Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 23, issue 5
Articles | Volume 23, issue 5
https://doi.org/10.5194/nhess-23-1891-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-1891-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 5
Research article
 | 
25 May 2023
Research article |  | 25 May 2023

Optimal probabilistic placement of facilities using a surrogate model for 3D tsunami simulations

Kenta Tozato, Shuji Moriguchi, Shinsuke Takase, Yu Otake, Michael R. Motley, Anawat Suppasri, and Kenjiro Terada

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Cited articles

Alhamid, A. K., Akiyama, M., Ishibashi, H., Aoki, K., Koshimura, S., and Frangopol, D. M.: Framework for probabilistic tsunami hazard assessment considering the effects of sea-level rise due to climate change, Struct. Saf., 94, 102152, https://doi.org/10.1016/j.strusafe.2021.102152, 2022. a
Annaka, T., Satake, K., Sakakiyama, T., Yanagisawa, K., and Shuto, N.: Logic-tree approach for probabilistic tsunami hazard analysis and its applications to the Japanese coasts, Pure Appl. Geophys, 164, 577–592, https://doi.org/10.1007/s00024-006-0174-3, 2007. a
Baba, T., Kamiya, M., Tanaka, N., Sumida, Y., Yamanaka, R., Watanabe, K., and Fujiwara, H.: Probabilistic tsunami hazard assessment based on the Gutenberg–Richter law in eastern Shikoku, Nankai subduction zone, Japan, Earth Planets Space, 74, 156, https://doi.org/10.1186/s40623-022-01715-1, 2022. a
Bamer, F. and Bucher, C.: Application of the proper orthogonal decomposition for linear and nonlinear structures under transient excitations, Acta Mech., 223, 2549–2563, https://doi.org/10.1007/s00707-012-0726-9, 2012. a
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This study presents a framework that efficiently investigates the optimal placement of facilities probabilistically based on advanced numerical simulation. Surrogate models for the numerical simulation are constructed using a mode decomposition technique. Monte Carlo simulations using the surrogate models are performed to evaluate failure probabilities. Using the results of the Monte Carlo simulations and the genetic algorithm, optimal placements can be investigated probabilistically.
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