Machine Learning-Based Rapid Assessment of Story-Level Seismic Damage in Steel Bundled-Tube Structures

作者：Junxia Zhou, Xiaohui Qin, Yong Hao, Jianchao Liu, Ruifang Hou, Pengfei Li · 发表于：Buildings · 年份：2025 · DOI：10.3390/buildings15203758 · 研究领域：Structural Health Monitoring Techniques、Structural Load-Bearing Analysis、Seismic Performance and Analysis

This study employed machine learning to establish an intelligent model for rapid and accurate seismic damage assessment of steel bundled-tube stories. The study built a 100-story elastoplastic steel bundled-tube model based on an actual engineering case, and then extracted and labeled data. Eight machine learning algorithms were employed to assess the seismic damage states of the steel bundled-tube stories. Hyperparameter optimization was performed on the two best-performing algorithms, and Shapley Additive Explanations (SHAP) analysis was used to investigate the influence of input variables on the five damage states. Using original parameters, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) showed highest accuracies (94.6% and 94.3%). After optimization, XGBoost’s accuracy rose by 2.2% to 96.5%, outperforming RF, and is thus recommended as the final model. This study fills the gap in story-level damage assessment using machine learning. SHAP analysis revealed peak acceleration and story load-bearing capacity as core variables. Displacement is more crucial in the low-damage state, while energy dissipation plays a dominant role in the high-damage state, which poses a challenge to the traditional seismic design that only limits displacement. The method identifies weak stories for targeted reinforcement, optimizing seismic performance of steel bundled-tube structures.