Journal of Natural Disaster Science, Volume 38，Number 2，2017，pp145-164

Development and Application of a Building Group Destruction Probability
Model Based on the Great East Japan Earthquake Tsunami Experience
Ryoichi Yanagawa**Department of Civil Engineering, National Institute of Technology, Kagawa College(Received: Dec.20, 2016

Accepted: Sep.13, 2017)

Abstract
This study examined the regional spatial characteristics of tsunami flooding and building damage
using geographic information systems. An analytical model that evaluates total building destruction
risk was developed using building damage data from coastal areas in Iwate Prefecture. Building
density characteristics in the study area were categorized into four types of environments: (1) many
isolated buildings, (2) combination of isolated and neighboring buildings, (3) combination ofneighboring and surrounding buildings, and (4) many neighboring buildings. Many isolatedbuildings were located along the narrow, low-lying areas facing the Pacific Ocean. In comparison,higher building densities were observed along the inner part of the enclosed gulf topography. Most
buildings located near the shoreline collapsed. Farther inland, a higher percentage of buildings
experienced half-collapse or no-damage. Closer inspection of the varying spatial distribution
characteristics and resultant building damage among the 27 target areas led to the identification ofseveral key indicators for predicted building damage including the structure, use, and density of theaffected building and the extent of tsunami inundation. Based on the building damage
characteristics following the 2011 tsunami, a building group destruction probability model wasdeveloped and verified. The proposed model successfully estimated building collapse ratios using
the available data.
Keywords: Great East Japan Earthquake, Tsunami, Building damage, Damage estimation, Iwate
Prefecture
1. Introduction
The Great East Japan Earthquake (GEJE) occurred on March 11, 2011. The Japan Weather
Association located the epicenter of the earthquake at 38.103°N latitude and 142.860°E longitude.
Its moment magnitude reached 9.0 and its maximum seismic intensity reached 7 in Kurihara City in
Miyagi Prefecture (Japan Meteorological Agency, 2012). The tsunami caused by the GEJE also hadsignificant impacts. In Iwate Prefecture, a maximum run-up height of 39.71 m was directly
observed during the 2011 tsunami (Mori et al., 2012), whereas results from geographic information
system (GIS) analyses estimated a maximum run-up height of 49.8 ± 0.2 m (Yanagawa et al., 2016).
On March 11, 2014, Japan’s National Police Agency confirmed 18,517 dead and missing persons
within 12 prefectural and city government jurisdictions as a result of the tsunami caused by the
GEJE and its aftershocks. Iwate Prefecture was especially impacted with more than 6,000 dead andmissing persons and 23,000 collapsed homes. More than 45,000 buildings in Iwate Prefecture wereinundated by the tsunami (Yanagawa et al., 2014a), and various reports noted that most buildings
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were flooded or extremely damaged (Arikawa, 2012; Hiraishi, 2012; Inukai et al., 2012; Kawasaki,
2012; Suppasri et al., 2012; Suzuki et al., 2012). In addition, various purpose-developed analyticaltechniques were applied to quantitatively clarify the extent of building damage, which could only
previously be ascertained qualitatively (Charvet et al., 2014; Gokon et al., 2016; Koshimura et al.,2012a; Koshimura et al., 2012b; Macabuag et al., 2016; Narita et al., 2015). Used as an indicator ofhouse damage, Figure 1 shows Hatori’s destruction ratio (Hatori, 1964) measured at 24 different
coastal locations. Kuji Bay in the north and Miyako Bay in the middle had relatively low Hatori
destruction ratios of 56.3% and 71.2%, respectively. Each of the other areas had significantly higher
Hatori destruction ratios. Among them, 15 areas had ratios over 90%, indicating that most or all of
the buildings were washed away or completely destroyed (Yanagawa et al., 2014b).

Fig. 1. Building damage in 24 regional coastal areas characterized
by Hatori’s destruction ratio
The extent of building damage is likely to be affected by a number of different factors
related to coastal morphology (e.g., elevation, bay size, existence of coastal protection facilities);
building features (e.g., shoreline setback, number of stories, structure type, building density); andtsunami wave characteristics (e.g., height, velocity, power, dominant direction, overtopping heightof facilities). TABLE 1 provides a partial list of potential explanatory factors affecting building
damage. Of these factors, the building structure type, the building density, and the extent of tsunami
inundation are most readily available for measurement. The development of an evaluation method
based on such readily accessible information is considered useful when conducting tsunami risk
assessment. Related research was carried out by Narita et al. (2015), who presented a tsunami
fragility curve corresponding to the topographical features by utilizing principal component analysis
and cluster analysis. However, their study only focused on severe tsunami damage (washed away),
and their consideration of cases with less damage was insufficient. Charvet et al. (2014) andMacabuag et al. (2016) created a fragility curve using various target areas, explanatory variables,
and analytical methods, but they did not conduct damage analysis in the coastal area of Iwate
Prefecture.

