Adriano, B. et al.

Paper:

Scenarios of Earthquake and Tsunami Damage Probability
in Callao Region, Peru Using Tsunami Fragility Functions
Bruno Adriano∗1 , Erick Mas∗2 , Shunichi Koshimura∗2 , Miguel Estrada∗3 ,and Cesar Jimenez∗4,∗5∗1 Graduate School of Engineering, Tohoku University
Aoba 468-1-E301, Aramaki, Aoba-ku, Sendai 980-0845, Japan
E-mail: adriano@geoinfo.civil.tohoku.ac.jp∗2 International Research Institute of Disaster Science (IRIDeS), Tohoku University, Japan∗3 Japan-Peru Center for Earthquake Engineering and Disaster Mitigation (CISMID),
National University of Engineering, Lima, Perú
∗4 Dirección de Hidrografı́a y Navegación, DHN, Callao, Perú
∗5 Fenlab, Universidad Nacional Mayor de San Marcos (UNMSM), Lima, Perú
[Received July 1, 2014; accepted September 6, 2014]

The implementation of adequate urban development and measures systems against tsunami impact
in coastal communities is improved by understanding damage probability among building structures.
Within the framework of the project Enhancement ofEarthquake and Tsunami Disaster Mitigation Technology in Peru (JST-JICA SATREPS), the authors analyze the damage probability of building structuresdue to tsunami impact in the Callao region of Peru.
Two different tsunami hazard scenarios are assumed
in assessing building damage probability. The firsttsunami scenario represents the worse-case scenario
of tsunami inundation that calculates the envelop of
maximum inundation depth and flow velocity values
from 12 probabilistic megathrust earthquake scenarios for central Peru. The second tsunami scenario corresponds to a historical tsunami event in this region.
We apply a methodology for evaluating different levels of building damage by combining tsunami numerical results and tsunami fragility functions. Damage
probability was analyzed in detail on a single building
scale in the La Punta district. For the rest of Callao
region, analysis was performed on a block-unit scale.
Our results suggest that approximately 30% of submerged building may be washed away by tsunami inundation in the probabilistic hazard scenario and approximately 60% in the historical hazard scenario.
Keywords: damage probability assessment, tsunami
fragility function, tsunami inundation scenarios in central
Peru.

1. Introduction(Mw 8.1), 1970 (Mw 7.9), and 1974 (Mw 8.1). Based on
the literature [1, 2], these events significantly structurally
damaged coastal infrastructures and took human lives. Aneven more gigantic event, however, considered one of themost catastrophic earthquakes and tsunamis disaster in
Peru’s history, occurred in 1746 (Mw 8.6-Mw8.8) off thecentral coast [3, 4]. The study presented in [5] reported
that ground shaking and subsequent tsunami inundation
completely destroyed Callao port. In short, it has beenover 250 years since the last megaearthquake and 40 years
since the last significant seismic activity in this area. It isthus clear that there has been an absence of earthquakes
and tsunamis of large or considerable magnitude in thesurroundings of the central Peru coast. Given the seismic
history of Callao region in Central Peru, it thus importantto take into account the high possibility of the occurrenceof a catastrophic seismic event accompanied by an enormous tsunami.
The evaluation of building damages from tsunami impact is used as a starting point for an effective tsunami
risk-reduction program [7]. Understanding damage probability among structures in vulnerable coastal areas may
improve the implementation of measure against tsunamis
and tsunami hazard planning. The authors estimate thedamage probability levels to buildings in the Callao region of central Peru. We conduct our analysis based on
two hazard scenarios. The first represents the worsecase scenario for tsunami inundation that calculates theenvelop of maximum inundation values from 12 probabilistic megathrust earthquake scenarios [8–10]. The second scenario corresponds to the estimated source of the1746 historical tsunami event [3]. To evaluate different
building damage levels, we introduce a novel methodology by combining tsunami numerical results and empirical tsunami fragility functions.

