Data-Driven Machine-Learning-Based Seismic Response Prediction and Damage Classification for an Unreinforced Masonry Building

Unreinforced masonry buildings are highly vulnerable to earthquake damage due to their limited ability to withstand lateral loads, compared to other structures. Therefore, a detailed assessment of the seismic response and resultant damage associated with such buildings becomes necessary. The present study employs machine learning models to effectively predict the seismic response and classify the damage level for a benchmark unreinforced masonry building. In this regard, eight regression-based models, namely, Linear Regression (LR), Stepwise Linear Regression (SLR), Ridge Regression (RR), Support Vector Machine (SVM), Gaussian Process Regression (GPR), Decision Tree (DT), Random Forest (RF), and Neural Networks (NN), were used to predict the building’s responses. Additionally, eight classification-based models, namely, Naïve Bayes (NB), Discriminant Analysis (DA), K-Nearest Neighbours (KNN), Adaptive Boosting (AB), DT, RF, SVM, and NN, were explored for the purpose of categorizing the damage states of the building. The material properties of the masonry and the earthquake intensity were considered as the input parameters. The results from the regression models indicate that the GPR model efficiently predicts the seismic response with larger coefficients of determination and smaller root mean square error values than other models. Among the classification-based models, the RF, AB, and NN models effectively classify the damage states with accuracy levels of 92.9%, 91.1%, and 92.6%, respectively. In conclusion, the overall performance of the non-parametric models, such as GPR, NN, and RF, was found to be better than that of the parametric models.