A NEW COMPUTATIONAL METHOD FOR SEISMIC HAZARD ANALYSIS

Abstract eng:
The purpose of this paper is focused on the determination of the seismic hazard for Taichung Area in Taiwan, based on the recorded historical data provided by National Central Weather Bureau of Taiwan. Before performing the probabilistic seismic hazard analysis (PHSA), the parameters related to the seismic input are identified based on the above data. In the conventional seismic hazard analysis, it is necessary to predetermine the distributions of the associated parameters involved in the probabilistic seismic hazard analysis. In order to overcome this shortcoming, the paper proposes a new methodology of determining the distributions by the concept of Kernel sampling density estimation. This approach is more nonparametric in the sense that less rigid assumptions will be made about the distribution of the observed data. The data is used to determine the Kernel sampling density function of the parameters and then the Monte Carlo simulation method is employed to estimate the associated seismic hazard. However, for the case of low seismic hazard, the required computational cost to reach an accurate result may be expensive. Therefore, simple Kernel method of importance sampling methods is performed with an aim to reduce the statistical error inherent in Monte Carlo methods.

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