Predicting the Influence of Soil–Structure Interaction on Seismic Responses of Reinforced Concrete Frame Buildings Using Convolutional Neural Network

• Published in: Buildings (Basel) Vol. 13; no. 2; p. 564
• Main Authors: Wang, Jishuai, Xie, Yazhou, Guo, Tong, Du, Zhenyu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Analysis; Artificial neural networks; Buildings; Computer simulation; Concrete; Concrete construction; convolutional neural network; Damage assessment; Deep learning; Drift; Earthquake construction; Earthquake damage; Earthquake loads; Earthquakes; Errors; Finite element method; Frame structures; Ground motion; Machine learning; Mathematical models; Neural networks; RC frame; regional seismic damage assessment; Reinforced concrete; Seismic activity; Seismic engineering; Seismic response; Shear forces; Shear strain; Soil dynamics; Soil-structure interaction; Soils; China

Most regional seismic damage assessment (RSDA) methods are based on the rigid-base assumption to ensure evaluating efficiency, while these practices introduce factual errors due to neglecting the soil–structure interaction (SSI). Predicting the influence of the SSI on seismic responses of regionwide...