Explainable Ensemble Learning for Rapid Seismic Damage Assessment: A Comprehensive Benchmark Using Real Data from the 2023 Kahramanmaraş Earthquakes

İngilizce (OpenAlex)

The 6 February 2023 Kahramanmaraş earthquakes caused widespread structural damage and highlighted the need for rapid building-level decision support in post-earthquake assessment. This study presents an explainable ensemble learning framework for seismic damage prediction using 16,611 building-level field observations from Kırıkhan, Hatay, Türkiye. The original damage records were reorganized into three operational classes: No-Damage, Slight–Moderate, and Heavy–Collapse. Eight tree-based ensemble models, LightGBM, CatBoost, XGBoost, Random Forest, Extra Trees, Gradient Boosting Machine, AdaBoost, and HistGradientBoosting, were evaluated under a consistent protocol using class-weighting strategies where supported, with Balanced Accuracy as the primary metric. LightGBM and Random Forest achieved the joint-highest Balanced Accuracy value (0.650). Random Forest produced the strongest agreement-based metrics, while LightGBM remained closely competitive and was selected as the representative model for explainability because of its balanced class-wise behavior. CatBoost achieved the highest Heavy–Collapse recall (0.729), XGBoost achieved the highest Macro-AUC (0.821), and GBM produced the highest Overall Accuracy (0.658), showing that model ranking varied by evaluation criterion. SHapley Additive exPlanations identified building age, lithology, number of floors, structural system, plinth area, and proximity to faults and surface ruptures as key contributors. The remaining classification uncertainty, particularly among adjacent damage states, indicates that the framework is best interpreted as a complementary decision-support tool for preliminary screening and prioritization before final safety decisions or official damage assessment.