Support vector machines in structural engineering: a review

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

Abstract

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.

Keywords:

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

How to Cite

Support vector machines in structural engineering: a review. (2015). Journal of Civil Engineering and Management, 21(3), 261-281. https://doi.org/10.3846/13923730.2015.1005021

Share

Published in Issue
February 26, 2015
Abstract Views
2979

View article in other formats

CrossMark check

CrossMark logo

Published

2015-02-26

Issue

Section

Review

How to Cite

Support vector machines in structural engineering: a review. (2015). Journal of Civil Engineering and Management, 21(3), 261-281. https://doi.org/10.3846/13923730.2015.1005021

Share