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Journal of Natural Disaster Science, Volume 38，Number 2，2017，pp145-164

TABLE 1. Potential factors affecting tsunami-related building damage

TsunamiBuilding

Geography

Items

Common collection
methodPlace to obtainin Japan

Difficulty ofobtaining

Elevation on land

Data download

Public agency

Easy

Depth at sea

Data download

Public agency

Easy

Size of the bay

Spatial analysis

Individual

DifficultShoreline length

Spatial analysis

Individual

DifficultState of the land

Site reconnaissanceIndividual

Difficult

Coastal protection facility

Document
Site reconnaissance

Public agency, Local municipality

Easy

Distance to the shoreline

Spatial analysis

Individual

Easy

Number of stories

Data download
Site reconnaissance

Public agency, Local municipality
Individual

Easy

Structure type

Data download
Site reconnaissance

Public agency, Local municipality
Individual

Easy

Density

Site reconnaissanceIndividual

Easy

Wave height

Numerical model

Public agency, Local municipality

Easy

Inundation area

Numerical model
Site reconnaissance

Public agency, Local municipality

Easy

Wave velocity

Numerical model

Public agency

Difficult

Wave power

Numerical model

Public agency

Difficult

Wave direction

Numerical model

Public agency

Difficult

Overtopping height at the facility

Numerical model

Public agency

Difficult

In this study, regional spatial characteristics regarding the extent of tsunami inundation andbuilding damage caused by the tsunami were examined using GIS. Additionally, an analyticalmodel that evaluates total building destruction risk was developed using building damage data from
Iwate Prefecture.
2. Data Collection and Methods

For each of the 27 regional areas on the Iwate coast, data regarding the building structure type,building density, extent of tsunami inundation (i.e., spatial distribution and height), and resultant
building damage were overlaid in a GIS within a computational mesh. The Geospatial Information
Authority of Japan (2012) provided elevation data after the GEJE as a lattice-shaped mesh with aspatial resolution of approximately 5 m (0.2 seconds in scale). Elevation data for each building
considered in this study were applied using data from the nearest location.
Tsunami inundation and building damage characteristics in each area were examined. The
maximum tsunami inundation for each building was calculated, and the relationship between
destruction types and building features was considered. Based on these findings, a probability
model was developed to predict the total building destruction risk for a target building using thethree indicators: building structure type, building density, and extent of tsunami inundation. The
accuracy of the model was then validated in the Iwate coastal area.

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2.1 Building Structure Type
Building characteristics, including the structure type, were obtained from a disaster survey
conducted by Japan’s Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) in 2011.
Specifically, this disaster survey recorded the building location (in latitude and longitude), buildinguse (residential, apartment complex, public institution, plant, commercial building, barn, etc.), andbuilding structure (reinforced concrete, steel, wood, and other/unknown). These data were used in
the analysis of this study.
2.2 Building Density
The building density was defined as the number of buildings within a 20-m radius from the centerof the target building. The site area per house was approximately 250 to 300 m2 (one side isapproximately 16 to 17 m long), and the straight distance to the adjacent building wasapproximately within 20 m; thus, the threshold was set at 20 m. In the coastal area of Iwate
Prefecture, there were eight or more buildings within a 20-m radius zone from the target in densely
populated residential areas. Similarly, two to four buildings were located in areas that wererelatively larger spaced. The relationship between the density expressed by this method and thedenseness actually experienced was qualitatively good. A building density of zero indicates no otherbuildings within the target radius; a building density of 10 indicates 10 buildings within the target
radius. Based on these direct building densities, three general building density categories were
defined: (1) isolated with no buildings around the target, (2) neighboring with 1–3 buildings aroundthe target, and (3) surrounding with ≥4 buildings around the target. Figure 2 depicts the stepwisebuilding density estimation procedure used in this study. This process concurrently consideredbuilding structure and use.

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Journal of Natural Disaster Science, Volume 38，Number 2，2017，pp145-164

Fig. 2. Building density estimation procedure: 1 enter building information in GIS, 2 set bufferzone radius to 20 m, 3 count building numbers within each buffer zone
2.3 Extent of Tsunami Inundation
The numerical model was used to estimate the maximum tsunami inundation area and height byfocusing on the 20 coastal areas within the study area and by using information provided by the
Iwate Prefecture Tsunami Disaster Prevention Technical Committee (Iwate Prefecture 2011–2012).
Figure 3 shows the 20 coastal areas that were used to support the calculations. TABLE 2 contains
the specifications of the tsunami inundation numerical model.

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Fig. 3. Tsunami inundation area calculation domains (designated by a rectangular border);numbers in the figure indicate the 27 target areas in TABLE 3 and TABLE 4
TABLE 2. Numerical model configuration for 2011 Tohoku tsunami by Iwate Prefecture
Item
Calculation Area

Basic Equation andSolving Method
Calculation Gridand Resolution
Roughness Coefficientat the Ground
Seismic Slip Model
Topography
Sea Surface Deformationby the Earthquake
Calculation Period
Time Resolution
Coastal Protection
Facilities