In Peru’s modern seismic history, the three largest,
most recent earthquakes of considerable magnitude occurred in the central coastal region of Peru in 1966
968

Journal of Disaster Research Vol.9 No.6, 2014

Scenarios of Earthquake and Tsunami Damage Probability
in Callao Region, Peru Using Tsunami Fragility Functions(a)Photo 211°58'S

Photo 4

1 Km

Photo 312°0'S

0

Photo 177°9'W

Quincha (wood) Masonry

AirportRC

Photo 3

Photo 412°2'S

Expansion

Photo 212°4'S

Photo 1
La Punta

Fig. 1. (a) View of the study area. The red solid-polygon shows part of the area corresponding to the runway of theInternational Airport Jorge Chavez. The red dashed line shows the projected development of airport facilities [6]. Photos 14 show major urban features in the study area. Approximate photo locations are shown in (a).

2. Description of the Study Area
This study covers the central Peruvian coast corresponding to the Callao region (Fig. 1(a)). Based on landuse, the study area is classified into three main sectors.
The first is located in the southern part of the study area
below 12◦ 02 30 and corresponds to built-up urban areas.
This area is thus the most densely populated area in Callao
region (Photos 1-2 in Fig. 1). The La Punta district, an
small peninsula, is also located in this sector (Fig. 1(a)).
The second land use sector is between 12◦ 01 00 and12◦ 02 30 and is mainly covered by agricultural fields(Photo 3 in Fig. 1). Part of the International Airport Jorge
Chavez runway is located inside this sector, as shown by
the red solid polygon in Fig. 1(a). Note that a large areain this sector is reserved for future expansion of airport
facilities [6], as shown by the red dashed line in Fig. 1(a).
There are also several urban communities placed along
the coastline.
The third sector is in the northern part of the study area
and is mainly occupied by large factories and, to the north,by urban communities (Photo 4 in Fig. 1).

2.1. Building Dataset
This study uses two building dataset. A building-unit
scale dataset used for the La Punta district contains contains information on the construction material type andnumber of stories. A block-unit scale dataset constructedby the Peruvian Institute of Statistic and Informatics(INEI) [11] is used for the rest of the study area. Each
Journal of Disaster Research Vol.9 No.6, 2014

block-unit has information on the total number of buildings classified by construction material. In this study, bothdataset are integrated to form a uniform data file. Basedon the construction material, the INEI classified building
into six groups for the material used in building walls (Table 1). Photo 1 in Fig. 1 shows an example of three typesof buildings, i.e., quincha wood, masonry, and reinforced
concrete (RC). The distribution of building type throughout the study area is shown in Fig. 2, where it is to be
noted that most residential houses in the study area areconstructed of brick masonry. Buildings are reclassified
into three main groups as follows:• Masonry: This type of construction uses brick walls

confined by vertical RC tie columns. Brick masonrybuildings are typically one or two stories (Photo 4 inFig. 1). Adobe masonry structures made from sundried bricks are included in this group.
• Wood: This construction uses wood. A similar ma-

terial, called Quincha, consist of wood put together
with cane as a frame covered in mud. These are
traditional and older structures in the area, most being part of the cultural heritage. An example of thishousing type is shown in Photo 1 in Fig. 1.
• RC: This group basically uses RC wall or a RC frame(column bean) as main structural component.
• Others:

This group includes structures built using
only steel frames or combined with RC. Large factories and warehouses in the north part of the study
area predominate in this group.
969

Adriano, B. et al.

Table 1. Classification of construction material based on the
INEI [11] and equivalent classification used in this study.
INEI’s classification
Brick
Adobe
Wood
QuinchaRC
Others77°9'0"W(a)Material (in this study)
Masonry
Wood
RC
OthersMaterial
11°58'0"S

Brick
Adobe
Wood
QuinchaRC
OthersMasonry
Wood
RC
Others
AirportExpansion

12°0'0"S

AirportExpansion

0

2 Km

12°4'0"S

12°2'0"S

12°2'0"S

12°0'0"S

11°58'0"S

INEI

12°4'0"S

77°9'0"W

(b)

0

2 Km

Fig. 2. Building classification in the study area based on
construction material. (a) Material distribution based on the
INEI [11]. (b) Equivalent classification used in this study.

