Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete

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dc.contributor.authorKabiru, O. Akande
dc.contributor.authorOwolabi, Taoreed O.
dc.contributor.authorSsennoga, Twaha
dc.contributor.authorOlatunji, Sunday O.
dc.date.accessioned2022-06-17T12:07:38Z
dc.date.available2022-06-17T12:07:38Z
dc.date.issued2014
dc.description.abstractConcrete compressive strength prediction is very important in structure and building design, particularly in specifying the quality and measuring performance of concrete as well as determination of its mix proportion. The conventional method of determining the strength of concrete is complicated and time consuming hence artificial neural network (ANN) is widely proposed in lieu of this method. However, ANN is an unstable predictor due to the presence of local minima in its optimization objective. Hence, in this paper we have studied the performance of support vector machine (SVM), a stable and robust learning algorithm, in concrete strength prediction and compare the result to that of ANN. It is found that SVM displayed a slightly better performance compared to ANN and is highly stable.en_US
dc.identifier.citationAkande, K. O., Owolabi, T. O., Twaha, S., & Olatunji, S. O. (2014). Performance comparison of SVM and ANN in predicting compressive strength of concrete. IOSR Journal of Computer Engineering, 16(5), 88-94.en_US
dc.identifier.issn2278-0661
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4016
dc.language.isoenen_US
dc.publisherIOSR Journal of Computer Engineeringen_US
dc.subjectArtificial Neural Networken_US
dc.subjectCompressive Strength of Concreteen_US
dc.subjectMix Proportionen_US
dc.subjectRobust Learning Algorithmen_US
dc.subjectSupport Vector Machineen_US
dc.titlePerformance Comparison of SVM and ANN in Predicting Compressive Strength of Concreteen_US
dc.typeArticleen_US
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