Seismic Fragility Estimation Based on Machine Learning and Particle Swarm Optimization

Abstract

In seismic performance assessment, the development of building fragility curves is critical for performance-based engineering. Traditional methods for time history analysis, reliant on detailed ground motion (GM) inputs, often suffer from inefficiency and a lack of automation. This study proposes an accurate fragility assessment methodology, which is assisted by machine learning (ML) and particle swarm optimization (PSO), adept at handling scenarios with both scarce and sufficient fragility data. Under scenarios of scarce data, the integrated algorithms of PSO and ML are utilized, focusing on selecting GMs that may induce maximum inter-story drifts. When the dataset is sufficient, an ML fusion model is utilized to predict engineering demand parameters (EDPs), facilitating the generation of more accurate fragility curves. The effectiveness of this method is demonstrated through a case study on a high-rise reinforced concrete (RC) building, revealing a marked improvement in the precision of GM selection and the estimated range of fragility curves over traditional approaches. The proposed methodology aids in advancing structural optimization and the development of early-warning systems for seismic events, thus holding the potential to enhance current seismic risk mitigation strategies.