Building vulnerability assessment in seismic areas using ensemble learning: A Nepal case study

Abstract

The vulnerability assessment is a crucial method for earthquake-prone areas to manage seismic risk. This paper develops an Extreme gradient boosting (XGBoost)-based model to assess the seismic vulnerability of buildings in Kavrepalanchok, Nepal. The vulnerability assessment model is trained based on the building survey data of the 2015 Gorkha earthquake. To rectify the impacts of class imbalance in raw data, the Synthetic Minority Oversampling Technique (SMOTE) technique is adopted in the data preprocessing stage. Then, XGBoost models for the 3 selected target variables are developed respectively. Finally, the performances of the developed models are evaluated, and the significant features that influence the vulnerability are investigated using the SHapley Additive exPlanations (SHAP) technique. Important findings are: (1) The developed SMOTE-XGBoost model for the overall building damage achieves the highest macro f1 score of 0.729 compared with other machine learning models. (2) The SMOTE technique is favorable when the label requiring more attention is the minor group (e.g., severe damage). (3) The foundation type, the roof type, and the building age are identified as the most significant features for the overall building damage grade. The developed learning model can be used as a decision tool to automate building vulnerability assessment in seismic areas using pre-earthquake data, providing decision support for disaster risk reduction at an early stage.