A hybrid stacked ensemble model for rapid seismic damage assessment with imbalanced training data: A case study on the 2023 Kahramanmaras earthquakes

Mostofi S., Yilmaz Z., BAŞAĞA H. B., OKUR F. Y., ALTUNIŞIK A. C., Taciroglu E.

ENGINEERING STRUCTURES, cilt.340, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 340
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.engstruct.2025.120754
  • Dergi Adı: ENGINEERING STRUCTURES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Geobase, ICONDA Bibliographic, INSPEC
  • Anahtar Kelimeler: Ensemble methods, Imbalanced dataset, Machine learning, Seismic damage assessment, Stacked ensemble
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Accurate and rapid classification of earthquake-damaged buildings is crucial for effective first response and recovery. Conventional inspection methods are labor-intensive, and thus, automated machine learning offers a promising alternative. Yet, real-world seismic datasets that can be used for training machine learning models are often highly imbalanced, with severely damaged or collapsed structures representing only a small minority. Previous studies that have explored class-imbalance methods focused on basic data-level or conventional ensemble techniques, as well as advanced strategies-such as SMOTEENN, CTGAN, and stacked ensembles with custom weighting-remain underexplored. In this study, building survey data from the 2023 Kahramanmaras, earthquakes were used to comprehensively evaluate data-level, algorithm-level, and hybrid methods. Guided by the results of these evaluations, a novel class-weighted stacked ensemble model featuring Balanced Random Forest and XGBoost as base learners was devised. This new model leverages customized misclassification penalties to improve minority class detection and achieves a balanced accuracy of 0.62 and a G-Mean of 0.75, markedly outperforming models employing data-level balancing alone.