Research Article Classification of Earthquake-Induced Damage for R/C Slab Column Frames Using Multiclass SVM and Its Combination with MLP Neural Network Ali Kia and Serhan Sensoy Department of Civil Engineering, Eastern Mediterranean University, Gazimagusa, Mersin 10, Turkey Correspondence should be addressed to Ali Kia; ali kia cien@yahoo.com Received 4 March 2014; Revised 3 June 2014; Accepted 5 June 2014; Published 23 July 2014 Academic Editor: Xuejun Xie Copyright © 2014 A. Kia and S. Sensoy. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 classifcation: 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 diferent ground motion records were selected and scaled to ten diferent levels of peak ground acceleration (PGA). Tus, 600 obtained data from the numerical simulations were applied to M- SVM and MM-SVM in order to predict the global damage classifcation of samples based on park and Ang damage index. Amongst the four diferent kernel tricks, the Gaussian function was determined as an efcient kernel trick using the maximum total accuracy method of test data. By comparing the obtained results from M-SVM and MM-SVM, the total classifcation accuracy of MM-SVM is more than M-SVM and it is accurate and reliable for global damage classifcation of R/C slab column frames. Furthermore, the proposed combined model is able to classify the classes with low members. 1. Introduction and Background Artifcial neural networks (ANNs) are one of the popu- lar computational models applied widely throughout the sciences. Also they are specially used in many felds of civil engineering like materials strength prediction, thermo- graphic inspection of electrical installations within buildings, trafc management and transportation systems, forecast water pressure in pipes, and so forth. Generally ANNs are used to solve complex problems by considering efective indices and establish a good relationship between the input and output parameters. Moreover, these networks can be applied in classifcation problems. Te frst classifcation algorithm was presented by Fisher [1]. In this algorithm, minimizing the classifcation error of train data was eval- uated as an optimization criterion. Tis method has been used in many classifcation algorithms, yet there are some problems encountered mainly the generalization properties of the classifers, which are not directly involved in the cost function. Also for doing the training process, determining the structure of the network was not easy. As an example, to determine the optimum number of neurons in the hidden layers of the multilayer perceptron (MLP) neural networks or the number of Gaussian functions in radial basis function (RBF) neural networks are difcult and time consuming. Cortes and Vapnik [2] introduced a new learning statistical theory which leads to presenting the support vector machines (SVMs). Te signifcant features of these networks are their ability to minimize the classifcation errors, maximize the geometric margins between classes, design the classifers with maximum generalization, and automatically determine the architecture of network for classifers and modeling the nonlinear separator functions using nonlinear cores. In recent years, several diferent neural networks such as SVM have been applied in diferent branches of civil engi- neering. In a tunnel construction, an intelligent controlling system was presented by Jun et al. [3]. Tis system needed to recognize the geophysical parameters to fnd the optimum Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 734072, 14 pages http://dx.doi.org/10.1155/2014/734072