3. Tsunami Hazard Scenarios
Hazard scenarios used to assess damage probability
in Callao region are defined below. In previous studies,
tsunami hazards were presented represented by a set of inundation maps mainly calculated through numerical calculations assuming a historical seismic event in the regionof interest as an earthquake source [12–16]. In the case ofPeru, previous studies evaluating tsunami vulnerability inthe study area have used either empirical equations or numerical calculations to estimate tsunami hazards in termsof maximum tsunami height [17, 18]. Most recently,
within the framework of the Japan-Peru JICA-SATREPS
project, a more realistic tsunami hazard has been introduced for the central region of the Peru coast [19]. Adriano et. al [19] estimated a set of detailed inundation maps,
and evaluated potential human casualties due to tsunami
970

flooding [20, 21]. To evaluate the probability of building
damage, we define two different tsunami hazard scenarios in this study in terms of maximum tsunami inundation
depth and flow velocity. The first tsunami hazard (Case 1)
scenario represents the worse-case scenario of tsunami
impact that calculates the envelop of maximum tsunami
inundation depth and flow velocity from 12 probabilistic megathrust earthquake sources in central Peru [8–10].
The second tsunami hazard (Case 2) scenario corresponds
to numerical results for a historical tsunami event considered to have been one of the most catastrophic seismic
events in Peru’s history [3]. The tsunami hazard scenarios
are shown in Fig. 3.
Figure 3 shows the significant difference between hazard scenarios, which is clearly observed by comparing
the inundation area in both cases (Figs. 3(a)-(b)). In thecase of inundation depth, Case 1 has areas approximately
8 m inundation depth that are mostly concentrated in thenorthern sector of the study area, and areas up to 6 m inundation depth in the southern sector where most urban
areas are largely located.
Conversely, Case 2 inundation depth values are almost
twice the height of those in Case 1. Case 2 has 16 m depth
in the northern sector and 12 m depth in the southern sector.
Regarding tsunami flow velocity, both case scenarios
reach values approximately over 9 m/s. It is also clear,
however, that Case 2 presents more areas where flow
velocity is higher than Case 1 presents (Figs. 3(c)-(d)).
These areas are mostly concentrated in the center of thestudy area and are mainly covered by agricultural fields.
An important fact in both scenarios is that the tsunami
flood reaches part of the area destined for future expansion of the International Airport Jorge Chavez [6]. The
flooded area in Case 2 also extents to part of the actual
runway zone of the international airport (Figs. 3(b) and(d)).

4. Tsunami Vulnerability Assessment
The 2011 Tohoku tsunami disaster demonstrated thedestructive power of tsunami inundation features incoastal infrastructures [22–25]. Recent studies havedemonstrated that damage to coastal structures follows
a dynamic component that depends on a number of parameters. The Papathoma Tsunami Vulnerability Assessment (PTVA) model, for example, was developed to assess building vulnerability to tsunami impact, and considers the coastal setting for identifying and ranking a series of attributes such as building use, construction material, and spatial location, for estimating tsunami damage
to buildings [12, 26, 27]. In another approach, the work
presented in [28], which uses a large database of building
damage from the 2011 Japan tsunami, identifies a set ofvariables, i.e., inundation depth, structural material, andbuilding function, that may be correlated with the level ofbuilding damage.

