Proceedings of the International Symposium on Engineering Lessons Learned fromthe 2011 Great East Japan Earthquake, March 1-4, 2012, Tokyo, JapanAPPLICATON OF SEAMLESS SIMULATION OF
SEISMIC RESPONSE ANALYSIS AND
HIGH RESOLUTION TSUNAMI SIMULATIONTO COASTAL AREA OF SENDAI
Kohei FUJITA1, Tsuyoshi ICHIMURA2, Muneo HORI3,Lalith WIJERATHNE4 and Seizo TANAKA5
1

Graduate Student, Department of Civil Engineering, University of Tokyo, Tokyo, Japan,
fujita@eri.u-tokyo.ac.jp
2
Associate Professor, Earthquake Research Institute, University of Tokyo, Tokyo, Japan,
ichimura@eri.u-tokyo.ac.jp
3
Professor, Earthquake Research Institute, University of Tokyo, Tokyo, Japan
hori@eri.u-tokyo.ac.jp
4
Assistant Professor, Global Center of Excellence for Sustainable Urban Regeneration,
University of Tokyo, Tokyo, Japan, lalith@eri.u-tokyo.ac.jp
5
Assistant Professor, Earthquake Research Institute, University of Tokyo, Tokyo, Japan,
stanaka@eri.u-tokyo.ac.jp

ABSTRACT: We perform a seamless simulation of Seismic Response Analysis (SRA)of structures and High Resolution Tsunami Simulation (HRTS) to estimate the overall
damage of a city under earthquake-tsunami hazard scenarios. We use a Common CityModel (CCM) converted from data stored in the geographic information system as inputsof both simulations. Results of SRA are reflected to HRTS by modifying the shape ofstructures if it is assumed to be collapsed. Application to coastal area of Sendai showsthat the tsunami flow changes according to the changes in CCM based on results of SRA.
Key Words: Integrated Natural Disaster Simulation, Seismic Response Analysis, High
Resolution Tsunami Simulation, Common City Model

INTRODUCTION
The 2011 Tohoku earthquake caused Strong Ground Motion (SGM) and tsunami in coastal areas ofnortheastern Japan, leading to a large number of fatalities and serious damage in coastal urban areas.
The Tokai, Tonankai, or Nankai earthquakes expected in southwestern Japan can cause a series ofsevere earthquake and tsunami hazards in coastal areas. To make appropriate mitigation strategies,
estimation of disasters under hazard scenarios with a series of hazards is needed.
As a candidate to simulate the overall damage of urban areas under natural disaster scenarios, theauthors have been developing an Integrated Natural Disaster Simulator (INDS); for example, see (Horiet al. 2008) and (Sobhaninejad et al. 2011). INDS is an integration of component simulations,540

simulating each phase of an urban disaster. Each component simulation uses physics-based
computational methods with input city models that reflect the actual properties of a city, constructedfrom data stored in the Geographic Information System (GIS). In the past, the main development ofINDS was targeted on earthquake disasters (Integrated Earthquake Simulator, IES). IES integrates
seismic wave propagation analysis from the fault to city, Seismic Response Analysis (SRA) ofstructures using the computed SGM, and simulation of evacuation processes using Multi AgentSimulation.
In this paper, we integrate SRA and High Resolution Tsunami Simulation (HRTS). We aim tosimulate damage and collapse of structures, since it is essential to ensure human life space. For SRA,
we use non-linear structural analysis methods on each structure in the target area. For tsunami
simulation, we use HRTS, which is a tsunami simulation using a three-dimensional analysis method
aimed to analyze destruction of structures and collapse of structures due to tsunami loading. Sincesuch a tsunami analysis code is unavailable to the authors, we develop a code using standard
computational fluid analysis methods and distributed memory type parallelization methods.
We perform a seamless simulation of SRA and HRTS by exchanging information between
simulations. We exchange information via Common City Model (CCM), which is a common data setof spatial and temporal properties of a city, accessible from each component simulation. Here, wedevelop data conversion modules that convert data stored in GIS to CCM, and data conversion
modules that exchange information between component simulations and CCM.
As an application of the developed method, we conduct a simulation targeted on the coastal area ofSendai. We convert external shapes of structures and ground elevation data stored in GIS to CCM, andmake input structure dataset for SRA. We assume a criterion for a structure to collapse, and modify thestructural shapes in CCM based on the results of SRA. We compare the results of HRTS run on themodified and original city configuration to confirm that we can run a seamless simulation that could
be used to evaluate the overall damage of a city under a series of earthquake and tsunami hazards.
METHODOLOGY
In this section, we explain about the framework developed to perform a seamless simulation of SRAand HRTS. In order to make an extendible method to accommodate multiple GIS data and component
simulations, we use CCM. CCM is a common data set that stores the properties of a city in common
data format. We first convert data stored in GIS to CCM. Then, each component simulation reads
relevant parts of CCM and makes an input city model, analyzes and writes the modifications to thecity model back to CCM; see Fig.1. By using a common data set of a city, we can integrate simulations
without the hassle to make direct data conversion modules between each component simulation. The
following are the details of the conversion methods and analysis methods used in this paper.
We convert elevation and structure data stored in GIS to CCM. Here, we develop modules thatconvert Digital Elevation Map (DEM) and two-dimensional vector structure data to CCM; see arrow(i) and (ii) in Fig. 1.
componentsimulations

