Machine Learning-Based Approach for Seismic Damage Prediction Method of Building Structures Considering Soil-Structure Interaction

Jongmuk Won and Jiuk Shin
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Jongmuk Won: Department of Civil and Environmental Engineering, University of Ulsan, Daehak-ro 93, Nam-gu, Ulsan 680-749, Korea
Jiuk Shin: Department of Architectural Engineering, Gyeongsang National University (GNU), Jinju-daero, Jinju 660-701, Korea

Sustainability, 2021, vol. 13, issue 8, 1-14

Abstract: Conventional seismic performance evaluation methods for building structures with soil–structure interaction effects are inefficient for regional seismic damage assessment as a predisaster management system. Therefore, this study presented the framework to develop an artificial neural network-based model, which can rapidly predict seismic responses with soil–structure interaction effects and determine the seismic performance levels. To train, validate and test the model, 11 input parameters were selected as main parameters, and the seismic responses with the soil–structure interaction were generated using a multistep analysis process proposed in this study. The artificial neural network model generated reliable seismic responses with the soil–structure interaction effects, and it rapidly extended the seismic response database using a simple structure and soil information. This data generation method with high accuracy and speed can be utilized as a regional seismic assessment tool for safe and sustainable structures against natural disasters.

Keywords: artificial neural network; soil–structure interaction effect; multistep analysis process; seismic performance evaluation; safe and sustainable structure (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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