Support vector machines in structural engineering: a review

    Abdulkadir Çevik Affiliation
    ; Ahmet Emin Kurtoğlu Affiliation
    ; Mahmut Bilgehan Affiliation
    ; Mehmet Eren Gülşan Affiliation
    ; Hasan M. Albegmprli Affiliation


Recent development in data processing systems had directed study and research of engineering towards the creation of intelligent systems to evolve models for a wide range of engineering problems. In this respect, several modeling techniques have been created to simulate various civil engineering systems. This study aims to review the studies on support vector machines (SVM) in structural engineering and investigate the usability of this machine learning based approach by providing three case studies focusing on structural engineering problems. Firstly, the concept of SVM is explained and then, the recent studies on the application of SVM in structural engineering are summarized and discussed. Next, we performed three case studies using the experimental studies provided. Applicability of SVM in structural engineering is confirmed by these case studies. The results showed that SVM is superior to various other learning techniques considering the generalization capability of produced model.

Keyword : support vector machines, statistical learning, structural engineering, ultimate load capacity, FRP reinforcement, SFRC corbels, haunched beams

How to Cite
Çevik, A., Kurtoğlu, A. E., Bilgehan, M., Gülşan, M. E., & Albegmprli, H. M. (2015). Support vector machines in structural engineering: a review. Journal of Civil Engineering and Management, 21(3), 261-281.
Published in Issue
Feb 26, 2015
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