GIS dataDEM
2D vectordata(i)(iii)

CCM(v)(ii)

SRA
(iv)

HRTS

Fig. 1 Design of INDS using CCM

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38°30’SendaiArahama

38°15’

Natori38°00’
140°45’141°00’141°15’

Fig. 2 Target area. Arahama, Wakabayashi ward, Sendai city, Japan.
The inputs of SRA are the shape of structures and input SGM. We convert structure shape data inCCM to inputs of SRA; see arrow (iii) in Fig.1. Since properties of structural members are notavailable in GIS, we guess the material properties and configuration of structural members using theexternal shape of structures based on the design code of Japan. SRA is performed using a
pre-developed code using One Component Model (OCM). OCM is a non-linear structural analysis
method designed for analysis of RC structures. The output of SRA is the time series of displacement ofstructures. We assume a criterion for a structure to collapse and change the CCM according to SRA
results; see arrow (iv) in Fig.1. For simplicity, we assume that a structure will collapse if the maximum
drift angle
R = max|u|/h,(1)

is larger than a threshold R0. Here, max|u| is the maximum displacement during the simulation and h is
the height of a structure. We modify the shape of a structure by making the height 1/4 and spreading
the structure 1.5 times in the horizontal directions if a structure is determined to be collapsed.
The inputs of HRTS are the external shapes of structures and ground elevation, and input tsunami
wave. We convert the structure and ground elevation data in CCM for HRTS; see arrow (v) in Fig.1.
We use a distributed memory type parallel analysis program using Smoothed Particle Hydrodynamics(SPH) for fluid analysis; see (Monaghan 1994) for details of SPH. The output of HRTS is a time seriesof fluid position, velocity and pressure. We do not change the configuration of the city based on HRTS,
although we plan to do this in the future using a fluid-structure interaction method.
APPLICATION TO COASTAL AREA OF SENDAI CITY
We apply the developed method to Arahama, Wakabayashi ward, Sendai, Japan, which is one of theseverely hit areas by the tsunami in the 2011 Tohoku earthquake; see Fig. 2. The objectives of thissection is to check that a seamless simulation of SRA and HRTS can be run on GIS datasets for
estimation of overall damage in coastal areas under earthquake-tsunami disasters.
We first convert data stored in GIS to CCM. The target area is a 2.0 x 3.0km domain with 1416structures. CCM consists of ground elevation and external shapes of structures; see Fig. 3. Here, GIS
data provided by the Geospatial Information Authority of Japan and NTT Geospace Corporation isused as an input.
We make an input city model for SRA and excite structures using SGM observed at K-NET
MYG013 station (Sendai) in the 2011 off the Pacific coast of Tohoku earthquake. For simplicity, we542

Fig. 3 Constructed CCM, consisting of external shape of structures and ground elevation. GIS data
provided by Geospatial Information Authority of Japan and NTT Geospace Corporation is used.

