Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls

Efficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and postprocessing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced concrete shear walls (RCSWs) by introducing stacked machine learning (Stacked ML) models based on Bayesian optimization (BO), genetic algorithm (GA), particle swarm optimization (PSO), and gradient-based optimization (GBO) algorithms. The study investigates 4-, to 15-Story RCSWs assuming different bay lengths and soil types to build a comprehensive database based on the incremental dynamic analysis (IDA) subjected to 56 near-field pulse-like and no-pulse records. Having 227,200 and 63,384 data points for a median of IDA curve (MIDA) and seismic probability curve, respectively, the proposed Stacked ML models have shown good performance on curve fitting ability by accuracy of 99.1% and 99.4% for MIDA and seismic fragility curves, respectively. In addition, the proposed models can estimate the mean annual frequency, lambda, which is a key parameter in seismic risk assessment of buildings. To provide the results of the study for general buildings, a user-friendly GUI is proposed that facilitates result utilization, offering insights into seismic performance levels, providing the estimated MIDA and seismic failure probability curves, and mean annual frequency calculations for specific performance levels and seismic hazard curves.