Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete
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Date
2014
Journal Title
Journal ISSN
Volume Title
Publisher
IOSR Journal of Computer Engineering
Abstract
Concrete 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.
Description
Keywords
Artificial Neural Network, Compressive Strength of Concrete, Mix Proportion, Robust Learning Algorithm, Support Vector Machine
Citation
Akande, 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.