONS
Maximum surface settlement in vertical, lateral and longitudinal directions above a tunnel has

been discussed here. Variation of surface acceleration in lateral direction is also presented. Seismic
loading has been applied in the model for five seconds in lateral direction. Some terms used toexpress results are MEF-LTL (Modified Empirical Formulae Long Term Loading), MEF-SL(Modified Empirical Formulae Seismic Loading), MAF-LTL (Modified Analytical Formulae Long
Term Loading), MAF-SL (Modified Analytical Formulae Seismic Loading) etc. Following
parameters are varied: diameter – 10.5m (D1), 10.8m (D2), 11.0m (D3), length – 27m (y1), 29m (y2),31m (y3) and relative depth – 4.5m (A1), 5.5m (A2), 6.5m (A3), 7.5m (A4), 8.5m (A5).
1555.1 Vertical Surface Settlement Above a Tunnel
Vertical surface settlement consists of stability of structures which stands on the ground surface.
Vertical surface settlement depends on the variation of relative depth, tunnel length and tunnel
diameter. These settlements are calculated based on previously mentioned equations and are shownin Figure 6. Three diameters represent almost similar results during seismic and long term loading
based on modified formula. Results of modified analytical and empirical formulae are very close forlong term loading. Settlement gradually decreases with the increase in relative depth based on author
modified formulae. Minimum settlement is 30mm at A5/D1 location using MAF-SL formula.

Figure 6: Vertical surface settlement for long term and seismic loading based on modified formulae.
Figure 7 and Figure 8 present the vertical surface settlement for long term and seismic loading basedon numerical analysis using PLAXIS 3D. Uplift has been observed for each loading types,
longitudinal length and diameter. Three construction stages of tunnel coincide at a point A5 for longterm loading which represents minimum settlement, apparently 7 mm at A5/D1 location. Third
construction stage of tunnel fully represents uplift under seismic loading for three diameters.
Minimum settlement is nearly 2 mm at A5/D1 location under seismic loading.

Figure 7: Vertical surface settlement for long term loading based on finite element method.
156

Figure 8: Vertical surface settlement for seismic loading based on finite element method.

5.2 Lateral Surface Displacement Above a Tunnel
Lateral surface settlement consists of lateral stability of super structures under long term (Figure9) and under seismic (Figure 10) loading effect. Change of these settlements is made based on
variation of depth of tunnel crown, length of tunnel and diameter of tunnel. Modified and numericalmethods have been used to calculate these settlements. At A5 location, lateral settlement is nearly
9mm of diameter D1 for long term loading based on modified formulae in Figure 9.

Figure 9: Lateral surface settlement for long term and seismic loading based on modified formulae.
At A5 location, lateral settlement is nearly 3mm of diameter D1 for long term loading based on finite
element method in Figure 10. According to Figure 11, uplift is observed during seismic loading at A5
location which indicates the minimum value.

157

Figure 10: Lateral surface settlement for long term loading based on finite element method.

Figure 11: Lateral surface settlement for seismic loading based on finite element method.

5.3 Longitudinal Surface Displacement Above a TunnelLongitudinal ground movement along length of the tunnel can be seen in Figure 12. Thesesettlement profile also express that the effect of phased construction of tunnel. Minimum value of
settlement is 2.6mm due to extension at A5/D1 location based on author modified formulae as can beseen in Figure 12.

Figure 12: Longitudinal surface settlement for long term loading based on modified formulae.
158

Figures 13 and Figure 14 show negative value of settlement which indicates the contraction ofground due to tunneling and positive settlement express that the extension of tunnel along its length.
Settlement gradually decreases with the increase of depth of tunnel crown for modified andnumerical formulae. Minimum settlement is 3mm due to contraction at A5/D1 locations for long termloading based on numerical analysis. Longitudinal settlement is very low during seismic analysis
which is nearly 0.3mm at A5/D1 location. Figure 15 shows longitudinal surface settlement profile
based on finite element method PLAXIS 3D.

Figure 13: Longitudinal surface settlement for long term loading based on finite element method.

Figure 14: Longitudinal surface settlement for seismic loading based on finite element method.

(a) Upward settlement due to tunneling159

(b) Downward settlement due to tunneling
Figure 15: Longitudinal surface settlement profile based on finite element method.

5.4 Surface Accelerations Above a Tunnel Under Seismic Loading (USL)
Ground movement above a tunnel has been produced by surface acceleration. Lateral acceleration oftunnel is critical than other two directions. A lot of points are present on the surface. One point (-3,15.5, -0.1) has been used here to express surface acceleration. Surface acceleration gradually
decreases with the increase in depth of tunnel crown for diameter, D1 as shown in Figure 16. Line A5
shows the minimum acceleration for diameter, D1 at 0.25s. The acceleration value is nearly 0.24
m/s2. The value of acceleration for diameter, D2 and D3 is higher than diameter, D1. Acceration
values are -0.5 m/s2 for diameter D2 and nearly 0.8 m/s2 for diameter D3 at 0.25s.

160

Figure 16: Lateral surface acceleration during seismic loading based on numerical analysis.

6

CONCLUSIONS
Movement of tunnel in homogeneous sand layer has been studied for ground structures in

Chittagong coastal area. Upward surface settlement occur because of soft upper clay layer. Anotherreason of upward settlement is the shallow depth of tunnel placement. The results obtained from
author modified empirical and analytical formula apparently match well. Surface settlement is more
important than the total settlement for tunneling. Results of these research deals with maximum
value. Minimum values of maximum vertical surface settlements are 7 mm for long term loading and
2 mm for seismic loading. Minimum values of maximum lateral surface settlements are 3 mm forlong term as well as seismic loading and 9 mm for modified formulae. Similarly, minimum values ofmaximum longitudinal surface settlements are 3 mm due to contraction for long term loading and 0.3
mm for seismic loading. Settlement obtained from the author modified formula are greater or nearly
equal to the finite element methods. Minimum value of acceleration is 0.24 m/s2 under seismic
loading. In these research, value of seismic loading is lower than the long term loading because of
seismic source is located far away from the tunneling project. Two major conclusions are:161

(1) Author modified formula may be used for practical purpose for all types of undergroundtunnel which passes through the sand layers.
(2) Suitable depth of the tunnel for this analysis is 8.5 m and suitable diameter is 10.5 m.
Future studies may be conducted such as replacement of homogeneous layer with theheterogeneous layers, considering strong seismic loading, inclination of tunnel, two or more tunnel,
tunnel passes through the active seismic fault reason etc.
ACKNOWLEDGEMENTS
This work has been carried out in the Department of Civil Engineering, BUET. The authors
gratefully acknowledge the support and facilities provided by BUET.
REFERENCES
Mair, R. J. & Taylor, R. N. (1997). Bored tunneling in the urban environment. In 14th
International conference on soil mechanics and foundation engineering, pages 2353–2385. 5,
10, 14, 169.
Vorster, T. E., Klar, A., Soga, K., & Mair, R. J. (2005). Estimating the e ects of tunneling on
existing pipelines. Journal of Geotechnical and Geoenviromental Engineering, pages 1399–
1410. 8, 22, 52, 65, 117.
Marshall, A. M., Farrell, R., Klar, A. & Mair, R. (2012). Tunnels in sands: the effect of size,
depth and volume loss on greenfield displacements. Géotechnique 62, No. 5, 385–399,
https://doi.org/ 10.1680/geot.10.P.047.
Klar, A., Vorster, T. E., Soga, K. & Mair, R. J. (2007). Elastoplastic solution for soil–pipe–tunnelinteraction.

J.

Geotech.

Geoenviron.

Engng

133,

No.

7,

782–792,

https://doi.org/10.1061/(ASCE) 1090-0241(2007)133:7(782).
Klar, A., Elkayam, I. & Marshall, A. M. (2015). Design oriented linear-equivalent approach forevaluating the effect of tunneling on pipelines. J. Geotech. Geoenviron. Engng 142, No. 1,
04015062, https://doi.org/10.1061/(ASCE)GT.1943-5606. 0001376.
Farrell, R., Mair, R., Sciotti, A. & Pigorini, A. (2014). Building response to tunnelling. Soils
Found. 54, No. 3, 269–279, https://doi.org/10.1016/j.sandf.2014.04.003.
Giardina, G., DeJong, M. J. & Mair, R. J. (2015). Interaction between surface structures andtunnelling in sand: centrifuge and computational modelling. Tunnelling Underground SpaceTechnol. 50, 465–478, https://doi.org/10.1016/j.tust.2015.07.016.
Marshall, A. M. & Franza, A. (2017). Discussion of “Observation of ground movement withexisting pile groups due to tunneling in sand using centrifuge modelling” by Ittichai Boonsiri
and Jiro Takemura. Geotech. Geol. Engng 35, No. 1, 535–539, https://doi.org/10.1007/s10706016-0083-x.
162

Ritter, S., Giardina, G., DeJong, M. J. & Mair, R. J. (2017). Influence of building characteristics
ontunnelling-induced

groundmovements.

Géotechnique

67,

No.

