No abstract

For an accurate regional seismic loss assessment, it is essential to quantify the uncertainties and correlations of the engineering demand parameters (EDP) of the building structures. Previous studies predicted the mean EDP of each structure by a regression function of the selected intensity measure (IM), while its variability is described by the “EDP residual.” The authors recently proposed a new formulation and Incremental Dynamic Analysis (IDA)‐based methods to evaluate the correlation between EDP residuals. This paper proposes an IM‐invariant method for estimating the variances and correlations of the EDP residuals of building structures. Based on the EDP residuals of various buildings estimated using the proposed method, primary structural characteristics affecting EDP residuals are identified. In addition, this study develops EDP estimation regression equations using predictive variables defined based on the identified structural characteristics to facilitate consideration of the EDP residual correlation in regional seismic loss assessment. Numerical examples verify the regression models and demonstrate that the proposed method can improve the accuracy of a regional loss assessment by considering the building types in the inventory.

Regional seismic loss assessment is essential for developing an emergency response plan in the event of an earthquake, which can reduce casualties and socioeconomic losses in an urban community. The uncertainties and correlations of structures’ engineering demand parameters (EDP) should be adequately considered to evaluate the community‐level seismic risk. Recently, the authors proposed an incremental dynamic analysis‐based method and regression‐based models to estimate the variances and correlations of residuals in EDP termed “EDP residuals.” The quantified uncertainties of the EDP residuals facilitate the accurate evaluation of the regional seismic performance. Still, the computational cost required in the estimation process makes its application a challenge. This study proposes two frameworks for regional seismic loss assessment based on deep neural networks (DNNs) to extend the applicability of EDP residual estimation and improve its accuracy. The first framework estimates the EDP residuals of buildings by combining the EDP residuals of various single‐degree‐of‐freedom (SDOF) systems through the modal combination rules. Three DNN models are constructed to predict the EDP residuals of SDOF systems. The second framework predicts the EDP residuals of buildings directly using two DNN models. The proposed frameworks are verified by numerical examples of regional seismic loss assessment, for which time history‐based “exact” solutions exist. The supporting source code, data, and trained models are available for download at https://github.com/TyongKim/EDP_residual.Highlights The importance of considering EDP residual correlation in seismic system reliability analysis is demonstrated. Two DNN‐based frameworks are developed to estimate EDP residuals of building structures. Modal combination rule is employed to utilize EDP residuals of SDOF systems representing structural modes. DNN models are constructed to predict EDP residuals of SDOF and MDOF systems. Accuracy and applicability of DNN‐based frameworks are successfully demonstrated by example of regional seismic loss assessment.