Setting
Name of Bay, Coast
Affiliated City in Iwate Prefecture
Hiranai Coast, Yagi Port
Hirono Town
Kuji Bay
Kuji City
Noda Coast
Noda Village
Urube Coast
Fudai Village
Shimanokoshi Coast
Tanohata Village
Omoto Coast
Iwaizumi Town
Taro Coast, Osawa Coast, Miyako Bay, Omoe Coast Miyako City
Yamada Bay
Yamada Town
Otsuchi Bay
Otsuchi Town
Ryoishi Bay, Kamaishi Bay, Touni Bay
Kamaishi City
Yoshihama Bay, Okirai Bay, Ryori Bay, Ofunato Bay Ofunato City
Ohno Bay, Hirota Bay
Rikuzentakata City
Based on the Non-Linear Long-Wave equation
Application of staggered leap-frog method
Overflow discharge formula for tsunami overflowing by Honma (1940),
3240m, 1080m, 360m and 120m without tsunami run-up
40m, 20m and 10m with tsunami run-up
Grid nesting calculation for multiple areas
Reference by Kotani et al. (1998)
Sea, River: 0.025, Agricultural field: 0.050, Forest field: 0.030
Residential area: 0.040 (low density), 0.060 (medium density), 0.080 (high density)
Fujii and Satake’s slip model (ver.4.0) by Fujii et al. (2011)
Modified 1994 Iwate Tsunami Prediction Model (Before the earthquake) or Survey data
in 2011-2012 (After the earthquake)
Manshinha & Smylie’s Method (1971)
Scale factor was calibrated by 1.05-2.90 times from the comparison between estimate value
and field survey
3 hours from the earthquake outbreak
0.1-0.4 sec, dependent on the calculation area
All of the facilities (Tide wall, River embankment, road embankment) are installed
and remaining after the tsunami.

The accuracy of the model was evaluated by utilizing the values of K and κ in Aida’s
method (Aida, 1984). The K value indicates consistency between the calculated and observed
values, a value of 1.0 reflects identical calculated and observed values, whereas values of 0.8–1.2
reflect highly accurate calculated results. The value of κ indicates the degree of variation; a value
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Journal of Natural Disaster Science, Volume 38，Number 2，2017，pp145-164≤1.6 is generally appropriate. In this study, the values of K were in the range 0.97–1.21, and thevalues of κ were in the range 1.00–1.38. These values suggest that the numerical model provides
highly accurate estimates that sufficiently reproduce the observed conditions and that it waspossible to proceed with subsequent steps of the study.
The tsunami inundation depth was calculated as the difference between the maximum
tsunami height and the elevation for each computational mesh in the GIS. Figure 4 depicts the
stepwise process for estimating the tsunami inundation depth. Regional inundation characteristics
were subsequently analyzed.

Fig. 4. Tsunami inundation mapping procedure(inundation depth = tsunami height - ground level)
2.4 Building Damage
To support development of the predictive model, building damage characteristics were also
obtained from the disaster survey conducted by Japan’s MLIT in 2011. In this disaster survey, seven
general categories of damage were defined: (1) collapse_washout, (2) collapse_removal, (3)collapse_renewable, (4) half-collapse_extensive damage, (5) half-collapse_flooded above floor, (6)no-damage_flood under floor, and (7) no-damage_avoid physical damage. In the current study,
three categories of damage we consolidated for the sake of simplicity: (1) collapse, (2) half-collapse,and (3) no-damage. Collapse includes washout, removal, and extensive destruction. Half-collapse
includes flooding above the floor. No-damage includes flooding below the floor.
3 Results and Discussion
TABLE 3 summarizes the number of affected buildings, the predominant building structure type,the building density, the tsunami inundation depth, and the predominant building damage
characteristics for each of the 27 regional coastal areas considered in this study. Notable regional
differences and interdependencies among these factors are discussed below.
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R.YANAGAWATABLE 3. Tsunami inundation and building characteristics in the 27 regional coastal areas(RC: reinforced concrete; S: steel; W: wood; O/U: others or unknown; Col: collapse; washout/removal/crucial damage; hCol:
half-collapse; flooded above floor or extensive damage; and no: no-damage; flooded under floor or less damage)Area
ID

Target area

1
2
3
4
5

Hiranai Coast
Yagi Port
Kuji Bay
Noda Coast
Urube Coast
Shimanokoshi
Coast
Omoto CoastTaro Coast
Osawa Coast
Miyako Bay
Omoe Coast
North Yamada Bay
Yamada Bay
North Funakoshi
Bay
South Funakoshi
Bay

6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27

Major
Regional
Name
Taneichi
Yagi
Kuji
Noda
OtanabeBuilding structure
Building density
Number[%][%]offlooded Reinforced
Neigh- SurrounOther/
Isolated
Steel Woodbuildings concrete
boringding
Unknown
55
124
1,207
1,055
64

12.7
0.0
3.4
2.1
7.8

9.1
5.6
9.1
0.9
14.1

69.1
90.3
63.9
80.9
67.2

Extent of damage[%]

Dominant combination of the building
structure & damage, and each ratio [%]W-hCol:32.9

W-Col:16.3

W-Col:50.2

W-hCol:25.6

W-Col:56.3

W-hCol:10.9

Inundation
depth [m]Most3frequentband
RC-Col:10.9
5-6
S-Col:5.6
3-4
W-No:14.7
1-2
O/U-Col:12.3
1-2
O/U-Col:10.9
7-9