10,

926–937,https://doi.org/10.1680/jgeot.SIP17.P.138.
GSB (1990). Geological Map of Bangladesh.
Mair, R. J. (1993). Developments in geotechnical engineering research: application to tunnels
and deep excavations Proc. Inst. Civil Engineers, Civil Engineering, Vol.93, 27-41.
O’Reilly, M. P. & New, B. M. (1982). Settlements above tunnels in the United Kingdom - theirmagnitude and prediction. In Tunnelling 82, Papers Presented at the 3rd International
Symposium, London, England, Brighton, England. Inst of Mining and Metallurgy. 13, 14, 22.
Attewell, P. B. & Woodman, J. P. (1982). Predicting the dynamics of ground settlement and its
derivatives caused by tunnelling in soil. Ground Engineering, Vol.15, No. 8, 13-22 and 36.
Pescara, M., Gaspari G. M., & Repetto, L. (2011). Design of underground structures underseismic conditions: a long deep tunnel and a metro tunnel. Geodata Engineering SpA, Torino,
Italy.
Power, M. S., Rosidi, D. & Kaneshiro, J. (1996). Strawman: screening, evaluation, and retrofit
design of tunnels. Report Draft. Vol. III, National Center for Earthquake Engineering Research,
Buffalo, New York.
O’rourke, M.J. & Liu, X. (1999). Response of buried pipelines subject to Earthquake effects.
Monograph No.III, MCEER (Multidisciplinary Center for Earthquake Engineering Research)
Publications, University at Buffalo, Red Jacket Quadrangle, Buffalo, NY 14261.
Paolucci, R., Pitikalis, k. (2007). Seismic risk assessment of underground structures undertransient ground deformation. In Pitikalis, K. (editor). Earthquake Geotechnical Engineering.
Chapter 18, pp 433-459. SpringerVienna.
Verruijt, A. & Booker, J. R. (1996). Surface settlements due to deformation of a tunnel in anelastic half plane, Geotechnique, Vol. 46, No. 4, 753-756.
Rankin, W. J. (1988). “Ground Movements Resulting from Urban Tunnelling: Predictions andEffects.” Engineering Geology of Underground Movements Geological Society, 76-88.
Loganathan, N. & Poulos, H. G. (1998). Analytical Prediction For Tunnelling Induced Ground
Movement In Clays. Journal of Geotechnical and Geoenvironmental Engineering / September
1998.
Hunt, D. (2004). Predicting The Ground Movements Above Twin Tunnels Constructed In
London Clay. PhD Thesis. Department of Civil Engineering, School of Engineering, The
University of Birmingham.

163

Zhou, B. (2014). Tunnelling-induced ground displacements in sand. PhD thesis, University of
Nottingham. millan Company, New York.
Technical Manual for Design and Construction of Road Tunnels — Civil Elements. Publication
No. FHWA-NHI-10-034. December 2009.
EUROCODE 8. (2003). Design of Structures for Earthquake Resistance, Draft No. VI, European
Committee for Standardization, Brussels.
BNBC 2006. Bangladesh National Building Code – 2006.
Franza, A., Marshall, A. M. & Zhou, B. (2018). Greenfield tunnelling in sands: the effects of soil
density and relative depth. GéotechniqueFranza A. and Marshall A.M. (2015). “Semi-analytical prediction of ground movements due toshallow tunnels in sand”. Proceedings of the XVI ECSMGE, Geotechnical Engineering for
Infrastructure and Development, ISBN 978-0-7277-6067-8, doi:10.1680/ecsmge.60678.
Atkinson, J. H. and Potts, D. M. (1977). Stability of a shallow circular tunnel in cohesionlesssoil. G´eotechnique, 27(2):203–215. 12, 29, 85.

164PART-X
HUMAN BEHAVIOUR AND WORKPLACE SAFETY
OF READY-MADE GARMENT FACTORIES INDHAKA DURING FIRE: A CURRENT SCENARIOBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Fahad Hossain
IT Hosain
Mehedi Ahmed Ansary165

INTRODUCTION
The Ready-Made Garments (RMG) industry occupies a unique position in the economy of
Bangladesh. It is the largest exporting industry, which experienced phenomenal growthduring the last 25 years. It attained a high profile in terms of foreign exchange earnings,
exports, industrialization and contribution to GDP of Bangladesh within a short span of time.
The industry plays a key role in employment generation and in the provision of income to thepoor. In this study an attempt has been made to describe the overall scenario of Bangladesh
Ready Made Garments industry. Despite the phenomenal success of the RMG industry inremittance earning and employment potential, in the last two decades, this sector has
experienced some worst industrial accidents in the history the fire.
All factories including those in the RMG sector in Bangladesh, have to obtain certificationfrom Bangladesh Fire Service and Civil Defense Authority (BFSCDA) for their compliancewith fire regulations. Purpose-built factories that constructed after 1993 have to comply with
Bangladesh National Building Code of 1993 (BNBC-93) and have to obtain the certificationof local authorities assigned from the Ministry of Housing and Public Works, Government of
the People’s Republic of Bangladesh. Factories operating in old buildings (buildings thosewere built before the code was formulated but was in practice), have to collect another fitness
certificate from the BFSCDA, which allows them to get a second certificate from the Ministryof Housing and Public Works. According to BFSCDA regulations, garment factories have totake an operational certificate at the very beginning of operation from them and this certificate
have to be renewed in every month by the respective zone officials. BFSCDA officials checka total of 61 parameters of which 41 parameters are structural or 'hard' in nature. It was
defined as ‘hard’ factors as those whose conditions cannot be changed very quickly. Since thebuildings are expected to have been built following the building code, our initial expectationis that the ‘hard’ parameters would be in a better condition as compared to the ‘soft’parameters, the performance in which can change from day-to-day due to management
practices.
Two international groups of stakeholders - Alliance for Bangladesh Worker Safety (Alliance)
and Accord on Fire and Building Safety in Bangladesh (Accord) - are currently conducting
regulatory checks and suggesting remedies for every RMG factory of Bangladesh. Although
the focus is wider safety environment, fire safety forms a part of their monitoring process as
well.

166

METHODOLOGY
The survey work was done in two phases. One analysis the human behavior during fire andthe other phase analyse the workplace safety for fire hazard. In the first phase team randomly
selects four garments factory in Bangladesh for survey work. After that the team randomly
selects some workers from the garments and asked them some pre determined questions. ThenSPSS (Statistical Package for the Social Sciences) tool is used to analyse the survey datacollected from the garments factory. In the second phase the team collected some informationfrom the garments factory relevant to fire safety. The information was about factory structure,
emergency exit system arrangements for firefighting and workers training facilities.
Data was collected by questionnaire survey from four factories in Dhaka named Pearl
Garments Company Ltd., MBM Garments Ltd., Cutting Edge Garments Factory Ltd. andCosmopolitan Industries Pvt. Ltd- CIPL-Epic group. Data was collected using real interaction
with people in the site from officers to workers without informing them earlier so thatcompany cannot give any training to the worker to manipulate their opinion.
Company nameCollected data (person)

CIPL

701

MBM

364

CUTTING EDGE

414

PGCL

291

ANALYSIS AND RESULT
Evacuation Behavior: Workers were asked about their evacuation plan and they were
provided with the options like shelter in place, evacuate. If they fail to make choice, then it
was considered the as “undecided”. Any other answer was considered in other category.

Figure1: Fire evacuation behavior in different factories workers.

167

These pie charts show us that in the event of fire in the garments factories the workers
behavior regarding evacuating that place. In all the factories, more than 95% which is the
majority of worker will evacuate that place as early as possible. Other 5% workers have mix
opinion about staying in the place, wait for rescue and some have no idea about what they will
do in the case of fire hazard in their factory. These indecisions are mostly from newly joined
people who did not get any fire training yet and a few are because of age.
Fire Evacuation Timing Based on Gender: Following tables show the cross tabulation
between gender and time to stop the work and leave if there is any effect or not.
Table 1: Fire Evacuation Timing on Gender (Cutting Edge)
In case of emergency, how much what is your gendertime would it take to stop your work Male

Total

Femaleand leaveLess

5 Count

than

minuets
More

196

217

413

47.3%

52.4%

99.8%

1

0

1

% of Total

0.2%

0.0%

0.2%

Count

197

217

414

% of Total

47.6%

52.4%

100.0%% of Total
5 Count

than

minutes
Total

Table 2: Fire Evacuation Timing on Gender (MBM)
In

case

of what is your Total

emergency, how gendermuchtime Male

Femalewould it take tostop your workand leave
Count 191

Less
than

5 %

of

170

361

52.5% 46.7% 99.2%

minutes

Total

More

Count 0

3

3

168

than

5 %

of

0.0%

0.8%

0.8%

Count 191

173

364

minutes Total
Total

%

of

Total

52.5% 47.5% 100.0%

Table 3: Fire Evacuation Timing on Gender (CIPL)
In case of emergency, how much what is your gendertime would it take to stop your work Male

Total

Femaleand leave
Less than 5 minutes
More

than

minutes
Total

Count

305

384

689

% of Total

43.5%

54.8%

98.3%

5

7

12

% of Total

0.7%

1.0%

1.7%

Count

310

391

701

% of Total

44.2%

55.8%

100.0%

5 Count

Table 4: Fire Evacuation Timing on Gender (PGCL)
In case of emergency, how much what is your gendertime would it take to stop your Male

Total

Femalework and leave
Less

than

minutesMoreminutes

5 Count

85

197

282

29.2%

67.7%

96.9%

0

9

9

% of Total

0.0%

3.1%

3.1%

Count

85

206

291

% of Total

29.2%

70.8%

100.0%% of Total
than

5 Count

From the data chart it is seen that from 1770 workers almost all the workers believe that they
can stop what they will be doing and leave the incident place within five minutes. Only 24
workers that they will take more than five minutes where 19 of them are female. This is169because they are may be concern about their children staying in day care centre situated inside
the factory. But this quantity is very low as most of the female workers are unmarried.
Fire Evacuation Timing Based on Age: Following bar chart summarized the cross tabulation
between escape time and age. It was asked that if the people think they will be able to stop thework what they are doing, stop the machine and can leave the compound in less than 5
minutes. The age of the workers is divided into many groups for convenience.