Journal of Disaster Research Vol.9 No.6, 2014

Scenarios of Earthquake and Tsunami Damage Probability
in Callao Region, Peru Using Tsunami Fragility Functions

(b)

(d)

77°9’0"W

Case 2Max. Inund.
Depth (m)
0-2
2-4
4-6
6-8
8 - 10
10 - 1212 - 14
14 - 16

Max. Flow
Veloc. (m/s)
0-2
2-3
3-4
4-5
5-6
6-7
7-8
8-9

Max. Flow
Veloc. (m/s)
0-2
2-3
3-4
4-5
5-6
6-7
7-8
8 - 1211°58’0"S

Case 111°58’0"S

Case 2

2 Km

0

2 Km

12°2’0"S

12°2’0"S

12°2’0"S

0

0

2 Km

12°4’0"S

12°4’0"S

77°9’0"Wb)

12°2’0"Sb)(c)

12°0’0"S12°0’0"S

11°58’0"SMax. Inund.
Depth (m)
0-2
2-4
4-6
6-8
8 - 10
10 - 1212 - 14
14 - 1612°4’0"S12°0’0"S

11°58’0"S

Case 177°9’0"W

12°0’0"S

77°9’0"W

12°4’0"S

(a)

0

2 Km

Fig. 3. Maximum tsunami inundation depth and flow velocity for the first ((a) and (c)) and the second ((b) and (d)) tsunami hazard
scenario. (a) and (b) Maximum tsunami inundation depth. (c) and (d) Maximum tsunami flow velocity.

Table 2. Classification levels of building damage used in this study, categorized by MLIT. The illustrations
were obtained and modified from [28].
DamageDescription

Condition

Minor

No significant structural or
non-structural damages

Possible to be use immediately

Moderate

Slight damages tostructural components

non-Possible to be use after
moderate reparations

Major

Significant damages to somewalls but no damages incolumns

Possible to be use after
complete reparations andretrofitting

Complete

Significant damages to several
walls (more that half of wall
density) and several columns

Possible to be use after major reparations

Collapse

Destructive damage to walls
and some columns

Lost of functionality. Nonrepairable or great cost of
retrofitting

Whaled away

Washed away, only foundation
remained, total overturned

Non-repairable, requires
total reconstruction

Journal of Disaster Research Vol.9 No.6, 2014

Illustration

971

Adriano, B. et al.

Fig. 4. Tsunami fragility curves based on structural material and damage levels. (a) RC, (b) masonry, (c) wood and(d) mixed-material buildings.

4.1. Application of Tsunami Fragility Functions
Tsunami fragility functions define the probability ofstructural damage due to tsunami inundation features,e.g., inundation depth and flow velocity, and are assumed
to be cumulative probability P of damage occurrence(Eq. (1))

ln x − μ P(x) = Φ. . . . . . . . . . (1)σwhere Φ is lognormal distribution function, x is inundation depth, μ  and σ  are mean and standard deviation ofln x. Statistic parameters of fragility function (μ  and σ  )are obtained by plotting ln x and the inverse of Φ−1 inlognormal probability papers, and least-square-fitting thisplot [29, 30].
Currently, several curves have been developed following major tsunami disasters for different site conditions.
In the case of the Indian Ocean tsunami of 2004, forinstance, fragility functions were developed for Banda
Aceh, Indonesia [30], and Phang Nga, Thailand for RC
buildings [31]. Following the 2010 Chilean tsunami in
Dichato, Chile, for mixed material structures [32]. After the 2011 Tohoku tsunami with much more detail sur972

veyed data [33–35]. Considering the engineering featuresof building and site conditions, tsunami fragility functions are used to evaluate the damage probability of buildings using different tsunami hazard scenarios. Pioneering
applications of tsunami fragility functions, in combination with numerical models, were presented at [36, 37].
These studies presented a preliminary straightforwardapplication of tsunami fragility for evaluating the damage probability of buildings within the inundated zone.
Furthermore, Adriano et. al [38] extended and presented
two different approaches for evaluating the probability ofbuilding damage using tsunami fragility curves.
In order to estimate the probability of building damage,
we use tsunami fragility functions constructed by [34].
Suppasri et. al [34] used over 250,000 structures surveyedfrom Chiba to Aomori by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan. Specific curves were constructed based on the damage level,
construction material, and number of stories. The six levels of damage to buildings adopted in [34], defined by
the MLIT, are also used in this study. These levels andtheir descriptions are presented in Table 2. The probability of building damage is based on the four construction materials shown in Table 1. Fig. 4 shows the set of
Journal of Disaster Research Vol.9 No.6, 2014

Scenarios of Earthquake and Tsunami Damage Probability
in Callao Region, Peru Using Tsunami Fragility Functions

Table 3. Parameters for construction of tsunami fragility functions used in this study, according to [34].