Col

hCol

no

O/U

1

2

27.3 0.0
22.6 1.6
52.4 16.7
30.0 5.3
20.3 0.0

0.0
0.0
0.0
0.1
0.0

W-Col:49.1

W-hCol:20.0

W-Col:66.9

W-hCol:21.8

9.1
4.0
23.6
16.2
10.9

45.5
49.2
20.1
14.1
26.6

43.6
43.5
59.8
70.3
71.9

10.9
7.3
20.0
15.6
1.6

72.7
75.8
30.9
64.5
79.7

Shimanokoshi

230

4.8

1.3

88.3

5.7

10.9

65.7

23.5

97.8

2.2

0.0

0.0 W-Col:86.5

O/U -Col:5.7

RC-Col:4.3

9-10

Omoto
Taro
Sakiyama
MiyakoOmoe, Otobe
Chikei
Yamada

385
1,019
80
5,252
120
50
3,736

1.8
2.2
4.1
2.9
0.8
2.0
2.0

1.6
3.0
6.1
7.2
4.2
0.0
5.8

52.7
91.0
79.6
82.3
87.5
98.0
90.6

43.9
3.8
10.2
7.6
7.5
0.0
1.7

8.0
14.5
67.3
10.7
65.8
80.0
8.8

41.0
62.3
32.7
52.0
34.2
20.0
52.8

50.9
23.3
0.0
37.3
0.0
0.0
38.5

77.5
91.6
81.6
53.2
95.8
98.0
87.3

21.7
6.8
13.3
37.3
3.3
2.0
10.7

0.8
1.6
5.1
9.5
0.8
0.0
2.0

0.0
0.0
0.0
0.0
0.0
0.0
0.0

O/U -Col:40.0

W-Col:36.4

W-hCol:15.7

W-Col:84.4

W-hCol:5.3

O/U-Col:3.7

Funakoshi

731

1.9

2.3

90.4

5.3

11.6

57.9

30.5

93.8

5.1

Kirikiri

703

2.3

1.4

13.5

82.8

17.2

62.2

20.6

93.5

6,246

1.7

2.6

17.7

78.1

11.4

53.6

35.0

470
2,449
634
36
814
17
461
5,338
1,400
28
7,155

1.7
12.2
1.1
0.0
1.1
0.0
2.0
2.9
1.1
0.0
1.9

2.8
16.0
3.3
2.8
4.7
11.8
4.3
10.0
3.6
0.0
3.1

5.1
29.7
24.0
83.3
82.2
52.9
72.7
71.9
69.6
21.4
49.7

90.4
42.1
71.6
13.9
12.0
35.3
21.0
15.2
25.7
78.6
45.3

13.4
9.8
25.7
58.3
18.1
82.4
16.1
11.6
11.6
32.1
10.4

37.0
38.7
55.8
41.7
65.2
17.6
67.7
58.1
64.0
67.9
51.9

Otsuchi,
Otsuchi Bay
Unosumai
Ryoishi Bay
Ryoishi
Kamaishi Bay
Kamaishi
Touni Bay
Kojirahama
Yoshihama Bay
YoshihamaOkirai Bay
OkiraiRyori Bay
Nonomae
Outer Ofunato Bay Ryori
Ofunato Bay
OfunatoOhno Bay
MassakiOuter Hirota Bay Hirota
Hirota Bay
Rikuzentakata

152

W-Col:62.2

W-hCol:12.2

O/U-Col:10.2

W-Col:46.4

W-hCol:28.3

W-No:7.6

W-Col:84.2

O/U -Col:7.5

S-Col:3.3

W-Col:96.0

W-hCol:2.0

RC-Col:2.0

W-Col:78.9

W-hCol:9.8

S-Col:5.3

3-4
7-8
4-5
1-2
8-9
9-10
4-5

1.1

0.0 W-Col:84.8

O/U -Col:5.1

W-hCol:4.5

11-12

5.3

1.3

0.0 O/U -Col:82.5 W-Col:7.4

W-hCol:5.1

8-9

86.4 13.0

0.6

0.0 O/U -Col:77.5 W-hCol:12.1

W-Col:5.1

7-8

49.6 96.4 3.4
51.6 83.7 14.5
18.5 89.4 9.3
0.0 94.4 5.6
16.7 87.2 11.1
0.0 100.0 0.0
16.3 77.9 17.8
30.3 70.0 25.3
24.4 87.4 10.3
0.0 96.4 3.6
37.7 95.2 2.6