Figure: Cross Tabulation between Escape Time and Age (Cutting Edge)

Figure: Cross Tabulation between Escape Time and Age (MBM)

Figure: Cross Tabulation between Escape Time and Age (CIPL)

Figure: Cross Tabulation between Escape Time and Age (PGCL)

170

From the bar chart it is seen that most of the workers are from 21 to 30 years old except one
factory named CIPL where they have around 34% workers are 19 to 20 years old. In Cutting
Edge factory, among 414 workers 412 of them said that they will be able to move less than 5
minutes. Only 2 out of 414 workers said that they may take more than 5 minutes. This is due
to position working place, and age as those people are around or above 40 years old. In otherfactories, the bar chart illustrates that no matter what the age group is majority of the people
think they will be able to stop the work what they are doing, stop the machine and can leave
the compound in less than 5 minutes. Only a few workers gave voice that 5 minutes is an
inadequate time for leaving. This might be due to their position working place, and some ofthem are willing to help in stopping fire and in some case the machine on which they are
working with is complicated to stop.
Capacity to Protect Against Injury: The following pie charts show the result of the question“Do you have necessary preparation to protect yourself from possible injury?” The answer
was limited to yes and no.

Figure: Capacity to protect against injury in different factories workers.
From the pie charts it is clear that majority of the people think they can protect themselves in
the case of injury during fire. This is a great turnover in the mentality of workers as they get
regular training from the company authority. In the COSMOPOLITAN INDUSTRY PRIVETLIMITED (CIPL) all the 701 workers are confident about there protection against injury.
Only a few workers from other garments do not have the necessary knowledge about how toprotect themselves in the event where injury can occur. This is because they are new and yet
to receive training from the company.

171Regular Fire Drill: Total 1770 people were questioned from four garments factory that if they
have regular fire drill in their factory. It is a very positive result that all the workers conduct
regular fire drill. Authority also learnt from previous fatal accidents and they are more aware
about there workers safety.
Evacuation Plan: The next pie charts below illustrate the opinion of the workers about how
they prefer to evacuate in case of fire incident. They have options like evacuate themselvesalone or along with their colleagues.

Figure: Evacuation plan in different factories workers.
From the pie charts it is seen that in all of the four garments around 70% of the workers wantto evacuate with their colleagues where rest people think they will evacuate themselves alone
as early as possible. This is because they are going through regular training and fire drill fromcompany. But still most of the elder people aged above 35 who have family do not want totake risk in case of fire as they have seen some fatal incidents earlier. In total, it is a positive
motivation among most of the workers.
Education and Evacuation Plan: The following charts represent changes in evacuation planwith the level of education. Most of the workers have studied from Primary School Certificate
to maximum Higher Secondary Certificate. Only manger and top-level employees aregraduate. Security guards and other staffs who are not directly related to production who haveno educational qualification and they are introduced by None.
These charts show the relation between escape plan and highest level of education. It showsthat no matter what the level of education is majority of the people will always try to evacuatewith their colleagues.
Help Stopping Fire: The following bar chart shows the outcome of the question “In case offire, will you help stopping the fire or evacuate?”. Option was ‘I will help stopping fire’ and ‘I
will evacuate’.
172

Figure: Education and Evacuation plan in different factories workers.
The bar charts show that more than half workers from all the garments are willing to help
stopping fire during a fire emergency. Among them more than 70% of CIPL workers havepositive thinking about it where PGCL have 53.6%. This is because they have less worker and
CIPL has 2.5 times more worker than PGCL and most of them are young and energetic. Thisis a positive scenario in RMG factories. It is happening because authority and workers bothare conscious about their safety and rules during an incident. And the dramatic changes in173

mentality and behavior of workers are the results of regular training and fire drill in working
place.

Figure: Willingness to stop fire in case of fire incident in different garments workers.
CONCLUSION
This paper explored the current behavior of workers in different RMG factories from manager
to operator during a fire incidence. The response during fire incident is positive especially
their willingness to evacuate with colleagues and help stopping fire. These are because ofregular training and fire drill arranged from the authority. This positive behavior will not only
save lives but also it will save a lot of goods and reduce a lot of property damage. This is asignificant movement in RMG sector of Bangladesh for workers as well as authority aftersome big fatal incident and bad reputation regarding workplace safety. This observation gives
an insight to the turnover in workers behavior during fire incident and allow the investors asecond thought before leaving this sector.
By conducting this type of behavior analysis will help to avoid accident related injury or death
it will also provide perception about strength and weakness of that company to face fire
hazard.
Acknowledgments
Authors like to give special thanks to S. M. Tanveer Anjum, Jenath Sultana Joya and Afsana
Zaman Pranty for helping in the survey and collecting data.
References[1] Wadud Z., Huda F.Y., "Fire Safety in the Readymade Garment Sector in Bangladesh:
Structural Inadequacy vs. Management Deficiency", May, 2016.

174

[2] Mannan M.S. (PE, CSP, DHC), "The Journey to Safety Excellence in the Garment
Industry in Bangladesh: Past, Present and Future", May, 2016.
[3] Campbell R., "Home Fire Involving Clothes Dryers and Washing Machines", NFPA
research, March, 2017.
[4] Campbell R., "Fires in Structures under Construction, Undergoing Major Renovation or
Being Demolished", NFPA research, April 2017.
[5] Ahrens M., "Fires by Occupancy or Property Type 2010-2014", NFPA research, March
2017.
[6] Colpas E., Zulueta J.D., Pappas D., "An Analysis of Hoarding Fire Incidents and MFB
Organizational Response", WPI (Worcester Polytechnic Institute), May, 2012.

175PART-XI
ASSESSMENT OF SEISMIC VULNERABILITYINDEX OF RAJUK AREA IN BANGLADESH USINGMICROTREMOR MEASUREMENTSBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: A. Khair
Mehedi Ahmed Ansary176

1. INTRODUCTION
Bangladesh has not suffered any damaging large earthquakes in recent past; but, in the past few 100
years, several large catastrophic earthquakes have struck this area. So far, all the major recent
earthquakes have occurred away from major cities, and have affected relatively sparsely populatedareas (Ansary & Raman, 2012). Dhaka city is the most populated city in Bangladesh having
population of 19 million. Besides, Bangladesh is located in a region of significant seismic activity,
most of the population and policy makers do not perceive seismic risk to be important (Ansary &Rashid, 2016). It is a cause for great concern that it may cause high damage to the infrastructures
when the next great earthquake may occur in this region.
In many past and recent earthquakes, it has been observed that the local soil conditions and topographic
effects have a great influence in the damage distribution. Severe damage even at large epicentral
distances may occur due to the local site effects and double resonance (double resonance is theresonance of body wave frequency with F0 of soil and then again resonance with the natural frequencyof structure) (Shiuly et. al., 2014). Microtremor observations are easy to perform, inexpensive and canbe applied to places with low seismicity. The H/V spectral ratio technique of microtremors has
gained popularity in the early nineties, after the publication of several papers (Nakamura 1989; Field
and Jacob 1993; Lermo and Chavez-Garcia 1994) claiming the ability of this technique to estimate
the site response of soft sedimentary deposits satisfactorily.
2. STUDY AREA AND SITE SELECTION
Dhaka is the capital, cultural, political, and financial center of Bangladesh. It is one of the largest andmost densely populated cities in South Asia. Microtremor studies have been conducted inside the
Dhaka city by several researches, Ansary and Saidur Rahman (2012) and Ministry of Food andDisaster Management (MoFDM) in 2009. In the current study, the location of stations will beconsidered for the whole Dhaka city and RAJUK (Capital Development Authority) area. The
application of microtremor analysis in this study is to determine the seismic vulnerability index (Kg),by using the H/V ratio Method. The primary distribution of the 500 stations is shown in Figure 2.
3. DESCRIPTION OF SITE EFFECT
The influence of local geologic and soil conditions on the intensity of ground shaking and earthquake
damage has been known for many years. Wood (1908) and Reid (1910) showed that the intensity ofground shaking in the 1906 San Francisco earthquake was related to local soil and geologic
conditions. Gutenberg (1927) developed site-dependent amplification factors from recordings of
microseisms at sites with different subsurface conditions.

177

Figure 1: Bangladesh country andmetropolitan area within RAJUK area

Figure 2: Distribution of the microtremor
record stations in RAJUK area

3.1 Effects of local site conditions on ground motion
Local site conditions can profoundly influence all of the important characteristics of strong groundmotion including amplitude, frequency content, and duration. The nature of local site effects can beillustrated in several ways: i) simple, theoretical ground response analyses, ii) measurements ofactual surface and subsurface motions at the same site, iii) measurements of ground surface motions
from sites with different subsurface conditions. Local site effects can be very significant, particularly
when long period structures such as bridges and tall buildings are founded on deposits. In general,
the effects of topography and basin are the main factors of site effects phenomenon.
3.1.1 Topography effect
The topographic effects caused by simple irregularities can be estimated from exact solutions toidealized problems (Aki, 1988). For a triangular infinite wedge subjected to vertically propagating
SH-waves (with particle motion parallel to its axis), apex displacements are amplified by a factor
2

where

is the vertex angle of the wedge (Figure 3 left). This approach can be used toapproximate topographic effects for certain cases of ridge-valley terrain (Figure 3 right).