Minor
Moderate
Major
Complete
Collapse
Washed away

RC
μ
σ
-1.9636 1.0966-0.9723 1.0600
0.1577 0.7090
0.9423 0.7522
1.9381 1.0120
2.8232 0.9635

Masonryμσ-2.1130 1.3362-1.1573 1.0400
0.1059 0.7693
0.9043 0.5746
1.1918 0.6821
1.6583 0.6913

fragility functions for different structural materials. These
functions give a maximum classification of damage probability based on tsunami inundation depth. Note that RC
structures have better performance than wood or masonrybuildings. The parameter to construct the fragility curves
are presented in Table 3.

4.2. Building Damage Estimation
We describe the methodology used to evaluate damage
probability for buildings based on the damage level defined in Table 2. It is important to point out, however,that construction quality and building standards in central
Peru are basically different from those in Japan, so thesame load conditions may generate differences in building performance. Curves used in this study also include
the contribution of ground shaking in the final damage
stage. Nevertheless, considering the large database using
in [34], recent fragility curves developed from the 2011
Tohoku event are the most appropriate to be applied inour study.
In a quantitative macroscale estimation of damage, thenumber of submerged buildings is first counted by using
an analysis interval of 0.5 m for the inundation depth.
Second, using a representative inundation value for eachinterval (mean value), the damage probability is estimatedusing respective fragility curves for individual construction material types. Third, the probable number of damage buildings in each interval for different construction
materials is calculated by multiplying the number of submerged buildings by the probability damage or ratio ofbuildings damaged within the interval. Fourth, based on
threshold values for building damage presented in [39],which are based on the material type and tsunami flow
velocity acting on each structure, a realistic number of affected buildings is estimated for each damage level at eachinterval of inundation depth. Fifth, the probable damagelevel for each building structure and block-unit is assigned
to the higher level of estimated damage for each structure.
4.3. Tsunami Vulnerability Mapping
Table 4 lists the total number and the percentage ofprobable affected buildings for the six modeled damagelevels in each case scenario. The total number of submerged buildings are 1,753 and 2,114 for the Case 1 andthe Case 2, respectively. In the Case 1, the percentage of
washed away and survivor buildings are almost similar.
Journal of Disaster Research Vol.9 No.6, 2014

Wood
μ
σ
-2.1216 1.2261-0.9338 0.9144-0.0400 0.7276
0.6721 0.4985
0.7825 0.5559
1.2094 0.5247

Othersμσ-2.4562 1.4874-1.1373 1.1150-0.0756 0.8277
0.5316 0.6235
0.8336 0.6077
1.2244 0.5723

Table 4. Total number and percentage of probable damagedbuilding for each damage level in the two hazard scenarios.

