Post-earthquake functionality and resilience prediction of bridge networks based on data-driven machine learning method

Abstract

Earthquake-induced bridge damage can disrupt transportation networks, potentially hindering emergency response and post-disaster recovery efforts, and posing public safety risks in affected areas. Rapid and accurate assessment of post-earthquake resilience of bridge networks is crucial for evaluating urban seismic performance. Traditional resilience assessment methods, constrained by complex traffic distribution processes, struggle to quickly evaluate the traffic performance of bridge networks during the post-earthquake recovery period. This paper presents a two-layer stacking ensemble model for predicting the functionality and resilience of bridge networks. The first layer integrates advantages of four base learners, including random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and extreme gradient boosting (XGBoost). The second layer completes regression of functionality based on a support vector machine (SVM). Bayesian optimization and 5-fold cross-validation are employed for hyperparameter tuning of the ensemble model. Finally, the proposed model is validated using the Sioux-Falls bridge network. Results demonstrate that the developed model provides rapid predictions of post-earthquake network functionality and resilience. Additionally, this model can guide post-earthquake repair decisions and assist in optimizing the allocation of regional repair resources.