Dynamic ensemble-learning model for seismic risk assessment of masonry infilled steel structures incorporating soil-foundation-structure interaction
提出一种动态集成机器学习模型，自动优化超参数，用于评估考虑土-基础-结构相互作用的砌体填充钢框架的地震概率曲线，精度达99.3%，并开发了图形界面工具。
• Dynamic ensemble ML (DE-ML) model is proposed for seismic probability assessment of steel MRFs • Steel MRFs equipped with infill masonry walls and soil-foundation-structure interaction • Proposed DE-ML model is hyperparameter free to estimate seismic probability curves • Automated optimizing and utilizing hyperparameters exhibited superior DE-ML performance • Seismic probability assessment can be done by proposed DE-ML implemented on GUI This research is focused on proposing a dynamic ensemble machine-learning (DE-ML) model with the hybrid ability to optimize the hyperparameters by genetic algorithms (GA) and particle swarm optimization (PSO). The purpose of investigation is to estimate seismic probability curves for different kinds of datasets, and decrease the need for modeling and nonlinear analysis of structures. The research investigates a comprehensive dataset of 1536 median of incremental dynamic analysis (M-IDA) curves and 6144 failure probability curves for four demand thresholds, which were determined for the 2-, to 9-story steel moment-resisting frames (MRFs) assuming five soil types with and without soil–foundation–structure interaction (SFSI), four bay lengths, and four implementing conditions of infill masonry walls (IWs). The results show that automated stacked ML (AS-ML) models have superior performance in predicting the measure curves of seismic performance and failure probability (i.e., 97.6%), while conventional ML models of extreme gradient boosting (XGBoost), gradient boosting machine (GBM), random forest (RF), and LightGBM have a prediction accuracy of 91% to 94.2%. However, proposed DE-ML models have the best-fitted curves and show an accuracy of 99.3%, while they are compatible with different datasets and case studies. Therefore, they were developed into the GUI to be used for predicting seismic probability curves and risk evaluation of MRFs including IWs and SFSI effects.