Figure 3: Characterization of simple topographic irregularities: (left) notation for a triangular wedge;(right) approximation of actual ground surface (solid line) at trough and crest by wedges.
(After Faccioli, 1991.)
178

Different geometries and different wave fields have been considered also (e.g., Geli et al., 1988;
Sanchez-Sesma, 1990; Faccioli, 1991). A schematic view of topography effect on amplification ofhorizontal acceleration for a linear elastic slope is presented in Figure 4.

Figure 4: Topography effect on amplification of horizontal acceleration for a linear elastic slope
3.1.2 Basin effectSince many large cities are located on or near alluvial valleys, the effects of basin geometry on
ground motion is of great interest in geotechnical earthquake engineering. The curvature of a basin inwhich softer alluvial soils have been deposited can trap body waves and cause some incident bodywaves to propagate through the alluvium as surface waves (Vidale and Helmberger, 1988). Thesewaves can produce stronger shaking and longer durations than would be predicted by one
dimensional analysis that consider only vertically propagating S-waves.
3.2 Methodology to evaluate site effect
The most preferred methodology in estimating PGA on bedrock is to use attenuation relations.
Though, all the building structures are not situated on the bedrock, they are mostly located on soil
surface. Consequently, applying attenuation relations will be accompanied by high uncertainties,
while local site effect needs to be assessed. The common approaches for evaluation of local site
effects are categorized as follows: Level 1: evaluating the local site effects using existing information that is readily
available from published reports and other sources.
 Level 2: methods require additional investigations including geotechnical investigations, geophysicaltesting and soil sampling from boreholes for laboratory tests.
Level 3: methods of zonation require the conducting of ground response analyses (including the
one-dimensional equivalent-linear and nonlinear analyses, and 2D and 3D analyses).

3.2.1 Microtremor survey method
The surface of the earth is always in motion at seismic frequencies, even without earthquakes. These
constant vibrations of the earth’s surface are called microseisms or microtremors. The amplitude ofthese microtremors is, with some extreme exceptions, generally very small. Although they are very
179

weak, they represent a source of noise to researchers of earthquake seismology; if amplifier gain is
increased to record earthquake signals from a distance source, the amplitude of microtremors
proportionally increases, and the desired earthquake signal is buried in the “noise” of microtremors.
Elimination of this background noise is technically extremely difficult or impossible to achieve.
3.2.2 H/V determination based on Nakamura technique
The method use for data analysis is the Horizontal to Vertical Spectral Ratio (HVSR) approachdeveloped by Nakamura (1989). The HVSR approach is applied to ambient noise and generates a
Fourier spectral ratio of amplitude versus frequency. The HVSR method divides the horizontal
component of noise to the vertical component to remove source effects as shown in Figure 5. The
spectral ratio is calculated by taking the Fourier transform (Welch, 1967) of the ambient noise
recordings.

Figure 5: Schematic of H/V method (Nakamura, 2008)
H/V technique has been frequently adopted in seismic microzonation investigation due to its lowcost both for the survey and analysis. The method is especially recommended in areas of low andmoderate seismicity, due to the lack of significant earthquake recordings, as compared to highseismicity areas. This technique consists in estimating the ratio between the Fourier amplitude
spectra of the horizontal (H) to vertical (V) components of the ambient noise vibrations recorded at
one single station. There have been some criteria given by SESAME European research project(2004), in order to obtain a reliable H/V curve and clear H/V peak, which are not mentioned here.
4. MICROTREMOR DATA COLLECTION AND ANALYSIS
The microtremor survey has been carried out in 500 locations in RAJUK area. At first, a regularsquare network with approximately 1.7 kilometers sides was considered which; one station was
implemented in each square. The regularity of the stations then was disoriented based on the nearest
sensitive center inside the square (i.e. health facilities, police stations, fire stations and, educational
centers) and accessibility through the roads. Then, the stations were omitted from military area andflood plains. A few coordinates of the planned stations are presented in Table 1.
180

Table 1: Coordinate of a few microtremor stations
No.

StationLongitude

LatitudeNo.

No.

StationLongitude

Latitude

No.

1

P307

90.350592

23.854833

4P373

90.433492

23.899472

P308

90.361233

23.857776

5P39090.368273

23.916326

3

P309

90.382323.854792

6

P391

90.385777

23.914658

4.1 Data Acquisition
Microtremor data were collected on a three component Sensors of GS11D Geospace Technology
with an internal 12V rechargeable lead battery with the capability of 5 hours continues recording.
More over the data logger having the characteristics are- A/D converter: 24-bit monolithic sigmadelta converter, 1 per channel, Sensitivity: 78 v/m/s (±10%) Natural Frequency: 4.5 Hz (±15%) etc.
Data were acquired using Seismowin Software. Surveys were made at 100 samples per second.

Figure 6: Illustration of a Seismowin Software during data acquisition in the field
4.2 Data Analysis and Obtaining the H/V curve
All 500 recorded data were analyzed by expert seismologists. The H/V method is applied to obtainthe natural frequency and amplification factor in the stations. The obtained graph from all the recordand after noise removal are shown in Figure 7 and Figure 8. The assessed parameters including
natural frequency, amplification factor and natural period in station 5 are presented in Table 2.

Figure 7: The H/V graph for all length of Figure 8: The H/V graph of the station 5
the record in station 5

after noise removal
181

Table 2: The obtained results of H/V graph for station 23
StationNo.
5NaturalAmplifi-Natural Vulnerability

Longitude Latitude Frequency

cationPeriod90.4372

23.6236

Index

Remarks2

f0 (Hz)Factor(A0)(s)(K=A0 /f0)

0.96061.64011.041

2.8003low

5. RESULTS
Seismic vulnerability index (Kg) is an index indicating the level of vulnerability of a layer of soil todeform. Therefore, this seismic vulnerability index is helpful for locating of areas that are weak zone
(unconsolidated sediment) during occurrence of earthquakes. Some studies like Nakamura (2000)
showed a good correlation between seismic vulnerability index (Kg) and the distribution ofearthquake disaster damage. This index is obtained from the peak value of HVSR squared, divided
by the value of the predominant frequency. The seismic vulnerability index has been classified into
four major types. These are Low (0–5), Moderate (6–10), High (11–20), and Very High (>20). The
highest Kg value has been obtained at station P326 (90.346478, 23.8654). It may be concluded that
this location is relatively weaker than other locations. Most of the zones having higher Vulnerability
Index (Kg) are situated in reclaimed areas. In the following Figure 9 and Figure 10 the zonation
maps of the natural frequency and amplification factor are presented.

Figure 9: Natural frequency variation in Figure 10: Amplification factor variation in
RAJUK areaRAJUK area

182

Figure 11 shows the vulnerable area in GIS map of 500 locations using Nakamura’s Vulnerability
Index (Kg). The number of low vulnerability type locations, which is 381, is the most common
among 500 locations. The second most predominant number of vulnerability type is moderate type,which varies between 6 and 10. The number of very high type vulnerable site is seven.

Figure 11: Seismic vulnerability index variation in RAJUK area
6. CONCLUSIONS
The microtremor investigation has been conducted within the RAJUK area. A network of 500
stations has been designed, considering the sensitive centers, roads, restricted areas, and flood plains.
The outputs of the study have been presented in the form of natural frequency, amplification factor
and seismic vulnerability index distribution. The predominant frequencies of the study area are
relatively uniform, ranging from 0.2 to 8.0 Hz. The low frequencies are obtained to soft soil sites inmaximum measurements and the high frequencies are obtained to stiff soil sites in a few
measurements. The amplification factor (A0) or peak of H/V spectral ratio is in investigation sites
ranging from 0.8 to 6.6. Low values are measured for stiff soil sites and high values for soft soil
sites. The seismic Vulnerability Index (Kg) varies between 0.2 and 124. The low seismic
vulnerability index indicates that the areas are very stiff as well as thick sediment deposit. Seismic
vulnerability index (Kg) in the study area has been found that the high values are scattered in the soft
alluvial deposits area having a high seismic vulnerability indication. The results of microtremor
investigation can be used as the input of many other studies, such as the site selection of geotechnicalboreholes.

