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.
Strengthening is often required for reinforced concrete, steel, and masonry structures or structural elements when they possess insufficient performance against external loads such as earthquakes. Recently, the use of carbon fiber-reinforced polymers (CFRP) has been considered a viable strengthening technique alternative to traditional methods. The major concern is premature debonding failure hindering the efficient use of CFRP systems. FRP anchor systems have been used to avoid this phenomenon. This paper employs a machine learning (ML)-based algorithm (support vector regression) to propose predictive models to simulate the bond-slip behavior of anchored CFRP strips externally bonded to the concrete surface. A comprehensive database was constructed using the previous reports on the bond-slip behavior of FRP-to-concrete joints anchored with CFRP strips. Afterwards, the collected database was used to train and validate the proposed models. The input parameters cover all possible factors, that is, compressive strength of concrete, width of concrete block, anchor hole diameter, anchor hole depth, number of anchors at one row, number of anchors at one column; elastic modulus, width, bonding length, and thickness of CFRP strip. The output parameters are maximum shear capacity, residual shear capacity, displacement values at peak shear, and residual shear. Results imply that the proposed models have high prediction accuracies with low error rates. Proposed models are also presented in code format to be easily incorporated into analysis software for practical use.
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