Washed away
Collapse
Complete
Major
Moderate
Minor
Survivor

Case 1# Build.
%
569
32.5
254
14.5
142
8.1
124
7.1
28
1.6
2
0.1
634
36.2

Case 2# Build.
%
1315
62.2
257
12.2
128
6.1
63
3.0
16
0.8
0
0.0
335
15.8

In the Case 2 the percentage of washed away buildings(> 60%) are significantly greater than the other damagelevels. Comparing both cases, the percentage of washedaway building is greater than the other damages levels.
Figure 5 shows the distribution of building damage foreach hazard scenario. The extent of the inundation area
is shown by the solid red line. Based on Fig. 5, buildings of the washed away level are largely concentrated in
the central and northern sectors of the study area. Fig. 6
shows the distribution of building damage in the La Punta
district for each hazard scenario. Case 1 shows that thewashed away state of damage probability is mostly concentrated in the surroundings of the district (Fig. 6(a)). In
Case 2, washed away buildings are found throughout thewhole district (Fig. 6(b)). An important point is regarding the designed evacuation building in this district, there
are 19 buildings designed for vertical evacuation in caseof tsunami [40], as shown by the white circle in Fig. 6. In
Case 1, there are 3 buildings that present a washed away
state of damage probability and 2 that present a complete
or major state of damage probability. In Case 2, 6 buildings present a washed away state of damage probability
and 2 present a collapsed and complete state of damage
probability.

5. Conclusions
In this paper, we have presented an estimation of building damage for the Callao region of Peru based on tsunami
fragility curves and tsunami numerical simulation. The
damage level to buildings was estimated based on the construction material type. This study presents the first timethat tsunami fragility curves were applied to the Callao
973

Adriano, B. et al.

77°9'0"W(b)(a)

12°4'15"SWashed away
Collapse
Complete
Major
Moderate
Survivor

077°10'0"W

77°9'40"W

190 m

12°4'15"S

77°9'40"W0

2 Km

Fig. 5. Building damage assessment maps. (a) and (b)shows the probable damage level for the first and the second scenario. The solid solid red line shows the inundation
limit of each scenario.

region for estimating the damage level to structures. We
used the six states of damage probability defined by the
MLIT. Two tsunami hazard scenarios were assumed inassessing the damage probability of buildings. The firsttsunami scenario represents the worse-case scenario of
tsunami inundation that calculates the envelop of maximum inundation depth and flow velocity values from12 probabilistic megathrust earthquake scenarios in central Peru. The second tsunami scenario corresponds toa historical tsunami event in this region. We provided amethodology for applying fragility curves for estimatingbuilding damage on a community scale.
Damage probability results show that in the case of theprobabilistic hazard scenario, approximately 30% of allbuildings in the inundation area may be washed away. In
the case of the historical hazard scenario, however, over
60% of submerged buildings had a washed away state ofdamage probability.
Vulnerability maps are presented in this paper to support building damage assessment and land use planning inthe study area.
Acknowledgements
This study was carried out within the framework of the SATREPS
project, Enhancement of Earthquake and Tsunami Disaster Mitigation Technology in Peru, sponsored by the Japan International Cooperation Agency and the Japan Science and Technology
Agency. We deeply appreciate support of the Ministry of Education, Culture, Sports, Science and Technology; Tohoku Univer-

12°4'30"S

12°4'15"S

12°2'0"S

2 Km

12°4'0"S

12°4'0"S

12°2'0"S(b)

77°10'0"W

190 m

12°4'30"SInund. limit

12°4'30"S

AirportExpansion

Inund. limit

974

77°9'40"W

12°4'15"S11°58'0"S

AirportExpansion

077°10'0"W

Case 2

Washed away
Collapse
Complete
Major
Moderate
Minor
Survivor

12°0'0"S

12°0'0"S

11°58'0"S

Case 177°9'0"W

12°4'30"S(a)

077°10'0"W

Washed away
Collapse

Complete
Major

77°9'40"W

Moderate
Minor

Survivor

Fig. 6. Building damage level in the La Punta district. (a)and (b) shows the probable damage level for the first andthe second scenario. White circles show the location of thedesigned evacuation building in the La Punta district.

