Classification of Earthquake-Induced Damage for R/C Slab Column Frames Using Multiclass SVM and Its Combination with MLP Neural Network

Nonlinear time history analysis (NTHA) is an important engineering method in order to evaluate the seismic vulnerability of buildings under earthquake loads. However, it is time consuming and requires complex calculations and a high memory machine. In this study, two networks were used for damage classification: multiclass support vector machine (M-SVM) and combination of multilayer perceptron neural network with M-SVM (MM-SVM). In order to collect data, three frames of R/C slab column frame buildings with wide beams in slab were considered. For NTHA, twenty different ground motion records were selected and scaled to ten different levels of peak ground acceleration (PGA). Thus, 600 obtained data from the numerical simulations were applied to M-SVM and MM-SVM in order to predict the global damage classification of samples based on park and Ang damage index. Amongst the four different kernel tricks, the Gaussian function was determined as an efficient kernel trick using the maximum total accuracy method of test data. By comparing the obtained results from M-SVM and MM-SVM, the total classification accuracy of MM-SVM is more than M-SVM and it is accurate and reliable for global damage classification of R/C slab column frames. Furthermore, the proposed combined model is able to classify the classes with low members.