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Pre‐ and post‐earthquake regional loss assessment using deep learning
Author(s) -
Kim Taeyong,
Song Junho,
Kwon OhSung
Publication year - 2020
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.3258
Subject(s) - vulnerability (computing) , computer science , seismic hazard , seismic risk , probabilistic logic , artificial neural network , vulnerability assessment , deep learning , hazard , fragility , earthquake scenario , earthquake simulation , seismology , machine learning , geology , artificial intelligence , psychology , chemistry , computer security , organic chemistry , psychotherapist , psychological resilience
Summary As urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community‐level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre‐ and post‐earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction of individual structures during the pre‐earthquake loss assessment, a widely used nonlinear static procedure is replaced by the recently developed probabilistic deep neural network model. The variabilities of the nonlinear responses of a structural system given the seismic intensity can be quantified during the loss assessment process. Second, to facilitate near‐real‐time post‐earthquake loss assessments, an adaptive algorithm, which identifies the optimal number and locations of sensors in a given urban area, is proposed. Using a deep neural network that estimates area‐wide structural damage given the spatial distribution of the seismic intensity levels as a surrogate model, the algorithm adaptively places additional sensors at property lots at which errors from surrogate estimations of the structural damage are the greatest. Note that the surrogate model is constructed before earthquake events using simulated datasets. To test and demonstrate the proposed frameworks, the paper introduces thorough numerical investigations of two hypothetical urban communities. The proposed frameworks using the deep learning methods are expected to make critical advances in pre‐ and post‐earthquake regional loss assessments.