Study on Mechanical Properties of Corroded Reinforced Concrete Using Support Vector Machines

Shao‐Horn, Yang; Chen, Fang; Yuan, Zhi Jie

doi:10.4028/www.scientific.net/amm.578-579.1556

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…However, they introduced SVM as an effective tool for structural health monitoring. There are many other studies in various domains that report the use of SVM as an effective approach [23][24][25][26][27][28][29][30]. Table 1 summarizes some of other SVM-related studies in SHM, which investigated kernels to develop more effective algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study

Hoshyar

Rashidi

et al. 2023

Remote Sensing

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Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study

Hoshyar

Rashidi

et al. 2023

Remote Sensing

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“…Yang et al (2014) investigated the mechanical properties of corroded concrete and performed tests on specimens under repeated loads. Deflection and maximum crack with parameters were predicted using least squares support vector machines (LS-SVM) [20]. Cao et al (2013) presented a predictive SVM based model for elastic modulus of SCC [21].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

“…Zhang and Song (2012) employed SVM to predict the residual mechanical characteristics of fly ash concrete specimens exposed acidic environment [19]. Yang et al (2014) investigated the mechanical properties of corroded concrete and performed tests on specimens under repeated loads. Deflection and maximum crack with parameters were predicted using least squares support vector machines (LS-SVM) [20].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Lifli Betonarme Kısa Konsolların Kesme Dayanımının Destek Vektör Makineleri ile Tahmini

Kurtoğlu¹

2018

Cumhuriyet Science Journal

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Precast corbels are commonly preferred structural members in industrial buildings. In this study, a novel application of support vector machines (SVM) is employed for the prediction of ultimate shear strength of fiber reinforced corbels, for the first time in literature. SVM models are developed and analyzed using a database of available test results in literature. Predictions of the selected model are compared against the test results and those of available model proposed by Fattuhi (1994). Proposed model has the capability to predict the shear strength of both steel fiber reinforced concrete (SFRC) and glass fiber reinforced concrete (GFRC) corbels. Additionally, a parametric study with a wide range of variables is carried out to test the effect of each parameter on the shear strength. The results confirm the high prediction capacity of proposed model.

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“…The mechanical properties of corroded reinforced concrete are investigated by Yang et al (2014) who conducted tests on specimens under repeated loads. Using support vector machines, the deflection and maximum crack width parameters are predicted and compared to those of test results.…”

Section: Predicting the Concrete Corrosionmentioning

confidence: 99%

Support Vector Machines in Structural Engineering: A Review

Çevik

Kurtoğlu

Bilgehan

et al. 2015

Journal of Civil Engineering and Management

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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.

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Study on Mechanical Properties of Corroded Reinforced Concrete Using Support Vector Machines

Cited by 5 publications

References 7 publications

Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study

Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study

Lifli Betonarme Kısa Konsolların Kesme Dayanımının Destek Vektör Makineleri ile Tahmini

Support Vector Machines in Structural Engineering: A Review

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