Development of Models for Prompt Responses from Natural Disasters

Kichul Jung, Daeryong Park and Sangki Park
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Kichul Jung: Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea
Daeryong Park: Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea
Sangki Park: Department of Smart Construction, Korea Institute of Civil Engineering and Building Technology, Gyeonggi-do 10223, Korea

Sustainability, 2020, vol. 12, issue 18, 1-16

Abstract: This study aims to provide an enhanced model for rapid responses from natural disasters by estimating the maximum structural displacement. The linear regression, support vector machine, and Gaussian process regression (GPR) models were applied to obtain displacement estimates. Further, normalization (NM) and standardization (SD) of variables, and principal component analysis (PCA) were applied to improve model performance. The k-fold cross-validation approach was used to assess the results from the models based on the root-mean-square error and the R-squared indices. According to the results, the GPR model with NM and SD tended to provide the best estimates among the three models. The model that was based on a PCA value of 97% yielded better displacement estimation than the models with PCA values of 95% and 100%. Based on the displacement estimation, the maximum inter-story drift ratio was used to produce the fragility curve that can be used for risk assessment. The fragility curve parameters obtained from the actual numerical and predicted models were investigated and yielded similar responses. The proposed model can thus provide accurate and quick responses in disaster case by rapidly predicting the structural damage information.

Keywords: Gaussian process regression; disaster probabilistic risk assessment; machine learning; maximum displacement; principal component analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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