Kabiru, O. AkandeOwolabi, Taoreed O.Ssennoga, TwahaOlatunji, Sunday O.2022-06-172022-06-172014Akande, 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), 88-94.2278-0661https://nru.uncst.go.ug/handle/123456789/4016Concrete 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.enArtificial Neural NetworkCompressive Strength of ConcreteMix ProportionRobust Learning AlgorithmSupport Vector MachineArticle