01 Aug 2022
01 Aug 2022
Status: a revised version of this preprint is currently under review for the journal NHESS.

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

Kenta Tozato1, Shuji Moriguchi2, Shinsuke Takase3, Yu Otake1, Michael R. Motley4, Anawat Suppasri2, and Kenjiro Terada2 Kenta Tozato et al.
  • 1Department of Civil and Environmental Engineering, Tohoku University, Aza-Aoba 6-6-06, Aramaki, Aoba-ku, Sendai 980-8579, Japan
  • 2International Research Institute of Disaster Science, Tohoku University, Aza-Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-8572, Japan
  • 3Department of Civil Engineering and Architecture, Hachinohe Institute of Technology, 88-1 Ohbiraki, Myo, Hachinohe, Aomori 031-8501, Japan
  • 4Civil and Environmental Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, Washington, 98195-2700, USA

Abstract. Tsunamis are associated with numerous uncertainties. Therefore, there has been an emphasis on setting the placement of infrastructure facilities based on probabilistic approaches. However, advanced numerical simulations have been often insufficiently utilized due to high computational costs. Therefore, in this study, we developed a framework that could efficiently utilize the information obtained from advanced numerical simulations for probabilistic assessment and investigation of the optimal placement of facilities based on calculated probability. Proper orthogonal decomposition (POD) techniques were employed for utilizing the data from the numerical simulations for probabilistic risk evaluation. We constructed a surrogate model in which POD was efficiently used to extract the spatial modes. The results of the numerical simulation were expressed as a linear combination of the modes, and the POD coefficients were expressed as a function of the uncertainty parameters to represent a result of an arbitrary scenario at a low computational cost. We conducted numerical simulations of the 2011 tsunami off the Pacific Coast caused by Tohoku Earthquake as an example of the method proposed in this study. The tsunami reached the target area, and the fault parameters of “slip” and “rake” were selected as the target uncertainties. We then created several scenarios in which these parameters were changed and conducted further numerical simulations using POD to construct a surrogate model. We selected the maximum inundation depth in the target area and the maximum impact force that acts on the buildings as the target risk indices, and we constructed a surrogate model of the spatial distributions of each indicator. Furthermore, we conducted Monte Carlo simulations using the constructed surrogate model and the information on fluctuations in uncertainties to calculate the spatial distribution of the failure criterion exceedance probabilities. We then used the Monte Carlo simulation results and a genetic algorithm to identify the optimal placement of facilities based on probability. We also discuss how the optimal placement changes according to differences in risk indices and the differences between parallel and series systems. The failure scenarios for each system are also discussed based on the failure probability. We show that the proposed method of efficiently utilizing advanced numerical simulation information was useful for conducting probabilistic hazard assessments and investigating the optimal placement of facilities based on probability theory.

Kenta Tozato et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-208', Anonymous Referee #1, 11 Sep 2022
    • AC1: 'Reply on RC1', Kenta Tozato, 20 Sep 2022
  • RC2: 'Comment on nhess-2022-208', Anonymous Referee #2, 29 Oct 2022
    • AC2: 'Reply on RC2', Kenta Tozato, 07 Nov 2022

Kenta Tozato et al.

Data sets

K-Tozato/3D_tsunami_simulation: (Dataset_for_NHESS) Tozato, K.

Kenta Tozato et al.


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Short summary
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.