0.2
1.8
1.3
0.0
1.0
0.0
3.3
3.7
1.1
0.0
0.2

0.0
0.0
0.0
0.0
0.7
0.0
1.1
0.9
1.1
0.0
2.0

17-18
5-6
4-5
10-11
4-5
8-9
2-3
4-5
9-10
6-7
9-10

O/U -Col:90.4

S-Col:2.8

W-hCol:2.8

O/U -Col:41.0

W-Col:21.5

S-Col:12.3

O/U -Col:70.8

W-Col:14.5

W-hCol:8.2

W-Col:80.6

O/U -Col:11.1

S-Col:2.8

W-Col:72.9

O/U -Col:10.0 W-hCol:8.1

W-Col:52.9

O/U -Col:35.3 S-Col:11.8

W-Col:55.7

O/U-Col:17.8

W-Col:13.0

W-Col:51.5

W-hCol:16.9

O/U-Col:10.0

W-Col:58.9

O/U -Col:24.1 W-hCol:9.0

O/U -Col:78.6

W-Col:17.9

W-Col:47.0

O/U -Col:43.4 S-Col:3.0

W-hCol:3.6

Journal of Natural Disaster Science, Volume 38，Number 2，2017，pp145-164

3.1 Building Structure Type and Damage by Region
Among the 27 coastal areas considered, the percentage of wooden building structures exceeded
80% on Noda Coast, Shimanokoshi Coast, Miyako Bay, Omoe Coast, Yoshihama Bay, and Okirai
Bay and 90% in Yagi Port, Taro Coast, North Yamada Bay, Yamada Bay, and North Funakoshi Bay.
The percentage of reinforced concrete (RC) and steel building structures exceeded 20% in only
three coastal areas—Hiranai Coast, Urube Coast, and Kamaishi Bay. Most of the buildings along
the Iwate coast were wooden structures, and most of the wooden buildings or those built withunknown materials were completely destroyed by the tsunami. At South Funakoshi Bay, Otsuchi
Bay, Ryoishi Bay, Touni Bay, and Outer Hirota Bay, more than 70% of the building structures werecategorized as other/unknown. A review of historic aerial photos and satellite images suggests thatmost of the unknown building structures were in fact wooden structures.
Building damage was minor in only a few regions; in Kuji Bay and Miyako Bay, 16.7% and9.5% of building structures experienced minor flooding below the floor, respectively. The
percentage of building structures avoiding serious damage in the whole of Iwate, including the areasof Kuji Bay and Miyako Bay, was only 2.2%. Thus, most building structures in the study area
experienced more than minor damage.
3.2 Building Density by Region
Building density characteristics in the study area were categorized into four types of environments:(1) many isolated buildings (observed on Osawa Coast, Omoe Coast, North Yamada Bay,
Yoshihama Bay, and Ryori Bay); (2) combined isolated and neighboring buildings (observed on
Hiranai Coast and Yagi Port); (3) combined neighboring and surrounding buildings (observed on
Omoto Coast, Miyako Bay, Yamada Bay, North Funakoshi Bay, Otsuchi Bay, Ryoishi Bay,
Kamaishi Bay, Ofunato Bay, and Hirota Bay); and (4) many neighboring buildings (observed in
Kuji Bay, Noda Coast, Urube Coast, Shimanokoshi Coast, Taro Coast, South Funakoshi Bay, Touni
Bay, Okirai Bay, Outer Ofunato Bay, Ohno Bay, and Outer Hirota Bay). Many isolated buildings
were located along the narrow, low-lying areas facing the Pacific Ocean. In comparison, higher
building densities were observed farther away along the inner part of the enclosed gulf topography.
3.3 Tsunami Inundation and Building Damage by Region
Near the shoreline, most buildings experienced collapse. Farther inland, the percentage of buildingsthat experienced half-collapse or no-damage generally increased. Considering the spatial
distribution of two coastal areas in detail, many buildings in Kuji Bay were categorized as
half-collapse and no-damage along the flooded seafront as well as in the city center. In comparison,nearly all buildings in Hirota Bay, which is located within 1.5 km of the shoreline, were completely
destroyed or carried away by the tsunami outflow. Figure 5 maps the estimated building damage
and tsunami inundation depth for these two coastal areas.

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Fig. 5. Building structure type, tsunami inundation depth, and building damage distribution for Kuji
Bay (Kuji city) and Hirota Bay (Rikuzentakata city)
In Kuji’s city center, 79.5% of the building structures experienced a tsunami inundation
depth ≤ 2 m. The tsunami inundation depth was generally higher in Rikuzentakata’s city center;
45.8% of the building structures were flooded with 9–11 m of water. Figure 6 shows therelationship between the tsunami inundation depth and building damage. When the tsunami
inundation depth exceeded 2 m, the rate of total destruction or collapse ratio of wooden building
structures in both regions significantly increased and the rate of no-damage significantly decreased.

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Journal of Natural Disaster Science, Volume 38，Number 2，2017，pp145-164

Fig. 6. Comparison of tsunami inundation depth, number of buildings, and their damage for Kuji
Bay and Hirota Bay. RC building structure: mainly commercial buildings, schools, city halls, public
facilities. Steel building structure: strong high-rise (commercial) and low-rise (housing). Woodenbuildings: mainly individual houses and warehouses.
An investigation of tsunami damage by Shuto(1992) found that inundation depths exceeding
1 m and 2 m lead to partial and total destruction, respectively. Koshimura and Gokon (2012a)
proposed a tsunami fragility function intended for the buildings affected by the 2011 tsunami in
Miyagi Prefecture. They found that the runoff ratio increased rapidly when the inundation depth
reached 2 m and approached 80% when the inundation depth reached 6 m. The results from priortsunami damage investigations are consistent with the results of this study—when the tsunami
inundation depth exceeded 2 m, many wooden building structures experienced significant
damage—although a direct comparison cannot be made because the affected buildings consideredpreviously differ from those considered in this study with respect to architectural standards.
Regarding RC and steel building structures, the extent of damage to RC buildings was
generally lower than the extent of damage to steel buildings for the same tsunami inundation depth.
Despite these noted trends, the determination of a definitive relationship between tsunami
inundation depth and building damage was challenged by the limited number of sample cases, thediversity in building designs, the confounding effects of neighboring building structures, and theextent of density conditions.
3.4 Relationship among Inundation Depth and Building Structure Type, Density, and Damage
Based on a damage survey for each building structure within the study area, the collapse ratio at atsunami inundation depth of 0–1 m was 14.0% for RC structures, 30.1% for steel structures, and34.2% for wooden structures. The magnitude of the damage differed among these building structure
types at the same inundation depths. Many of the affected buildings were damaged beyond repair.
Figure 7 shows the collapse ratio as an interaction of the tsunami inundation depth and building
density for the three building structure types.

