Earthquake-Induced Structural Damage Classification Algorithm

A classification algorithm is developed for evaluating the damage state of buildings subjected to earthquakes. Nonlinear response history analysis is used to generate the time histories of each building subjected to each earthquake. This report summarizes the analysis procedure used to extract data and describes the different classification algorithms that are developed to predict damage state. Support vector machines (SVM), multinomial logistic regression and k-nearest neighbors (KNN) are considered for the classification algorithm. Features are selected from building parameters, ground motion parameters and combinations of both. Four damage states are estimated: minimal damage, moderate damage, severe damage and collapse. The support vector machine classifier yields the most accurate results for evaluating the damage state with an overall accuracy of 71.2%. This is in part due to the highly nonlinear nature of this problem. The accuracy of classifying a damage state with a misclassification tolerance of ±1 is 95.2%.