Abstract
Due to the complex interaction of components, the design of concrete mix becomes difficult. This paper presents an artificial neural network (ANN) based visualization method to optimize the concrete mix design. It aims to minimize the cost of concrete such that all desired qualities are maintained. The procedure can be described as mapping data of concrete mix from multidimensional space to a two-dimensional plane with an ANN model, and then generating concrete property contours on this plane. The optimized mix proportions region can be determined intuitively based on the contours distribution. By means of an inversion mapping algorithm, the optimal point in this region can be mapped inversely to the original multidimensional space. Practical production test results show that good concrete mixes, which agree with the concrete compressive strength criterion and have lower cost, can be obtained. Application of this method can contribute significant benefits to the commercial concrete companies.
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© 2009 Springer-Verlag Berlin Heidelberg
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Shi, B., Yan, L., Guo, Q. (2009). Application of Visualization Method to Concrete Mix Optimization. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_5
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DOI: https://doi.org/10.1007/978-3-642-01513-7_5
Publisher Name: Springer, Berlin, Heidelberg
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