Fig. 4 Results of SRA at t = 91.0s from start of simulation. Colors indicate magnitude of deformation.
Response of each structure under SGM observed in the 2011 Tohoku region earthquake is analyzed.
input the same SGM to the base of each structure. The simulation is 0.01s x 30,000steps = 300s. Fig. 4
shows snapshots at t = 91.0s after start of the simulation. From this figure we can see that structures
have different response to the same input SGM according to its height, shape, and directions.
We deform structures by assuming that a structure will collapse if the maximum deformation angle
defined in Eq. (1) is larger than a threshold R0 = 1/40. We set this threshold relatively small comparedto the real estimate for a structure to collapse so that a number of structures will collapse; we do this tohave a clear difference between the modified and original city models. Fig. 5 shows the deformed stateof the city, where blue indicates collapsed shape, transparent indicates original shape, and solid white
indicate shapes of structures that are not determined to be collapsed.
Two cases of HRTS are performed; case 1 is with the modification of structures based on SRA,
and case 2 is the original CCM without modifications. We conduct HRTS on a 1,000 x 800m area,
each discretized in resolution of 1m. We input tsunami wave using a block of water 12m in height withinitial velocity of 5m/s; see Fig. 6. Although this setting is quite different from the actual tsunami wavethat hit this area, it is enough to confirm that the HRTS is applicable to a real city dataset stored in GIS.
We plan to use computed tsunami waves from the source to bay area as input tsunami waves in thefuture. The number of boundary particles used to model elevation and structure shapes for the 2 cases543

Fig. 5 Modified city model based on results of SRA. Blue indicate shape of structures that aredetermined to be collapsed, transparent shape indicate original shape, and solid white indicate shapesof structures that are not determined to be collapsed.

Fig. 6 Simulation setting of HRTS. Domain size of 1000 x 800m is discretized in resolution of 1m, andinput tsunami wave (12m in height, initial velocity 5m/s), indicated in blue, is inputted from the coast.
are 2,120,399 and 2,218,985, respectively, while the number of fluid particles used to model thetsunami is 3,694,981 for both of the cases. The simulation is 0.001s x 40,000steps = 40s. Figs. 7 and 8
show the results for the two cases. From these figures, we can see that three dimensional flows aroundstructures are computed. Comparing these figures, we can see that flow changes according to themodification to the city configuration.
CLOSING REMARKSIn this paper, we developed a framework to integrate SRA and HRTS by using CCM. We converted
data stored in the GIS to both the inputs for SRA and HRTS via CCM, and modified the city
configuration based on the results of SRA. HRTS is conducted using the modified city model.
Application to coastal area of Sendai shows that the flow of tsunami changes according to themodification in the CCM based on results of SRA. Our future works are to develop fluid-structureinteraction methods to simulate destruction of structures under tsunami loading.

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Fig. 7 Results of HRTS for case 1 (with modification to city model based on SRA results). Colors
indicate fluid velocity. Left figure: t = 15s, Right figure t = 25s.

Fig. 8 Results of HRTS for case 2 (original city model). Colors indicate fluid velocity.
Left figure: t = 15s, Right figure t = 25s.
ACKNOWLEDGMENTS
The authors would like to thank Dr. Toshikazu Kabeyasawa of Building Research Institute forproviding One-Component-Model structural analysis code used in this study. GIS data used in thisstudy was provided by the Geospatial Information Authority of Japan and NTT Geospace Corporation.
We used K-NET SGM records provided by National Research Institute for Earth Science and Disaster
Prevention (NIED) in this study.
REFERENCES
Hori, M. and Ichimura, T. (2008). “Current state of integrated earthquake simulation for earthquake
hazard and disaster.” Journal of Seismology, Vol. 12, 307-321.
Monaghan, J. J. (1994). “Simulating free surface flows with SPH.” Journal of Computational Physics,
Vol. 110, 309-406.
Sobhaninejad, G., Hori, M., Kabeyasawa, T. (2011). “Enhancing integrated earthquake simulation withhigh performance computing.” Advances in Engineering Software, Vol. 42, No. 5, 286-292.

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