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The knowledge modeling system of ready-mixed concrete enterprise and artificial intelligence with ANN-GA for manufacturing production

Author

Listed:
  • Jia-Bei Yu

    (Sichuan University)

  • Yang Yu

    (Sichuan University)

  • Lin-Na Wang
  • Ze Yuan

    (Sichuan University)

  • Xu Ji

    (Sichuan University)

Abstract

Based on the characteristics of ready-mixed concrete enterprises, this paper puts forward that knowledge management (KM) is an effective way to contribute to enterprise production and operation. The knowledge content and relevant models of concrete enterprises are proposed, including advanced enterprise management, decision support for production operation, production and operation cost, and marketing-customer relationship. Afterwards knowledge contents are divided into static, strategic and reasoning knowledge. Besides knowledge unified expression is put forward accordingly. In addition, the KM system for process ready-mixed concrete enterprises management is established to facilitate effective production processing. As part of exploratory study, artificial neural network coupled with genetic algorithm (ANN-GA) as knowledge mining technology is applied in KM system to predict the 28-day compressive strength in concrete enterprises. The results shows that compared to back-propagation artificial neural network, the convergence rate of ANN-GA algorithm has been significantly improved and almost all the relative errors of predicted compressive strength of concrete C30 are within 3 %. It not only confirms the validity of the models, but also proves that ANN-GA algorithm is an effective knowledge mining method applied in concrete industry.

Suggested Citation

  • Jia-Bei Yu & Yang Yu & Lin-Na Wang & Ze Yuan & Xu Ji, 2016. "The knowledge modeling system of ready-mixed concrete enterprise and artificial intelligence with ANN-GA for manufacturing production," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 905-914, August.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:4:d:10.1007_s10845-014-0923-6
    DOI: 10.1007/s10845-014-0923-6
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    References listed on IDEAS

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    1. Chad Syverson, 2008. "Markets: Ready-Mixed Concrete," Journal of Economic Perspectives, American Economic Association, vol. 22(1), pages 217-234, Winter.
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    Cited by:

    1. Huifeng Su & Renzhuang Li & Ming Yang, 2021. "An experimental study of modified physical performance test of low-temperature epoxy grouting material for grouting joints with tenon and mortise," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 667-677, March.
    2. Nicola Albarella & Dario Giuseppe Lui & Alberto Petrillo & Stefania Santini, 2023. "A Hybrid Deep Reinforcement Learning and Optimal Control Architecture for Autonomous Highway Driving," Energies, MDPI, vol. 16(8), pages 1-19, April.

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