Computational Technology Resources - CCP

Paper IV.7

Advanced Control for VC-Value of Roller Compacted Dam Concrete using Artificial Neural Networks

M. Matsushima* and N. Yasuda+

*Tokyo Electric Power Services Company, Tokyo, Japan
+Tokyo Electric Power Company, Kawasaki, Japan

doi:10.4203/ccp.53.4.7

M. Matsushima, N. Yasuda, "Advanced Control for VC-Value of Roller Compacted Dam Concrete using Artificial Neural Networks", in B.H.V. Topping, (Editor), "Advances in Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, pp 207-214, 1998. doi:10.4203/ccp.53.4.7

Abstract

In this paper, an advanced method of quality control for the mixing of roller-compacted dam (RCD) concrete is presented. The method to predict the workability function VC value from the input parameters of mix proportion and mixing energy using a neural network. A successful neural network system for prediction of VC value was developed using experimental data. According to sensitivity analysis, the parameters surface moisture of fine aggregate, volume of fine aggregate, water volume and power consumption are shown to be important parameters which have a significant effect on VC value.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 53 ADVANCES IN ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping Paper IV.7 Advanced Control for VC-Value of Roller Compacted Dam Concrete using Artificial Neural Networks M. Matsushima* and N. Yasuda+ *Tokyo Electric Power Services Company, Tokyo, Japan +Tokyo Electric Power Company, Kawasaki, Japan doi:10.4203/ccp.53.4.7 purchase the full-text of this paper Full Bibliographic Reference for this paper M. Matsushima, N. Yasuda, "Advanced Control for VC-Value of Roller Compacted Dam Concrete using Artificial Neural Networks", in B.H.V. Topping, (Editor), "Advances in Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, pp 207-214, 1998. doi:10.4203/ccp.53.4.7 Abstract In this paper, an advanced method of quality control for the mixing of roller-compacted dam (RCD) concrete is presented. The method to predict the workability function VC value from the input parameters of mix proportion and mixing energy using a neural network. A successful neural network system for prediction of VC value was developed using experimental data. According to sensitivity analysis, the parameters surface moisture of fine aggregate, volume of fine aggregate, water volume and power consumption are shown to be important parameters which have a significant effect on VC value. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £75 +P&P)
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