Evaluation of correlation between engineering demand parameters of structures for seismic system reliability analysis

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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.

“…To facilitate calculation of the joint failure probability using Equation (), Kang et al 21 . derived the correlation coefficient

{\rho _{{F_i}{F_j}}}

\begin{equation} {\rho}_{{F}_{i}{F}_{j}}=\frac{{b}_{i}{b}_{j}{\sigma}_{{\widehat{{S}_{a}}}_{i}}{\sigma}_{{\widehat{{S}_{a}}}_{j}}}{\sqrt{{b}_{i}^{2}{\sigma}_{{\widehat{{S}_{a}}}_{i}}^{2}+{\sigma}_{{\psi}_{i}}^{2}}\sqrt{{b}_{j}^{2}{\sigma}_{{\widehat{{S}_{a}}}_{j}}^{2}+{\sigma}_{{\psi}_{j}}^{2}}}{\rho}_{{\widehat{{S}_{a}}}_{i}{\widehat{{S}_{a}}}_{j}}+\frac{{\sigma}_{{\psi}_{i}}{\sigma}_{{\psi}_{j}}}{\sqrt{{b}_{i}^{2}{\sigma}_{{\widehat{{S}_{a}}}_{i}}^{2}+{\sigma}_{{\psi}_{i}}^{2}}\sqrt{{b}_{j}^{2}{\sigma}_{{\widehat{{S}_{a}}}_{j}}^{2}+{\sigma}_{{\psi}_{j}}^{2}}}{\rho}_{{\psi}_{i}{\psi}_{j}} \end{equation}

where

{\sigma _{{{\widehat {{S_a}}}_i}}}

{\sigma _{{{\widehat {{S_a}}}_j}}}

…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Kang et al 21 . proposed to estimate the EDP residual correlation

{\rho _{{\psi _i}{\psi _j}}}

using the IDA method.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quantifying uncertainties and correlations of engineering demand parameters of building structures for regional seismic loss assessment

Kang

Kwon

Song

2022

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Seismic risk analysis of electrical substations based on the network analysis method

Liang

Blagojević

Xie

et al. 2022

Electrical substations are critical elements of a power grid, enabling the transmission and distribution of electric power from power generators to the end‐users. Experiences from previous earthquakes have shown that electrical substations can be damaged due to ground shaking, reducing their functionality and potentially preventing the generated electric power from reaching end‐users. To assess the seismic vulnerability of a substation, a modular quantitative assessment method is proposed. In this method, the relation between the functionality state of a substation and the damage state of its components was established through the connection matrix technique. A substation is viewed as a network system, whose topology is defined by the connections among various pieces of electrical equipment (i.e., the components), represented in the connection matrix. The maximum allowable power transmission capacity of the substation after an earthquake is adopted as the system functionality metric, which is jointly determined by the power input, transform, and output capacity of the substation. The seismic vulnerability of an electrical substation is quantified by probabilistically calculating its postearthquake functionality when exposed to various earthquake intensities using Monte Carlo sampling. Finally, the risk of substation functionality loss is quantified by integrating the seismic hazard curve with the seismic vulnerability model of the substation. Two realistic case studies on a distribution substation and a transmission substation, with the same equipment configuration but different power delivery paths, were performed using the proposed method. Furthermore, a sensitivity analysis regarding the equipment fragility parameters is conducted, providing a risk‐informed basis for improving the seismic performance of the substation system.

Deep neural network‐based regional seismic loss assessment considering correlation between EDP residuals of building structures

Kang

Kim

Kwon

et al. 2022

Self Cite

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.