A Seismic Demand Model for Bridges Based on IM and EDP Parameter Matching and Bidirectional Optimization

Abstract

The accuracy of seismic demand models in seismic vulnerability analysis of structures or components mainly depends on the seismic intensity measures (IMs) and engineering demand parameters (EDPs). This paper proposes a novel method to obtain the optimal seismic demand model for the seismic vulnerability analysis of bridges. The method obtains the IM-EDP combination by matching all IMs and EDPs within a wide range one by one, considering the contribution of multiple IM parameters to the seismic response of the structure and avoiding the blindness of EDP selection. The IM is determined by calculating Pearson correlation coefficient and partial correlation coefficient, controlling the correlation between EDP and IM (or IMs) to a minimum to reduce the multicollinearity within the vector IMs and avoid ill-conditioned models. The optimal seismic demand model is obtained by inspecting the scatter plot and residual plot of suboptimal seismic demand models determined from all combinations by guaranteeing efficiency and sufficiency. The efficiency of seismic demand models is guaranteed by controlling the root mean square error (RMSE) and the coefficient of determination (R2). The sufficiency of models is guaranteed by controlling the slope of fitted line. A continuous rigid frame bridge with double thin-walled piers is used as a case study and a dynamic time-history analysis is performed to obtain the seismic vulnerability of bridge with the proposed method. The results show that the proposed method is feasible and ideally suited for optimizing seismic demand model.

Importance Analysis of Structural Seismic Demand Based on Support Vector Machine

“…However, it needs a large number of samples. Even if the efficient sampling method is applied to MC [24], the sample size is still large, which will increase the calculation difficulty for the complicated structures. In view of this, this article proposes a new method based on the support vector machine (SVM) algorithm [25,26] to perform the importance analysis and investigate the influence of random variables on the seismic demand of structures, where the sample size required by the proposed method is far less than that of MC.…”