Seismic Fragility Analysis of High‐Rise Shear Wall Structures Based on Multilayer Perceptron and Random Forest

ABSTRACT The traditional structural finite element seismic response analysis process is often complex and time consuming. This study introduces a novel prediction methodology utilizing machine learning (ML) models to facilitate the rapid analysis of the seismic fragility of high‐rise shear wall structures. Three high‐rise shear wall structural models with varying story heights were designed in compliance with current Chinese codes. Ground motion records were selected as inputs based on the target response spectrum, and a sample database for ML was constructed using the incremental dynamic analysis (IDA) method. Key ground motion intensity and structural parameters were chosen as the characteristic inputs, with the maximum interstory drift angle ( θ max ) and maximum floor acceleration serving as the output parameters. Structural responses were predicted using both the multilayer perceptron (MLP) and random forest (RF) methods. The MLP model was further analyzed using SHapley Additive exPlanations (SHAP) to explore the contributions of each characteristic parameter to the structural response. The seismic fragility of the high‐rise shear wall structures was subsequently assessed. Results indicate that the RF‐based method provides higher accuracy in seismic fragility analysis compared with the MLP algorithm. Notably, peak ground acceleration ( PGA ) emerged as the most significant parameter impacting structural response. The proposed methodology effectively enables rapid seismic fragility analysis of high‐rise shear wall structures.