183

REFERENCES
Aki, K., (1988): Local site effects on strong ground motion, proceedings, earthquake engineeringand soil dynamics ii – recent advances in ground motion evaluation, ASCE, geotechnical special
publication no. 20, pp. 103-155
Ansary, M. A. & Rahman, M. S., (2013). Site amplification investigation in Dhaka, Bangladesh,using H/V ratio of microtremor. Environmental Earth Sciences, ISSN 1866-6280, 70:559–574
Ansary, M. A. & Rashid, M. A., (2016). Generation of Liquefaction Potential Map For Dhaka,Bangladesh. 8th ASCE Specialty Conference on Probabilistic Mechanics and Structural Reliability
PMC2000-061
Faccioli, E., (1991): Seismic amplification in the presence of geological and topographic irregular-ities,
2nd international conference on recent advances in geotechnical earthquake engineering and soil
dynamics, st. louis, missouri, vol. 2, pp. 1779-1797.
Gutenberg, b. (1927): grundlagen der erdlebenkunde, Berlin.
Nakamura Y. (1989) A method for dynamic characteristics of subsurface using microtremors on the
ground surface, Quick Reportof Railway Technical Research Institute, vol 30, no 1, pp 25–33 (inJapanese)
Nakamura,Y.,(2008):

On

the

H/V

spectrum,

the

14th

world

conferenceon earthquake engineering, Beijing, China.
SESAME European research project, (2004): guidelines for the implementation of the h/v spectral
ratio technique on ambient vibrations, measurements, processing and interpretation, european
commission-research general directorate project no. evg!-ct-2000-00026 SESAME.
Shiuly, A., Kumar, V. & Narayan, J. P., C., (2014). omputation of Ground Motion Amplification in
Kolkata Megacity (India) Using Finite-Difference Method for Seismic Microzonation. Acta
Geophysica, vol. 62, no. 3, pp. 425-450
Vidale, J. E., Helmberger, D. V., (1988): elastic finite difference of the1971 san fernando earthquake,"bulletin of the eismological society of America, vol. 78, no. 1, pp. 122-141.

184

PART-XII
THREE DIMENSIONAL FINITE ELEMENTMODELING OF CONTIGUOUS PILE WALL AS A
SOIL RETAINING STRUCTURE ON THE CONTEXT
OF BANGLADESHBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Istiakur Rahman
Mehedi Ahmed Ansary185

1. INTRODUCTION
Construction projects involving deep excavations are widespread in many urban areas around theworld. The commonly used systems for the support of such deep excavations to minimize lateral andvertical ground movements are secant and contiguous pile walls, diaphragm walls, and steel sheet
piles. The above soil retention systems are used in combination with horizontal struts, corner struts,
and anchors, etc. (Ou, 2006). The behavior of a deep excavation support system is defined andanalyzed by using a number of measures, mainly: (a) the displacement of wall elements, (b) the
movement of soil masses surrounding the excavation, (c) the movement of existing adjacentstructures, and (d) the forces acting on the lateral support elements (Ng et al., 2004). The abovemeasures can be evaluated by the following methods: (1) performance of numerical analyses(Zdravkovic et al., 2005), (2) analyzing physical models of small and medium scale (Laefer et al.,2009) and (3) collecting performance data from instrumented large scale deep excavation projects(Zekkos et al., 2004).
Numerical modeling is an effective way to investigate the performance of deep excavations. A
significant amount of numerical analyses have been conducted on deep excavations to approximate
real deep excavations in the design process by adopting 2D analyses (Clarke et al., 1984; Hubbard etal., 1984; Finno et al., 1991; Hashash et al., 1996). Most of these analyses rely on simplifying
assumptions, and therefore, the information they can provide is limited and sometimes misleading.
In reality, all geotechnical problems involving retaining structures are three dimensional, andideally, three-dimensional analyses, fully representing the structure’s geometry, loading conditionsand variations in ground conditions across the site. Three-dimensional analyses have someadvantages over two-dimensional analyses. For instance, a 2D analysis is not able to consider thecorner effects in deep excavations, which indicate that the wall deformation and ground movement
are smaller closes to the wall corner than around the wall center. Besides, 2D plane strain analysis
tends to overestimate the wall deflection and ground settlement behind the wall compared to thesimplified 3D symmetric square or rectangular analysis (Gouw et al., 2014; Ou et al., 1996).
In Bangladesh, reinforced retaining system, i.e., contiguous pile wall is common and relatively
economical to be used in cohesive soil. There is a scarcity of research work regarding the finite
element application on contiguous pile wall. Recent work has been done by Vinoth and Ghan(2018)
to present a case history on the use of temporary support of excavation using contiguous bored piled
wall technique for deep basement excavation in Chennai. They conducted a two-dimensional finite
element analysis using PLAXIS 2D and monitored the data with a total station. They concluded byobserving the Total station measurements that contiguous pile moved towards retaining side186

(negative deflection) at the top of the contiguous pile wall due to the development of tension zones
behind the contiguous pile.
In this Study, a three-dimensional analysis of Contiguous pile wall based on site investigation reporttaken in Dhaka the capital city of Bangladesh has been conducted using PLAXIS 3D. The practicaldemonstration will be demonstrated by validating a case study of Siam Motor building in Bangkok
and also validating with field monitoring data of strut force in excavation using vibrating wire strain
gauge obtained from the construction of a Pier in 2nd Bridge over Gumti River.
2. MODELING USING PLAXIS 3D
In this study, several three-dimensional finite element model have been analyzed by using PLAXIS
3D. The models include a soil profile, a deep excavation with different basement levels and bracing
components, nearby structures, etc.
A summary of engineering properties collecting from the soil investigation report of the site inDhaka which have been used for both MC model and HS model are given in Table 1. According to
the site investigation report, the site is underlain by alluvial deposits. The groundwater table was well
below the borehole depth of 39m. The soil profile is divided into 4 sub-layers according to thedifference in soil characteristics, physical and mechanical properties. The subsurface consists of a
10m layer of stiff silty clay and 5 m thick layer of medium dense fine sand. Underlying is a 15m
thick very dense silty fine sand. Underneath the silty fine sand is a 9.5m thick layer of very densefine sand.
Table 1: Engineering Properties of the site in Dhaka
Formation
Parameter

Unit

Avg SPT N
Unit weight
Dry

kN/m3

unit kN/m3

Stiff Silt with Medium dense Verylittle fine sandfine Sandfine sand

16

37

>50

19

18

20

15.8

16

17

weightLiquid limit

%

50

-

-

Plasticity

%

22

-

-

kPa

19

-

-

index
Undrained

dense

Shear
Strength, Su
187

Cohesion, c`
Angle

kPa

31

0

0

of Degree

14

31

33

Degree

0

1

3

0.3

0.3

0.3

m/s

7x10-105.27x10-6

5.78x10-6

kPa

26500

27000

28000

kPa

35000

43000

35000

kPa

33000

22000

35000

kPa

105000129000105000friction,
Dilantancy
angle,
Poision’s
ratio,
Co-efficientofPermeability
Young
Modulus, E
SecantModulus, E50
Oedometer
Modulus, Eoed
Unloadingreloading
Modulus, Eur
Soils were modeled by 10-noded elements. Undrained Method A was adopted for MC model andUndrained method D was adopted for HS model. Initial groundwater was assumed to be 50 meters
below the existing ground surface for the site in Dhaka. Reduced shear resistance was assumed witha standard reduction factor of 0.7 for contiguous pile wall and 0.5 for basement slab. The contiguous
pile wall modeled as an embedded beam element.

The interaction between the pile and thesurrounding soil at the pile shaft is described employing embedded interface elements. There is noneed for mesh refinement around piles as the 3D mesh is not distorted by introducing embedded
elements which make embedded piles very efficient and time-saving especially when a large groupof piles is modeled (Engin and Brinkgreve 2009). The internal strut, walling, king post, and raftfoundation was modeled as beam element whereas bored piles were modeled as embedded beam.
Piled-raft foundation system was considered as the type of foundation. For advanced HS model,
similar assumptions were used so that a direct comparison of performance could be made. Additional
geotechnical parameters required for the input were added to those used in the MC model.

188

A cross section of the excavation zone has been presented in Figure 1. The section is approximately
35 m across between contiguous pile walls and the excavation is 3m, 6m, and 9m respectively forsingle, double and three basement system deep. The finite element model connectivity plot after thebase slab being cast for the contiguous pile is presented in Figure 2.

Figure 1: Plan layout of excavation zone

Figure 2: Connectivity plot of finite element model
A summary of structural elements properties are given in the following Table 2.
Table 2: Summary of structural elements properties

Properties

Unit

Steel

Steel

King

Cap

Strut

Walling Post

Beam Pile

78.5

24

Unit Weight, γ

kN/m3 78.5

Area, Am2

0.00736 0.00862

0.4900 0.700

Thickness, d

m

-

-

-

Modulusof

Elasticity, E
DiameterElement Type

kN/m2
m

24

Contiguous

Pile
below
raft

24

24

210

210

30

30

30

30

x106

x106

x106

x106

x106

x106

-

-

-

-

0.5

0.5

Beam

Beam

Beam

Beam

Embedded

Embedded

Beam

Beam

189

3. VALIDATION OF MODEL
In this study, validation of the model has been done by taking field instrumentation data of theconstruction of a pier in the 2nd bridge over the Gumti River. Vibrating wire strain gauge has beenused in the investigation of lateral movement of soil supported by sheet pile wall with eighthorizontal struts. The struts were installed at the corners of two different levels. The forces in all theeight struts were estimated from frequency reading taken every five seconds over six months.
A model of the bridge pier has been done using PLAXIS 3D. The cross-section of the bridge pier isshown in Figure 3.

Figure 3: Cross section of bridge pier (No. 12)
The meshing of the model is done by using coarse meshing and coarseness factor as 0.25. Deformed
mesh of the model is shown in Figure 4.

Figure 4: Deformed mesh of the model
It has been observed from the field instrumented data for strut force is that maximum force acting on
the strut is approximately 202 kN whereas maximum force obtained for strut force using PLAXIS
3D is 206 kN. The difference between the results is around 2%. In this particular case, it may beconcluded that the model is validated.

