On the application of machine learning techniques to derive seismic fragility curves

Abstract

Abstract Deriving the fragility curves is a key step in seismic risk assessment within the performance-based earthquake engineering framework. The objective of this study is to implement machine learning tools (i.e., classification-based tools in particular) for predicting the structural responses and the fragility curves. In this regard, ten different classification-based methods are explored: logistic regression, lasso regression, support vector machine, Naive Bayes, decision tree, random forest, linear and quadratic discriminant analyses, neural networks, and K-nearest neighbors with the structural responses resulted from the multiple strip analyses. In addition, this study examines the impact of class imbalance in training dataset, which is typical among data of structural responses, when developing classification-based models for predicting structural responses. The statistical results using the implemented dataset demonstrate that among applied methods, random forest and quadratic discriminant analysis are, respectively, preferable with the imbalanced and balanced datasets since they show the highest efficiency in predicting the structural responses. Moreover, a detailed procedure is presented on how to derive the fragility curves based on the classification-based tools. Finally, the sensitivity of the applied machine learning methods to the size of employed dataset is investigated. The results explain that logistic regression, lasso regression, and Naive Bayes are not sensitive to the size of dataset (i.e., the number of performed time history analyses); while the performance of discriminant analysis significantly depends on the size of applied dataset.