Abstract
The determination of concrete mix ratio is known as the concrete mix design which involves many theories and practice knowledge and must satisfy some requirements. In order to get high performance concrete, the mix design should be tuned using optimization. However, a lot of concrete experiments are needed to correct models which are very time-consuming and expensive. In this paper, a neural network surrogate model based method is proposed to optimize concrete mix design. This approach focuses on the optimization of compressive strength. Experimental results manifest that the optimum design which achieves high compressive strength can be found by employing the novel approach.
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Wang, L., Yang, B., Zhang, N. (2013). Constructing Surrogate Model for Optimum Concrete Mixtures Using Neural Network. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_61
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DOI: https://doi.org/10.1007/978-3-642-39068-5_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
Online ISBN: 978-3-642-39068-5
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