sity; National Research Institute for Earth Science and Disaster
Prevention; and the International Research Institute of Disaster
Science.
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[18] PNUD/SDP-052/2009, “Investigación sobre el PELIGRO DETSUNAMI en el Área Metropolitana de Lima y Callao,”in
SISTEMA DE INFORMACIÓN SOBRE RECURSOS
PARA ATENCIÓN DE DESASTRES, COOPERAZIONE INTERNAZIONALE ? COOPI, 2010.
[19] B. Adriano, E. Mas, S. Koshimura, Y. Fujii, S. Yauri, C. Jimenez,and H. Yanagisawa, “Tsunami inundation mapping in Lima, for two
tsunami source scenarios,” Journal of Disaster Research, Vol.8,
No.2, pp. 274-284, 2013.
[20] S. Koshimura, T. Katada, H. O. Mofjeld, and Y. Kawata, “A method
for estimating casualties due to the tsunami inundation flow,” Natural Hazards, Vol.39, pp. 265-274, Oct. 2006.
[21] A. Muhari, F. Imamura, S. Koshimura, and J. Post, “Examinationof three practical run-up models for assessing tsunami impact onhighly populated areas,” Natural Hazards and Earth System Science, Vol.11, pp. 3107-3123, Dec. 2011.
[22] N. Mori, T. Takahashi, and The 2011 Tohoku Earthquake Tsunami
Joint Survey Group, “Nationwide post event survey and analysisof the 2011 Tohoku Earthquake Tsunami,” Coastal Engineering
Journal, Vol.54, No.1, pp. 1250001-1, 2012.
[23] H. Gokon and S. Koshimura, “Mapping of Building Damage of the2011 Tohoku Earthquake Tsunami in Miyagi Prefecture,” Coastal
Engineering Journal, Vol.54, No.1, pp. 1250006-1, 2012.
[24] A. Suppasri, S. Koshimura, K. Imai, E. Mas, H. Gokon, A. Muhari,and F. Imamura, “Damage Characteristic and Field Survey of the2011 Great East Japan Tsunami in Miyagi Prefecture,” Coastal Engineering Journal, Vol.54, No.1, pp. 1250005-1, 2012.
[25] A. Suppasri, N. Shuto, F. Imamura, S. Koshimura, E. Mas, and
A. C. Yalciner, “Lessons Learned from the 2011 Great East Japan
Tsunami: Performance of Tsunami Countermeasures, Coastal
Buildings, and Tsunami Evacuation in Japan,” Pure and AppliedGeophysics, July 2012.
[26] M. Papathoma and D. Dominey-Howes, “Tsunami vulnerability assessment and its implications for coastal hazard analysis and disaster management planning, Gulf of Corinth, Greece,” Natural Hazards and Earth System Science, Vol.3, No.6, pp. 733-747, 2003.
[27] F. Dall’Osso, M. Gonella, G. Gabbianelli, G. Withycombe, and
D. Dominey-Howes, “A revised (PTVA) model for assessing thevulnerability of buildings to tsunami damage,” Natural Hazards
and Earth System Science, Vol.9, pp. 1557-1565, Sept. 2009.
[28] N. Leelawat, A. Suppasri, I. Charvet, and F. Imamura, “Building
damage from the 2011 Great East Japan tsunami: quantitative assessment of influential factors,” Natural Hazards, Feb. 2014.
[29] S. Koshimura, Y. Namegaya, and H. Yanagisawa, “Tsunami
fragility: a new measure to identify tsunami damage,” Journal ofDisaster Research, Vol.4, No.6, pp. 479-488, 2009.