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Fig. 7. Relationship among tsunami inundation depth and building structure type, density, anddamage at Iwate coast, where “Number of surrounding buildings” signifies the building density,“Inundation depth” is the water height estimated by the tsunami numerical model, and “Collapse
ratio” is the proportion of buildings completely destroyed amongthe number of flooded buildings,
evaluated according to the three parameters.
For RC building structures and a tsunami inundation depth ≤ 2 m, the collapse ratio in
isolated (no surrounding buildings) building density environments was 4.3%, whereas the collapse
ratios in neighboring (1–3 nearby buildings) and surrounding (≥ 4 nearby buildings) building
density environments were higher. As the tsunami inundation depth increased, the collapse ratios foreach building density environment increased until all differences between building density
environments were indistinguishable. Isolated RC buildings generally included robust structures
used as communal facilities, plants, or high-rise buildings. In comparison, surrounding buildings
generally included weaker residential structures with higher associated collapse ratios.
Similar to the RC building structures, the collapse ratio for steel building structures inisolated building density environments was lower than the collapse ratios in neighboring andsurrounding building density environments for a tsunami inundation depth ≤ 2 m. When thetsunami inundation depth exceeded 2 m, the collapse ratios again increased sharply. Isolated steelbuildings were generally used as commercial, industrial, agricultural, and public facilities. In
contrast, surrounding steel buildings were generally intended for residential use.
For wooden building structures and a tsunami inundation depth ≤ 2 m, the collapse ratios
were 42.7%, 37.2%, and 30.3% for isolated, neighboring, and surrounding building density
environments, respectively. As the building density increased, the collapse ratios decreased. Whenthe tsunami inundation reached 4 m, the collapse ratio for all building density types reached nearly
70%. Wooden building structures were generally used as residences or simple barns for storing
fishery equipment. Isolated wooden buildings in coastal areas are directly affected by tsunamis andwere severely damaged; however, higher building density areas (e.g., collective housing) were morecapable of withstanding the power of the tsunami. Hence, differences in the building structure type,use, and density collectively affect the extent of building damage.

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3.5 Building Group Destruction Probability Model Application
In terms of the potential factors related to the geography in TABLE 1, all of the items except for"Coastal Protection Facility" and "Distance to the shoreline" affect the wave height or inundation
depth. Because the items "Coastal Protection Facility" and "Distance to the shoreline" did not
directly affect buildings, it was thought that they influence the magnitude of building damage as asecondary effect. Regarding potential factors related to the building itself, all of the items directly
affected the building resilience to the tsunami. All of the wave parameters expressing the tsunami
characteristics were considered to be representable mainly by the depth and range of the inundation.
The above items were adopted for the following three reasons: data pertaining to the tsunami
simulation results of Iwate Prefecture made it impossible to obtain the velocity and tsunami wave
strength in the inundated area; in the process of model development, the goal was to make areasonable evaluation by combining items that can easily be obtained by ordinary people; items
related to the kinetic momentum typified by the tsunami force and flow velocity can be evaluatedindirectly to some extent from the inundation depth. However, as pointed out by Park et al., it isnecessary to evaluate the kinetic momentum to clarify the influence on a building more strictly(Park et al., 2013). In addition, buildings located in the lower inundation depth zone were greatly
influenced in that they experienced damage other than the inundation depth (for example, terrainsuch as agricultural fields and roads, the presence of walls and trees, and floating cars and houses);thus, evaluation at the lower depth zone has limitations. In order to conduct a more precise
evaluation, additional items could be examined and a high-precision numerical model developed. In
summarizing these items, the value of "Number of stories" was missing in these areas; hence, its
parameters could not be analyzed in this study. In addition, several items focusing on the coastline
were excluded from this study. As a result of summarizing the relationship between the building
damage characteristics and their factors (selected from the above items), the relationship shown in
Fig. 8 was obtained and named the “Building Group Destruction Probability Model.” Fig. 8 is an
integration of the empirically obtained on-site data shown in Fig. 7 and it is impossible to improve
its functionality because of missing data or unique building characteristics. Therefore, the data were
presented as a bar chart estimating the destruction probability with the combination of the following
three variables. Explanatory variables included the building structure type (RC, steel, and wood);building density (isolated, neighboring, and surrounding); and the tsunami inundation depth. Fig. 8
depicts the relationship between these explanatory variables and the collapse ratio, which was used
to indicate building damage. Because the inundation depth band included missing data above 12 m,
the estimated collapse ratio was assumed to be 100% based on proximate data.

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Fig. 8. Proposed building group destruction probability model using building structure type,building density, and tsunami inundation depth as explanatory variables
To test the application of the proposed model, coastal cities in Iwate Prefecture that had ˃
1,000 tsunami-affected buildings were considered. These cities included Kuji Bay, Noda Coast,
Taro Coast, Miyako Bay, Yamada Bay, Otsuchi Bay, Kamaishi Bay, Ofunato Bay, Ohno Bay, andHirota Bay, shown on the map in Fig. 9. TABLE 4 summarizes the reproducibility of the collapse
probability for each area and building structure. In the coastal cities that experienced lower tsunami
inundation depths (Kuji Bay, Noda Coast, and Miyako Bay), the collapse probability approaches
100% along the seafront, with the collapse probability declining near the run-up end. Comparing
Fig. 9 and Fig. 5 for Kuji Bay, the waterfront area of the tide wall contained a barn and a simple
facility for storing fishing equipment. These structures were completely destroyed by the tsunami,
and the estimated collapse probability exceeded 90%. The estimated collapse probability in theresidential densely populated areas was as low as 30–50% in the Natsui and the Osanai district
where houses were densely built around Kuji River. In this area, the actual damage was
half-collapse or flooded below the floor, and the estimated value reflected the local situation. In
addition, the damaged buildings inundated below the floor that were located on the inland side ofthe densely populated residential area were approximately 30% or less in this analysis.