190

Another model was validated by following a case study of Siam Motor Machine Building in
Bangkok which was conducted by Chhunla Chheng and Suched Likitlersuang (2017). The output
result of the displacement of the model is shown in Figure 5.

Figure 5: Displacement of the Sheet pile wall
It is observed that maximum displacement occurs in the short side of the sheet pile wall where B1
inclinometer was used to measure the reading of displacement. The data for the displacement of theshort side of the sheet pile wall obtained from the study of Chhunla Chheng and Suched
Likitlersuang (2017) was 216mm and the same data appeared in the inclinometer was 222mm. In thisstudy, the value obtained for the displacement of the same side of the sheet pile was 208.4mm. The
discrepancy between the performed analysis with the review of Chhunla Chheng and Suched
Liktlersuang is around 3.5% whereas the difference is about 6% with the inclinometer data. As thedifferences are within 10%, so it is recommended that the model is validated for this case.
4. RESULT AND DISCUSSION
Performance of deep excavation is related to both stability and deformation. The ratio of wall depth
to an excavation depth of 2 was applied to provide a comfortable margin of safety against wall
instability and excessive base heave. With the strengthening of retaining structure in place, theaspects of interest in the performance of deep excavation at hand reduced to wall deflection. The
results of numerical modeling of the excavation using the Mohr-Coulomb (MC) and Hardening Soil
(HS) models for the site in Dhaka using contiguous pile wall retaining system have been obtained
and compared with the different conventional method. Note that the while drawing different graphs
contiguous pile is set at the origin point of the graph and extends up to pre-defined depth for different
cases in the Y-axis of the graph. In this study, the Ux value obtained from the PLAXIS output resultsof wall displacement depicts the lateral movement for the short side of the retaining wall while the191Uy value renders the lateral displacement value of the long side of the retaining wall. Moreover, thepositive value of Ux indicates that the wall may be pushed into the soil retained and negative valueof Ux represent the wall may tile away from the soil retained for this particular study. Again, thepositive value of Uy shows that the wall moves away from the soil retained and the negative value of
Uy indicates the wall may be pushed into the soil retained. The displacement in the X, Y, Z direction,
as well as the total displacement, are also depicted in the graph to have a general idea of wall
deflections for a particular case.
The contiguous pile wall displacements for Dhaka site for double basement system for both the longside and short side of the contiguous pile wall obtained by applying MC and HS model is shown in
Figure 6.
0

0
Ux

Ux

Uy

Uy

Uz

Uz

Total
Displacement

Total
Displacement
HS

MC

Depth (m)Depth (m)

HS

-5

-10

-20

MC

-5

-10

-10

0

10

20

-10

Displacement (mm)

0

10

20

Displacement (mm)Figure 6: Contiguous pile wall displacement in the short and long side-MC & HS Model
It can be observed from the output result of MC model is that maximum lateral displacement isfound out to be around 12mm in the long side of contiguous pile wall and 13mm in the short side ofcontiguous pile wall whereas in case of HS model the maximum lateral displacement is 5mm in thelong side of contiguous pile wall which is noticed at 3m below the ground surface and 4mm in the
short side of contiguous pile wall. In case of MC model, the maximum lateral displacement is on thepositive side in the long side and on the negative side in the short side of contiguous pile wall whichindicates that the contiguous pile wall move away from the retained soil the displacement occurs insuch a way that it indicates that the wall move away from the retained soil and the scenario is sameas well as for HS model. The maximum total displacement obtained from the MC model isapproximately 16mm in the long side of contiguous pile and 18mm in the short side of contiguous
pile wall and it is 8mm for both the side in case of HS model. In both the case, the effect of raft slab
to restrain wall movements is obvious. It is also observed that movement at the bottom of the wall isalmost restrained. Moreover, the bulging of deflection curves above the raft slab is also visible. In
addition, the ground settlement of the contiguous pile wall is also obtained.
192

A comparison of the output result with the available conventional method is shown in Table 3.
Table 3: A comparison of output result with available literature for the displacement of contiguous
pile wall for MC and HS modelResearcher/

Basis of the Result

Research groupcomparison

Cloughand Lateral

O’Rourke (1990)
CloughDisplacementand Lateral

O’Rourke (1990)

Yandzio (1998)

Displacement
Lateral
Displacement
MaximumEngineering

total

Handbook (2007)

displacement
Lateral
Displacement

fromthe literature

HS modelMC model

12mm

5mm

13mm

18mm

5mm

13mm
Not matched

Bottom of the

National

Kung (2007)

from Result from Result

wall assumed Matched
not to displace

25mm-75mm

7mm

18mm

12mm

5mm

13mm

In this study, the effect of the adjacent structure on the model has been investigated. It has been
considered that in total 8 six-storied buildings are around the excavation area. The surcharge loadthat has been imposed by each building is assumed as 86 kN/m2. The displacement of contiguous
pile with presence of adjacent structures is shown in Figure 7
0
Ux
Uy
Uz

Depth (m)Total
Displacement

-5

-10

-20

0

20

40

Displacement (mm)Figure 7: Contiguous pile of displacement with presence of adjacent structure
193

The effect of the adjacent structure on the model is visible. The maximum lateral displacement isfound 12mm in the long side and 13mm in the short side of the contiguous pile wall without thepresence of any adjacent structures by applying MC model whereas the value is 21mm and 9mm in
the long side and short side respectively in the presence of nearby structures.
In the actual model, six struts are used in the X direction keeping 5m spacing, and five struts are usedin the Y direction maintaining the same spacing like X direction. The influence of the number of
struts has been investigated by reducing the number of struts in both the direction. The struts are
reduced to four struts in the X direction by maintaining 7m spacing and two struts in the Y direction
by keeping 10m spacing. The contiguous pile wall displacement reducing the number of struts isshown in Figure 8.
0
Ux
Uy
Uz

Depth (m)Total
Displacement

-5

-10

-10

0

10

20

Displacement (mm)Figure 8: Contiguous pile wall displacement - reducing the number of strut in both side
The effect of the reduction of the number of struts on the model is evident. The maximum lateraldisplacement is found 12mm in the long side of the contiguous pile wall whereas the value is found
13mm in the long side while reducing the number of the struts
5. CONCLUSIONS
In general, it is noticed that the HS model predicts lower than the MC model both for the Sheet pilewall and contiguous pile wall. The reason behind this is due to the fact that the MC model does notconsider the strain-dependent stiffness behavior or the small strain characteristics that involve highstiffness modulus at small strain levels of soil. The MC model only uses a single Young's modulus
and does not also distinguish between loading and unloading stiffness. In the HS model, soil stiffness
is calculated much more accurately by using three different stiffnesses (triaxial loading secant
stiffness, triaxial unloading/reloading stiffness and oedometer loading tangent stiffness). The MCmodel represents Young's modulus of soil in the in situ stress state. On the other hand, the HS model194represents its three moduli at the reference pressure, and these moduli at the in situ stress state areautomatically calculated as a function of the current stress state. Moreover, the displacements ofcontiguous pile wall show good harmony with the available conventional methods. The effect ofadjacent structures and reduction of struts on the displacement of contiguous pile wall is also evident
from the model.
REFERENCES
Brinkgreve, R.B.J., Engin, E. and Swolfs, W.M., 2012. Plaxis 3D material models manual. In Plaxisbv.
Chheng, C. and Likitlersuang, S., 2017. 3D Finite Element Modelling of Sheet Pile Wall Excavation:
A Case study in Bangkok. IPTEK Journal of Proceedings Series, 3(6).
Clarke, B.G. and Wroth, C.P., 1984. Analysis of Dunton Green retaining wall based on results ofpressuremeter tests. Geotechnique, 34(4), pp.549-561.
Clough, G.W., 1990. Construction induced movements of in situ walls. Design and performance ofearth retaining structures, pp.439-470.
Finno, R.J., Harahap, I.S. and Sabatini, P.J., 1991. Analysis of braced excavations with coupled finite
element formulations. Computers and Geotechnics, 12(2), pp.91-114.
Tjie-Liong, G.O.U.W., 2014. Common mistakes on the application of Plaxis 2D in analyzing
excavation problems. International Journal of Applied Engineering Research, 9(21), pp.8291-8311.
Hashash, Y.M. and Whittle, A.J., 1996. Ground movement prediction for deep excavations in soft
clay. Journal of geotechnical engineering, 122(6), pp.474-486.
Hubbard, H.W., Potts, D.M., Miller, D. and Burland, J.B., 1984. Design of the retaining walls for theM25 cut and cover tunnel at Bell Common. Geotechnique, 34(4), pp.495-512.
Kung, G.T., Juang, C.H., Hsiao, E.C. and Hashash, Y.M., 2007. Simplified model for wall deflection
and ground-surface settlement caused by braced excavation in clays. Journal of Geotechnical andGeoenvironmental Engineering, 133(6), pp.731-747.
Laefer, D.F., Ceribasi, S., Long, J.H. and Cording, E.J., 2009. Predicting RC frame response to
excavation-induced settlement. Journal of geotechnical and geoenvironmental engineering, 135(11),pp.1605-1619.
National Engineering Handbook 2007. “Design and Use of Sheet Pile Walls in Stream Restorationand Stabilization Projects.” Technical Supplement 14R, Part 654.
Ng, C.W., Simons, N.E. and Menzies, B.K., 2004. A short course in soil-structure engineering ofdeep foundations, excavations and tunnels (Vol. 5). Thomas Telford.
Ou, C.Y., 2006. Deep excavation: Theory and practice. Crc Press.

