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Application of Importance Sampling Method in Sliding Failure Simulation of Caisson Breakwaters
Authors:WANG Yu-chi  WANG Yuan-zhan  LI Qing-mei  CHEN Liang-zhi
Affiliation:State Key Laboratory for Simulation & Safety of Hydraulic Engineering, Tianjin Key Laboratory of Harbor and Ocean Engineering, Tianjin University, Tianjin 300072, China;State Key Laboratory for Simulation & Safety of Hydraulic Engineering, Tianjin Key Laboratory of Harbor and Ocean Engineering, Tianjin University, Tianjin 300072, China;State Key Laboratory for Simulation & Safety of Hydraulic Engineering, Tianjin Key Laboratory of Harbor and Ocean Engineering, Tianjin University, Tianjin 300072, China;State Key Laboratory for Simulation & Safety of Hydraulic Engineering, Tianjin Key Laboratory of Harbor and Ocean Engineering, Tianjin University, Tianjin 300072, China
Abstract:It is assumed that the storm wave takes place once a year during the design period, and N histories of storm waves are generated on the basis of wave spectrum corresponding to the N-year design period. The responses of the breakwater to the N histories of storm waves in the N-year design period are calculated by mass-spring-dashpot mode and taken as a set of samples. The failure probability of caisson breakwaters during the design period of N years is obtained by the statistical analysis of many sets of samples. It is the key issue to improve the efficiency of the common Monte Carlo simulation method in the failure probability estimation of caisson breakwaters in the complete life cycle. In this paper, the kernel method of importance sampling, which can greatly increase the efficiency of failure probability calculation of caisson breakwaters, is proposed to estimate the failure probability of caisson breakwaters in the complete life cycle. The effectiveness of the kernel method is investigated by an example. It is indicated that the calculation efficiency of the kernel method is over 10 times the common Monte Carlo simulation method.
Keywords:caisson breakwater   complete life cycle   failure probability   importance sampling
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