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Fig. 9. Spatial distribution of estimated building collapse probability at major coastal cities in Iwate
Prefecture

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R.YANAGAWATABLE 4. Mean building collapse ratio reproducibility in the 27 regional coastal areas(Col = collapse, hCol = half-collapse, and no = no-damage)
Reinforced Concrete
Area
ID

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27

Estimated Mean
Collapse Ratio

Actual Mean
Collapse Ratio

78.3
68.3
92.9
100.0
94.0
50.0
96.9
100.0
74.3
100.0
40.0
82.2
85.4
89.3
92.6
98.0
84.6
86.7
85.0
77.5
67.3
82.1
96.8

85.7
29.3
63.6
100.0
90.9
22.2
64.0
100.0
39.0
100.0
100.0
85.1
92.9
93.8
95.2
62.5
72.8
85.7
66.7
30.8
70.4
87.5
95.7

Steel

Number oftarget buildings
with the exception ofdamage unknown
Col

hCol

6
0
12
14
5
10
2
16
4
60
1
1
63
13
15
99
5
217
6
0
6
0
4
107
14
0
132

1
0
17
7
0
1
7
6
0
73
0
0
11
1
1
5
3
80
1
0
3
0
5
38
2
0
6

Estimated Mean
Collapse Ratio

Actual Mean
Collapse Ratio

90.0
64.3
58.4
91.4
100.0
90.0
76.7
90.0
100.0
76.1
85.0
84.2
96.3
89.0
87.6
93.1
81.3
96.0
90.0
83.7
70.0
79.4
69.5
81.9
94.8

60.0
100.0
17.3
77.8
33.3
100.0
37.5
69.7
83.3
47.3
80.0
91.7
94.1
100.0
87.5
100.0
77.0
95.2
100.0
78.9
100.0
80.0
66.1
96.0
97.7

no
0
0
12
1
0
0
0
3
0
21
0
0
0
0
0
0
0
1
0
0
0
0
4
7
0
0
0

160

Wood
Number oftarget buildings
with the exception ofdamage unknown
Col

hCol

3
7
19
7
3
3
3
23
5
178
4
0
198
16
10
140
13
302
20
1
30
2
16
351
48
0
213

2
0
84
1
6
0
5
10
1
181
1
0
17
1
0
19
0
88
1
0
8
0
4
167
1
0
5

Estimated Mean
Collapse Ratio

Actual Mean
Collapse Ratio

81.1
67.8
57.0
65.2
97.5
91.9
67.3
94.4
88.7
76.6
89.7
86.9
85.2
87.1
66.3
71.8
82.0
79.5
78.8
84.8
83.1
93.3
76.7
70.9
84.4
80.0
93.9

71.1
74.1
25.6
62.2
83.7
98.0
69.1
92.7
78.2
56.4
96.2
98.0
87.2
93.8
54.7
28.8
41.7
72.4
60.5
96.7
89.3
100.0
77.6
72.2
85.7
83.3
95.5

no
0
0
7
1
0
0
0
0
0
17
0
0
1
0
0
1
0
2
0
0
0
0
0
13
1
0
0

Number oftarget buildings
with the exception ofdamage unknown
Col

hCol

no

27
83
197
530
36
199
181
939
61
2,437
101
48
2,949
620
52
318
10
527
92
29
593
9
257
2,747
825
5
3,362

11
27
397
270
7
4
78
59
12
1,484
3
1
365
33
36
755
13
168
52
1
66
0
60
902
126
1
152