195

Ou, C.Y., Chiou, D.C. and Wu, T.S., 1996. Three-dimensional finite element analysis of deep
excavations. Journal of Geotechnical Engineering, 122(5), pp.337-345.
Vinoth, M. and Ghan, S.M., 2019. Support of Deep Excavation Using Contiguous Pile—A Case
Study. In Geotechnical Applications (pp. 273-281). Springer, Singapore.
Yandzio, E., 1998. Design guide for steel sheet pile bridge abutments. Steel Construction Institute.
Zdravkovic, L., Potts, D.M. and St John, H.D., 2011. Modelling of a 3D excavation in finite element
analysis. In Stiff Sedimentary Clays: Genesis and Engineering Behaviour: Géotechnique Symposiumin Print 2007 (pp. 319-335). Thomas Telford Ltd.
Zekkos, D.P., Athanasopoulos, A.G. and Athanasopoulos, G.A., 2004. Deep Supported Excavationin Difficult Ground Conditions in the City of Patras, Greece—Measured vs. Predicted Behavior.

196

PART-XIII
BUILDING CODE IMPLEMENTATION AND
ENFORCEMENT STRATEGY FOR RAJUKBANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Mehedi Ahmed Ansary197

INTRODUCTION
According to Ansary and Barua (2015), rampant safety issues were discovered at more than athousand RMG factories in Bangladesh. The country has been under pressure to improve its safety
record after the 2013 Rana Plaza building collapse, one of the worst in the industry, killing more than
1100 workers. After the incident an Accord for Fire and Building Safety in Bangladesh was signed.
The Accord is an agreement between more than 180 largely European apparel makers including
H&M and Adidas. Signatories pledge to increase safety standards at Bangladeshi factories. The
agreement required independent inspections and public reporting of the results.
More recently, in March 2019, another major Fire broke in the 24-story F.R. Tower, killing 25
people. In a drive launched after the FR Tower fire incident, RAJUK has found 1,155 buildings thatare in violation of fire safety regulations. Meanwhile, at least 431 residential high-rise buildings and
44 government establishments were built violating the building code, the capital city development
authority has discovered. According to the RAJUK report, only 539 buildings in the city havesufficient fire safety facilities – i.e. fire extinguishers, fire alarm, hose pipes, and fire hydrants.
The article establishes the absolute need for code enforcement within the area governed by RAJUK
by highlighting the poor state of fire safety in Dhaka as well as by comparing the major fire incidents
that have taken place in Dhaka in recent years. One common thread that runs through all the manmade (as opposed to natural) disasters is that they are often directly traceable to code and safety
violations that are quite often blatant.

GAP ANALYSISThe following are some of the important findings and suggestions from the extensive stakeholderinterviews:
No Certificate of Occupancy is taken out for 99% of buildings. This makes a mockery of theentire regulatory process.


Without Certificate of Occupancy, municipal corporations should withhold issuance ofholding no.; utility connections are dependent on the holding no.
Reliable sources have found RAJUK very reluctant to issue Certificate of Occupancy evenwhen it is properly applied for and when there is no code violation.
Time limit set in BNBC-2017 Part 2, Chapter 3 (15 days from time of application) needs tobe enforced for RAJUK to issue Certificate of Occupancy.


Building approval is obtained for a particular land use zone and use purpose, but then thedesign is changed, and the building is used for other purposes.
198

Along some streets, declared commercial, permission for change in use is routinely granted.

Elsewhere, applications are evaluated on a case by case basis and have to be referred to the Ministry.
This policy needs to be reexamined to avoid negative public perception.


A few accredited engineers and architects sign a disproportionate percentage of approved

drawings issued by RAJUK. It has been suggested that a limit be put on the number of jobs for which
an engineer or architect can sign the drawings in a year.
Enforcing the requirement to obtain a Certificate of Occupancy appears to be key to solving

this problem.
The key to eradication of corruption and a lack of transparency – perceived or real - in theapproval process of RAJUK is Electronic Construction Permitting, electronic record keeping, and soforth.
Every U.S. Building Department is basically divided into two parts: a part that does plan review bycertified individuals and the other part that does inspections by qualified individuals certified in areas
they inspect. Typically, structural and nonstructural plan checks are done by one group, while MEP
plan checks are done by another. Similarly, structural and nonstructural inspections are doe by one
group, while MEP inspections are done by another. RAJUK is already divided along the same lines.
RAJUK’s plan review, while reasonably rigorous, is confined to only architectural drawings.
Structural drawings, when submitted, are not checked because RAJUK does not have the needed
trained manpower. To what extent fire safety requirements are checked is not clear. In the UnitedStates, the setback requirements are based solely on fire safety considerations.
Table 1 Plan Review by RAJUK – Current Situation

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Table 2-2: Inspections by RAJUK – Current Situation

MODELS OF BUILDING CODE IMPLEMENTATION AND ENFORCEMENT
A number of other building code implementation and enforcement models used in a number of cities
and countries around the world are reviewed. While some jurisdictions were selected based on theirdemographic and cultural similarities with the areas covered by RAJUK, others were selected in thedeveloped and developing parts of the world, considering the seismic hazard.

INCENTIVES
Issuing an Occupancy Certificate to a code-compliant building will be one of the best tools for code
enforcement in Bangladesh. A code-compliant building with an Occupancy Certificate will enjoy thefollowing benefits: (i) A holding number from the City Corporation, which is necessary for multiple
purposes, (ii) Service or utility connections, (iii) Lower City Corporation taxes, (iv) Low interest rate
at banks, and (v) Higher rental in view of better and safer living, especially in the event of fire andearthquake incidents. The government can introduce these incentives to encourage construction ofcode-compliant buildings. On the other hand, RAJUK’s Authorized Officers should also be
empowered to impose severe penalties on non-compliant buildings, the provision for which exists in
the Building Construction Act of 1952.
In the relatively near future, it would be most desirable to develop and put into use a seismic rating
system for the Dhaka metropolitan area. The U.S. Resiliency Council’s system as well as the New
Zealand system can be consulted to come up with a system that is right for Bangladesh.

BNBC IPLEMENTATION AND ENFORCEMENT STRATEGY
What should be the future role of RAJUK needs to be visualized. Given RAJUK’s limitations interms of shortage of manpower and insufficiency of technical knowledge, it has become almost
inevitable for RAJUK to outsource certain activities, especially the vetting of building design. The
public’s perception of RAJUK was never favorable and has become considerably worse in the wake200

of recent fire related incidents and the loss of many valuable lives in Dhaka. People want change inthe time-worn procedural strategy of RAJUK. Dhaka City dwellers expect RAJUK to be a moretransparent, proactive and pro-people organization. The following concepts toward enhancing thelegal framework and competent resources of RAJUK should be considered for adoption:
1. RAJUK should focus primarily on strengthening its Development Control and Planningwings. The existing resources and the attention of RAJUK Management should be diverted
more towards Development Control activities, so that RAJUK can gradually transform into
more of a Regulatory Body. The main purpose for enacting the BC Act of 1952, which
eventually resulted in the creation of DIT and subsequently RAJUK, was to stop ‘haphazard
construction’ through activation of the Development Control wing of RAJUK. So thisdivision needs to be mobilized as fully as practicable.
2. Currently over 50% of the approved posts of Authorized Officers (AOs) are lying vacant.
This in itself defeats the purpose for which RAJUK was created. The vacant posts of
Authorised Officers should be filled up immediately, and AOs provided with logistical andfinancial resources to facilitate proper inspection of buildings ‘during construction. For filling
the posts of AOs, if Executive Engineers/Architects/Planners are not available in sufficientnumbers, then as a fall-back option, Assistant Engineers/Planners/Architects with 3-5 yearsof experience should be trained and posted to fill-up the vacant positions.
3. Consideration should be given to vesting all Authorized Officers of RAJUK with full
Magisterial Power to implement and enforce the Building Code. To ensure such enforcement,
RAJUK should also have its own police force or assured access to police force under thecontrol of another part of Government, to help the AO conduct Mobile Court and impose
penalties for code violations on the spot.
4. Considering RAJUK’s manpower situation, knowledge base, and public safety and welfare
needs, Tables 3 and 4 below are proposed as the concept for pre-construction checking ofstructural, non-structural and MEP drawings and for inspection during construction,
respectively. Plan checking involves RAJUK as well as 3rd-party plan checkers. Similarly,
inspections also involve RAJUK as well as 3rd party inspectors. Although it is true that, overtime, RAJUK has developed a pool of trained in-house Authorised Officers and Inspectors,
they still need to be trained elaborately and rigorously about BNBC-2017 provisions. This isbecause a fundamental aspect of the concept presented in Tables 3 and 4 is that, as aRegulatory Body, RAJUK will supervise and monitor the activities of 3rd Party plan checkers
and inspectors. It is primarily the 3rd-party plan checkers who will ensure that structural,
non-structural as well as MEP drawings are in full compliance with BNBC-2017. Similarly, it201

will mostly be the 3rd-party plan checkers who will supervise and monitor buildings underconstruction.
Table 3: Plan Review by RAJUK (Pre-Construction) – Recommendations