Journal of Disaster Research Vol.9 No.6, 2014[30] S. Koshimura, T. Oie, H. Yanagisawa, and F. Imamura, “Developing fragility functions for tsunami damage estimation using numerical model and post-tsunami data from Banda Aceh, Indonesia,”
Coastal Engineering Journal, Vol.51, pp. 243-273, Sept. 2009.
[31] A. Suppasri, S. Koshimura, and F. Imamura, “Developing tsunami
fragility curves based on the satellite remote sensing and the numerical modeling of the 2004 Indian Ocean tsunami in Thailand,”Natural Hazards and Earth System Science, Vol.11, pp. 173-189,
Jan. 2011.
[32] E. Mas, S. Koshimura, A. Suppasri, M. Matsuoka, M. Matsuyama,
T. Yoshii, C. Jimenez, F. Yamazaki, and F. Imamura, “Developing
Tsunami fragility curves using remote sensing and survey data of
the 2010 Chilean Tsunami in Dichato,” Natural Hazards and Earth
System Science, Vol.12, pp. 2689-2697, Aug. 2012.
[33] A. Suppasri, E. Mas, S. Koshimura, K. Imai, K. Harada, and F. Imamura, “Developing tsunami fragility curves from the surveyed dataof the 2011 great east Japan Tsunami in Sendai and Ishinomaki
plains,” Coastal Engineering Journal, Vol.54, No.1, pp. 12500081-1250008-1, 2012.
[34] A. Suppasri, E. Mas, I. Charvet, R. Gunasekera, K. Imai, Y. Fukutani, Y. Abe, and F. Imamura, “Building damage characteristics
based on surveyed data and fragility curves of the 2011 Great East
Japan tsunami,” Natural Hazards, Vol.66, pp. 319-341, Nov. 2012.
[35] I. Charvet, I. Ioannou, T. Rossetto, A. Suppasri, and F. Imamura,“Empirical fragility assessment of buildings affected by the 2011
Great East Japan tsunami using improved statistical models,” Natural Hazards, Mar. 2014.
[36] B. Adriano, E. Mas, S. Koshimura, and Y. Fujii, “Remote Sensingbased Assessment of Tsunami Vulnerability in the coastal area of
Lima , Peru,” in The 10th International Workshop on Remote Sensing for Disaster Management, Japan, pp. CD-ROM, 2012.
[37] B. Adriano, E. Mas, S. Koshimura, and Y. Fujii, “Tsunami Vulnerability Assessment of Buildings Using Remote Sensing Analysis and Numerical Modeling in Lima, Peru,” in Proceedings ofInternational Sessions in Coastal Engineering, JSCE, Vol.4, Vol.4,
pp. CD-ROM, 2013.
[38] B. Adriano, E. Mas, and S. Koshimura, “Application of tsunami
fragility functions for building damage assessment : two different
approaches,” in The APRU symposium series on Multi-hazards
around the Pacific Rim, pp. CD-ROM, 2013.
[39] S. Hayashi, B. Adriano, E. Mas, and S. Koshimura, “Study oftsunami run-up model considering building failure,” in Annual
Meeting of the Tohoku Branch Technology Research Conference,
Japan Society of Civil Engineers. Hachinohe, Japan., pp. CD-ROM,
Tohoku University, 2014.
[40] E. Mas, B. Adriano, and S. Koshimura, “An Integrated Simulationof Tsunami Hazard and Human Evacuation in La Punta , Peru,”
Journal of Disaster Research, Vol.8, No.2, pp. 285-295, 2012.

Name:
Bruno Adriano

Affiliation:
Ph.D. Student, Graduate School of Engineering,Tohoku University

Address:
Aoba 468-1-E301, Aramaki, Aoba-ku, Sendai 980-0845, Japan

Brief Career:
2012-2013 Research student, ReGiD, IRIDeS, Tohoku University, Japan
2013- PhD Student, Graduate School of Engineering, Tohoku University

Selected Publications:• B. Adriano, E. Mas, S. Koshimura, and Y. Fujii, “Tsunami Vulnerability
Assessment of Buildings Using Remote Sensing Analysis and Numerical
Modeling in Lima, Peru,” in Proceedings of International Sessions in
Coastal Engineering, JSCE, Vol.4, No.4, CD-ROM, 2013.
• B. Adriano, E. Mas, S. Koshimura, Y. Fujii, S. Yauri, C. Jimenez, and H.
Yanagisawa, “Tsunami inundation mapping in Lima, for two tsunami
source scenarios,” J. of Disaster Research, Vol.8, No.2, pp. 274-284, 2013.

Academic Societies & Scientific Organizations:• Japan Society of Civil Engineers (JSCE)• Japan Geoscience Union (JpGU)• Institute of Electrical and Electronics Engineers (IEEE)

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