1 st International Conference on Human, Architecture, Civil Engineering and City ICOHACC 2015 May 2015, Tabriz, Iran 1 Comparison of LS-SVM, ANFIS, RBFNN and LMNN for Damage Detection of Structures Ramin Ghiasi 1 *, Mohammad Reza Ghasemi 2 , Yaser Binaee 3 , Hamid Reza Ghaffari 4 PhD Candidate in Civil Engineering, Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran (rghiasi@pgs.usb.ac.ir) Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran (mrghasemi@hamoon.usb.ac.ir) PhD Candidate in Computer Engineering,Islamic Azad University of Ferdows, Ferdows, Iran (yaser.binaee@gmail.com) Assistant Professor, Islamic Azad University of Ferdows, Ferdows, Iran (hamid_ghaffari@yahoo.com) Abstract Over the past two decades, a large volume of research has been carried out in the area of damage detection of structural systems and the field of Structural Health Monitoring (SHM) has become a major field of research. In this study, structural damage detection is performed incorporating several methods of Artificial Intelligence (AI) including Least Square Support Vector Machines (LS-SVMs), Adaptive Neural-Fuzzy Inference System (ANFIS), radial basis function neural network (RBFNN), Large Margin Nearest Neighbors (LMNN) and the comparative results are presented. By considering dynamic behavior of a structure as input variables, four AI methods are constructed, trained and tested to detect the location and severity of damage in civil structures. The results indicate that LS-SVM models have better performance in predicting location/severity of damage than other methods. Keywords:Damage detection, LS-SVM, ANFIS, RBFNN, LMNN