Table 4: Inspections by RAJUK (during Construction) – Recommendations

5. Issuance of the Certificate of Occupancy and post-construction inspections should be as
indicated in Table 5.

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Table 5: Issuance of Certificate of Occupancy and Post-Construction Inspections

6. The 3rd-party Inspectors should have the legal mandate to inspect buildings underconstruction. If access is denied or other resistance develops, the 3rd-party inspectors being
non-government persons, the 3rd Party Inspectors should be entitled to seek and receive help
from the concerned Authorised Officers who will be in a position to use the powers vested in
him or her, if need be.
7. The best and probably the only way to implement a fast, transparent and pro-people permitprocessing procedure is to introduce the ‘Electronic Construction Permitting’ (ECP) System.
Development of ECP is currently underway by the S-7 Component of RAJUK’s URP. In
addition, Fast Automated Structural Plan Check (FASP) and Fast Remote Control Method(FRCM) should also be in place. They may be used as initial screening and flagging tools,
although they will not provide sufficient check for plan approval.
8. The recruitment and accreditation of 3rd-party firms and professionals should be done understrict policy similar to that practiced in the U.S. or other developed countries. RAJUK
engineers, architects and planners will be trained to monitor the 3rd parties before renewing
their license of accreditation. The license of accreditation of a 3rd party provider can becancelled (or withheld) and the architects, engineers, planners and Inspectors can be blacklisted in case they are found to have been involved in any malpractice. Depending upon thenature of violation, the 3rd-party firm along with the individuals can also be barred fromtaking part in any future contract under RAJUK.

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PART-XIVCYCLONE BULBUL, 9th NOVEMBER, 2019
BANGLADESH NETWORK OFFICE FOR URBANSAFETY (BNUS), BUET, DHAKA

Prepared By: Maruf Billah
Mehedi Ahmed Ansary

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BackgroundA severe cyclonic storm Bulbul, hit Bangladesh on 9 November, 2019 through Khulna division.
Maximum sustained wind speed within 64 km of the severe cyclone center was about 100 kph rising
to 120 kph in gusts/squalls according to the Met office. The coastal areas of Bangladesh receiver
heavy rainfall prior to the cyclone’s arrival which continued as the cyclone passed. The cyclone
crossed Khulna coast around 5am on Sunday with a heavy precipitation, flooding low-lying areas in
the district. The low-lying areas of the coastal districts including Chattogram, Noakhali, Laxmipur,
Feni, Chandpur, Barguna, Bhola, Patuakhali, Barishal, Pirojpur, Jhalakati, Bagherhat, Khulna,
Satkhira and their offshore islands and chars are likely to be inundated by storm surge of 5-7 feetheight above normal astronomical tide, said Abdul Hamid, a meteorologist of Bangladesh
Meteorological Department. Among these 14 districts, Barisal, Sathkhira and Khulna seem to havebeen affected the most.
Local offices of the Department of Agricultural Extension (DAE) in 12 districts told different
newspapers that more than 250,000 hectares of cultivable lands, where farmers were growing paddy,
mustard, beetle leaf, vegetables and fruits, have been damaged. In Sathkhira, some 25,000 hectaresof paddy was fully or partially damaged, while 2,000 hectares of vegetables, beetle leaf, mustard andplums were damaged as well. In Khulna, 25,000 hectares of Aman paddy land and 1,092 hectares offruits and vegetable land were partially or fully damaged. In Barisal, Aman and Boro paddy in
100,000 hectares of lands were affected, while 6,000 hectares of mustard and 2, 000 hectares ofpulses were partially or fully damaged. Besides, some 100,000 small and large trees were uprooted
or felled during the storm. Croplands were fully or partially damaged in Madaripur, Bhola, Jessore,
Barguna and Bagerhat as well.
Director of Crops Wing at the DAE, (Abdur Razzak) told Dhaka Tribune that they had yet to receive
reports about the state of the damage caused by Bulbul from the affected districts. About 150,150
houses were damaged in Satkhira, Khulna, Barisal, Bagerhat, Madaripur, Chandpur, Barguna,Gopalganj, and Jhalakathi. Besides, 151 schools were partially or fully damaged. Some 5,017 fish
farms in Satkhira, 160 fish and shrimp farms in Barguna and 435 fish farms in Barisal weredamaged. Besides, 120 km of road in Barisal and 38.5km of embankment in Barguna were damaged
as well.
Over 2 million people were evacuated to more than 5,500 cyclone centers in 14 districts according tocountry’s media reports (The Daily Star, 10/11/2019). According to the Department of Disaster
Management, government allocated 4300 metric ton of rice, 14,000 dry food packages, children foodworth BDT 900,000, animal food worth BDT 900,000 and total cash grant of BDT 18,500,000 in 16
districts heavily affected by the cyclone.
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The cyclone damaged households, agricultural lands, destroyed plantation and disrupted
communication. According to SOS forms published by the DC, DRRO, the worst hit districts
included Satkhira, Khulna, Barguna and Bagerhat. The cyclone also claimed lives of 17 individuals.
Overview of Bulbul Impact
SOS forms reveal that the cyclone hit coastal areas and heavily damaged the district of Shatkhirawhere water logging become the most prominent and visible damage. Other districts that sufferedless severe damage included Potuakhali, Bagerhat, Bhola, Borguna and Khulna. So far the cyclonic
destruction resulted in the loss of seventeen lives, out of which eleven people died from tress fallingon them. The cyclone also damaged houses, crops, fish enclosures and embankments. (The Daily
Star, 11/11/2019).(Due to discrepancies in data found in NDRCC reports, this note has been
prepared primarily using SOS forms signed by DC, DRRO from respective districts, Information isstill being updated so it is difficult to comprehend the extent of damage),
Table 01: Affected people in Different District.
District

Number of people affected

Shatkhira

226,000

Khulna2,97,500

Bagerhat (Morrelganj and Rampal

135,859

Upazila)Borguna

12,225

Total

671,584

Source: (SOS Forms signed by DC, DRRO from respective districts).

Damages of Shelter
Damage to houses have been most visible in the districts of Shatkhira and Khulna, with Shatkhirafacing more damage. SOS forms reveal the extent of damaged houses suffered in these areas:

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Figure 01: Pictorial representation of some partial and fully damaged houses.

Table 02: Damages of shelter in respected districts
District

Damage to Shelter
Partially damaged houses

Fully damaged houses

Shatkhira33,63016,580Khulna37,8209,455Bagerhat (Morrelganj and10,764826

2025

Non reported

Rampal Upazila)
Borguna

Source: (SOS Forms signed by DC, DRRO from respective districts).

Impacted area of Cyclone Bulbul on coastal regions of Bangladesh
Extent of damages – overview
Human lives and injuries
According to DGHS data, 15 were injured due to the storm. At least 13 people have died due to theimpact of cyclonic storm Bulbul in Bangladesh, However, the official estimates of Directorate
General of Health Services (DGHS) confirmed 8 people were killed so far and at least 55 others
injured207

Figure 02: Impact of Cyclone Bulbul on coastal regions of Bangladesh.
Table 03: Number of Death in respected districts.
Sl. NoDistrict01

Khulna02

02

Patuakhali

01

03

Bagerhat

01

04

Bhola

01

05

Barguna

01

06

Gopalganj

02

07

Shariatpur

01

08

Barishal

01

09

Pirojpur

01

10

Satkhira

02

Total

13

208

Casualty Reported

Food security and Livelihood
In Sathkhira, 25,000 hectares of Aman paddy, 1,200 hectares of vegetable crops and 6,000 shrimp
enclosures have been destroyed. In Bagerhat, the cyclone severely damaged Aman paddy on 5,000
hectares of land along with farmlands of winter vegetables. In Khulna, 750 fish enclosures were
washed away (RCO Flash Update 2, 11/11/2019). According to information from DC office ofBorguna 9,863 hectares of agricultural land, 550 hectares of vegetation land and approximately 50
hectares of fish enclosures have been damaged. The monetary loss suffered by aquaculture amount to
BDT 10,300,000. Damage to agriculture affects income of farmers as well as laborers who work on
the agricultural land, especially in Sathkhira and Bagerhat where high rates of engagement inagriculture (HIES 2016) is present. Also if there is salinity intrusion due to tidal surge agriculture
might be severely affecting for the next growing season due to decreased soil fertility.
Health
So far seventeen individuals have been reported dead due to the cyclone with falling trees being the
major cause (The Daily Star, 11/11/2019). Moreover, inundation of water from heavy rain heavy rain
can become a breeding ground for spreading water borne diseases. Salinity intrusion is a major risk
since fish enclosures have been washed away. Additionally, as mentioned in the previous section,
submerged latrines can also impose health risks, especially on women.
Electricity
Power supply snapped due to uprooting of electricity poles, damage to substations and transmission
lines.

Figure 03: Fully and partial damage of electricity poles by cyclone Bulbul.
Governance infrastructures
Special Circuit House Khulna, Office and Residence of SP and Collector have been badly damaged
like many other buildings.

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Communications
Huge numbers of trees have been uprooted resulting in disruption of road communication. Sathkhira
and Khulna city have been severely affected. Telecom towers have been affected resulting in cellular
and telephone network down in wide area. All telephone and cell phones are down in 14 district.
Telephone and mobile connectivity has also been severely affected in Sathkhira including Barisal
city.

Figure 04: Communication hampered by tree blockage.

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