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
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings13020564

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 structure portfolios remains a challenging undertaking, as it requires developing numerous high-fidelity, integrated models to capture the dynamic interplay and uncertainties in structures, foundations, and supporting soils. This study develops a one-dimensional convolutional neural network (1D-CNN) model to efficiently predict to what degree considering the SSI would change the inter-story drifts and base shear forces of RC frame buildings. An experimentally validated finite element model is developed to simulate the nonlinear seismic behavior of the building-foundation–soil system. Subsequently, a database comprising input data (i.e., structural and soil parameters, ground motions) and output predictors (i.e., changes in story drift and base shear) is constructed by simulating 1380 pairs of fixed-base versus soil-supported structures under earthquake loading. This large-scale dataset is used to train, test, and identify the optimal hyperparameters for the 1D-CNN model to quantify the demand differences in inter-story drifts and base shears due to the SSI. Results indicate the 1D-CNN model has a superior performance, and the absolute prediction errors of the SSI influence coefficients for the maximum base shear and inter-story drift are within 9.3% and 11.7% for 80% of cases in the testing set. The deep learning model can be conveniently applied to enhance the accuracy of the RSDA of RC buildings by updating their seismic responses where no SSI is considered.