Various technologies for improving earthquake-disaster-mitigation capability in urban communities are being developed and proposed. However, these technologies are sometimes difficult to use in actual applications due to the lack of incentives for owners of buildings or infrastructures, because the owners cannot calculate their cost-effectiveness and thus consider the installation payment for these technologies as a cost to be reduced. To address this problem, we propose the construction of an “Urban cyber physical system (CPS).” This urban CPS evaluates the earthquake-resistant capability of buildings and/or social infrastructures to help owners easily understand the cost-effectiveness of adopting these technologies. The CPS also calculates the effects of newly-developed technologies, thereby allowing owners to accept new technologies based on their effectiveness. The study concept of the urban CPS is as follows: (1) Construction of an “Information platform” by using data aggregation and analysis of existing vibration data of structures, datasets of building (or construction) information modeling and various other available databases; (2) Development of a “Simulation platform” that has a prediction function to calculate the behaviors of urban communities during earthquakes by using data in the Information platform and an identification function to identify structural systems from input earthquake motions and responses of structures; and (3) Establishment of an “Eco-system” to operate the urban CPS in urban community design, based on the perspective of earthquake resilience.

1. [1] “Technical report on preparedness for large scale natural disasters delivering national crisis,” http://committees.jsce.or.jp/chair/node/21 (in Japanese) [accessed March 10, 2020]
2. [2] “White Paper on Disaster Management,” http://www.bousai.go.jp/kaigirep/hakusho/h29/ (in Japanese) [accessed March 10, 2020]
3. [3] Y. Usuda et al., “Effects and Issues of Information Sharing System for Disaster Response,” J. Disaster Res., Vol.12, No.5, pp. 1002-1014, 2017.
4. [4] Network Center for Earthquake, Tsunami and Volcano, “About MOWLAS,” http://www.mowlas.bosai.go.jp/mowlas/?LANG=en [accessed March 10, 2020]
5. [5] “Tokyo Metropolitan Resilience Project,” http://forr.bosai.go.jp/ (in Japanese) [accessed March 10, 2020]
6. [6] Consortium for Socio-Functional Continuity Technology (SOFTech), http://www.softech.titech.ac.jp/index_en.html [accessed March 10, 2020]
7. [7] “Final report of site-investigation on damages caused by Hyogo Nanbu Earthquake,” https://www.kenken.go.jp/japanese/research/iisee/list/topics/hyogo/pdf/h7-hyougo-jp-all.pdf (in Japanese) [accessed March 10, 2020]
8. [8] “Building Standards Act,” Japanese Law, Act No.201 of 1950 (in Japanese).
9. [9] “Act on Promotion of Seismic Retrofitting of Buildings,” Japanese Law, Act No.123 of 1995 (in Japanese).
10. [10] “City Planning Act,” Japanese Law, Act No.100 of 1968 (in Japanese).
11. [11] Japan Electronics and Information Technology Industries Association (JEITA), “CPS/IoT,” https://www.jeita.or.jp/cps-e/ [accessed March 10, 2020]
12. [12] Code for America, “What if all government services were this good?,” https://www.codeforamerica.org/ [accessed March 10, 2020]
13. [13] Hyogo Earthquake Engineering Research Center, National Research Institute for Earth Science and Disaster Resilience (NIED), http://www.bosai.go.jp/hyogo/ehyogo/index.html [accessed March 10, 2020]
14. [14] “Web API for IoT: Requirements Definition Document,” http://yashirolab.iis.u-tokyo.ac.jp/files/webapi-for-iot-rdd-v201812.pdf (in Japanese) [accessed March 10, 2020]
15. [15] T. Yamashita, H. Muneo, and K. Kajiwara, “Petascale Computation for Earthquake Engineering,” Computing in Science & Engineering, Vol.13, No.4, pp. 44-49, 2011.
16. [16] K. Fujita et al., “Scalable Multicase Urban Earthquake Simulation Method for Stochastic Earthquake Disaster Estimation,” Procedia Computer Science, Vol.51, pp. 1483-1493, 2015.
17. [17] H. Kawai et al., “Effective Implementation of Tensor Operation Library for Continuum Mechanics Based on High Performance Design Pattern,” Trans. of the Japan Society for Computational Engineering and Science (JSCES), Vol.2018, Article No.20180012, 2018 (in Japanese).
18. [18] G. Alzetta et al., “The Deal.II Library, Version 9.0,” J. of Numerical Mathematics, Vol.26, No.4, pp. 173-183, 2018.
19. [19] “ASEBI: Archives of E-Defense Shaking table Experimentation Database and Information,” https://www.edgrid.jp/ (in Japanese) [accessed March 10, 2020]
20. [20] O. Abdeljaber et al., “Real-Time Vibration-Based Structural Damage Detection Using One-Dimensional Convolutional Neural Networks,” J. of Sound and Vibration, Vol.388, pp. 154-170, 2017.
21. [21] M. Shiraishi et al., “Local Damage Detection Using Densely Deployed Vibration Sensors and Output Error of Substructures,” J. of Structural and Construction Engineering (Trans. of AIJ), Vol.82, No.736, pp. 801-811, 2017.
22. [22] R. Adner, “The Wide Lens,” Penguin Books, 2012.
23. [23] https://www.futurecenteralliance-japan.org/en/innovation/livinglab [accessed March 10, 2020]
24. [24] “The Geo Spatial Information Center,” https://www.geospatial.jp/gp_front/ (in Japanese) [accessed March 10, 2020]