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Multimedia Technology and Enhanced Learning. 4th EAI International Conference, ICMTEL 2022, Virtual Event, April 15-16, 2022, Proceedings

Research Article

Research on the Whole Process Quality Control Method of Water Conservancy and Hydropower Construction Based on BP Neural Network

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-18123-8_25,
        author={Mingdong Yu and Qian He},
        title={Research on the Whole Process Quality Control Method of Water Conservancy and Hydropower Construction Based on BP Neural Network},
        proceedings={Multimedia Technology and Enhanced Learning. 4th EAI International Conference, ICMTEL 2022, Virtual Event, April 15-16, 2022, Proceedings},
        proceedings_a={ICMTEL},
        year={2022},
        month={10},
        keywords={BP neural network Water conservancy and hydropower projects Whole process quality control Data acquisition},
        doi={10.1007/978-3-031-18123-8_25}
    }
    
  • Mingdong Yu
    Qian He
    Year: 2022
    Research on the Whole Process Quality Control Method of Water Conservancy and Hydropower Construction Based on BP Neural Network
    ICMTEL
    Springer
    DOI: 10.1007/978-3-031-18123-8_25
Mingdong Yu1,*, Qian He1
  • 1: Department of Civil and Hydraulic Engineering Institute, Xichang University
*Contact email: xccyymd@126.com

Abstract

The traditional quality control method of the whole construction process is lack of process quality evaluation, which leads to the low overall quality score of the project after the completion of the project construction. A whole process quality control method of water conservancy and hydropower construction based on BP neural network is designed. First, set up water conservancy and hydropower construction site monitoring, and design data acquisition equipment for the whole construction process according to different construction positions; According to the acquisition equipment set above, collect the water conservancy and hydropower construction data at different locations and use them as the control data; On this basis, the framework structure of BP neural network is established, the control network level is determined, the back propagation function is determined, and the collected data of the whole process of water conservancy and hydropower construction are trained to complete the whole process quality control of water conservancy and hydropower construction. The example analysis results show that the whole process quality control of the project using the design method can effectively improve the construction quality.

Keywords
BP neural network Water conservancy and hydropower projects Whole process quality control Data acquisition
Published
2022-10-19
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-18123-8_25
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