0
2
177
52
0
0
3
15
5
401
1
0
69
8
7
31
1
33
8
0
5
0
14
158
12
0
8

Journal of Natural Disaster Science, Volume 38，Number 2，2017，pp145-164

On the other hand, in the areas where the depth of flooding closely behind the tide wall wasless than 2 m, the building damage was assumed to be low; but in reality, many wooden buildings
collapsed. There were few such cases compared to the total number of buildings included in thesample, but they were underestimated compared to building damage with the same tsunami
inundation depth, building density, and building structure. Furthermore, items other than theparameters presented in this paper may have a great influence. These results were qualitatively
consistent with the observation. In low-lying areas between the shoreline and the mountain foothills
that experience higher tsunami inundation depths (Taro Coast, Ohno Bay, and Hirota Bay), thecollapse probability was estimated to be higher, consistent with observation. When the
reproducibility in Hirota Bay (Fig. 5 and Fig. 9) was examined in detail, the proportion of total
destroyed buildings against the total number of buildings by structure was 95.7%, 97.7%, and95.5% in RC, Steel, and Wood, respectively. The mean collapse probability was 96.8%, 94.8%, and93.9%, respectively, which is similar (TABLE 4). In particular, the estimated collapse probability
for buildings that actually collapsed on ground ranging from flat to loose slopes was more than 90%,thus reproducing the actual situation. In sloped areas, the extent of damage decreased with anincrease in altitude, and the estimated collapse probability decreased from 80% to 30%. In thevicinity of the tsunami run-up end, where the altitude was higher, although buildings with low
reproducibility of the estimated damage ratio could be confirmed, it is suspected that the accuracyof the tsunami numerical simulation was attributed to it. Urube Coast, Shimanokoshi Coast, Taro
Coast, Omoe Coast, North Yamada Bay, North Funakoshi Bay, Ryoishi Bay, Yoshihama Bay, andOhno Bay have terrain similar to that of Hirota Bay and higher tsunami inundation depths. The
collapse probabilities in these regions were also estimated to be high. The estimated collapse
probabilities for Otsuchi Bay and Yamada Bay were more moderate. Beyond the tsunami run-up
end, building damage dramatically declined with an estimated collapse probability 40–80%. A
higher proportion of RC and steel building structures existed within the Kamaishi Bay and Ofunato
Bay tsunami inundation areas. Estimated collapse probabilities differed for RC and steel building
structures compared with wooden building structures. The proposed model estimated low collapse
probabilities for isolated RC buildings in relatively high tsunami inundation depth zones. Moreover,the proposed model estimated high collapse probabilities for densely arranged steel buildings inlower inundation depth zones. Both results are consistent with observations based on building
structure type, use, and density. Hence, the proposed empirical model can be qualitatively
confirmed to predict tsunami damage risk; building damage estimation can be further enhanced byconsidering the building arrangement and building group aspects.
To quantitatively assess the proposed model, areas with a higher proportion of woodenbuilding structures and higher associated collapse ratios offer greater reproducibility. Candidate
areas included Shimanokoshi Coast, Taro Coast, Omoe Coast, North Yamada Bay, North Funakoshi
Bay, Yoshihama Bay, Okirai Bay, Ryori Bay, and Rikuzentakata (Kuji Bay, Miyako Bay, Otsuchi
Bay, and Ryoishi Bay have lower wooden building proportions). The mean differences between theobserved and estimated collapse ratios for all target buildings were 8.2%, 5.9%, and 5.1% for RC,
steel, and wooden building structures, respectively.
Some geographical features challenged the performance of the model. The reproducibility ofbuilding damage was relatively low for the affected buildings behind the seawall in Kuji Bay,
Miyako Bay, in the vicinity of the tsunami run-up border in Ofunato Bay, Otsuchi Bay, and otherareas. Both the buoyancy generated by the increased tsunami inundation depth and the wave energy
applied to the seawall (proportional to the inundation depth magnitude) are considered to affect theestimation results. Behind the seawalls, wave energy proportional to the inundation depth
magnitude and kinetic energy induced by potential energy during tsunami overflow are assumed to161

R.YANAGAWA

act on the buildings. Thus, consideration of the maximum inundation depth alone may notadequately determine the energy applied to a building immediately behind the seawall. In areas near
the tsunami run-up border where inundation depths are shallow, the extent of flooding is affected by
the microtopography and the presence or absence of buildings. Referenced tsunami calculation
models were modified to account for the influence of buildings. These improvements to the tsunami
run-up model were expected to enhance the reproducibility near the tsunami run-up border and inshallow flooding regions.
The tsunami inundation depth can be estimated under a variety of scenarios using a
numerical model to obtain the building density. Therefore, these damage estimation methods andparameters can be used to assess disaster risk in other regions. Because this study focused on
conditions in the coastal areas of Iwate Prefecture with a high proportion of low-rise, woodenbuilding structures, application to urban areas comprising more high-rise buildings may require
additional research and review.
5 Conclusion
This study examined the regional spatial characteristics of flooding and building damage incurred
during the tsunami following the GEJE of 2011 using GIS and an analytical model. The model,
which evaluates total building destruction risk, was developed using building damage data from thecoastal areas of Iwate Prefecture. The following results were obtained:(1) As an overall trend, a higher proportion of collapsed buildings was observed near the
shoreline; in contrast, higher proportions of buildings that were partially collapsed or had nodamage were observed inland. A spatial survey of the difference between the building damagein two coastal areas revealed that a tsunami inundation depth of 9–11 m affected 45.8% of thebuildings in Hirota Bay, whereas a tsunami inundation depth of ≤ 2 m was found to affect79.5% of the buildings in Kuji Bay. Their unique topographical features were thought toaccount for the difference in damage.
(2) Collapse ratios differed based on the building structure type, use, density, and tsunami
inundation depth. Isolated RC and steel buildings experienced less damage than those inhigher density environments for tsunami inundation depths ≤ 2 m. In comparison, under thesame tsunami inundation depth conditions, isolated wooden buildings had a higher collapse
ratio than higher density environments.
(3) Based on the building characteristics in the coastal areas of Iwate Prefecture, the total building
destruction risk was examined and a predictive model was developed. This model was provencapable of estimating the building collapse ratio using the building structure type, building
density, and extent of tsunami inundation as explanatory variables.
This study represents an initial step in correlating tsunami and building damage
characteristics. To enhance these results, additional small-scale investigations considering thetsunami inundation depth, tsunami speed, detailed coastal geography, building distribution, among
other factors, should be conducted. New findings are expected to enable the development of a moresophisticated numerical model.

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Acknowledgements
Field survey data were provided by Japan’s Ministry of Land, Infrastructure, Transport, andTourism and the Geospatial Information Authority. The output of the numerical simulation modelfor the tsunami was provided by Iwate Prefecture. This work was supported by the Japan Society
for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) Program(Grant Number 15K20849) and the Takahashi Industrial and Economic Research Foundation.
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