Article
BIM Model Design of Deep Foundation Pit Engineering Based on BP Network
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Abstract
It is of great significance to predict the multistage deformation of the foundation pit. A new neural network method is proposed in this paper. The disadvantages of the BP neural network multistage forecast method are discussed. A multistage recursive neural network model for foundation deformation prediction is established. They are taking a deep foundation pit project in the soft soil area as an example. The multistage deformation prediction method is verified in this paper. This new detection technique is feasible. This method can also be used for multistage forecasting.
Keywords
Deep foundation pit, Recursive neural network, Multi-step deformation prediction, Engineering design, 92B20
Citation
Chenghui, W., Hao, C., & Di, W. (2023). BIM model design of deep foundation pit engineering based on BP network. Applied Mathematics and Nonlinear Sciences, 8(1), 2607–2616. https://doi.org/10.2478/amns.2023.1.00441
W. Chenghui, C. Hao and W. Di, “BIM model design of deep foundation pit engineering based on BP network,” Applied Mathematics and Nonlinear Sciences, vol. 8, no. 1, pp. 2607–2616, 2023, doi: 10.2478/amns.2023.1.00441.
Chenghui W, Hao C, Di W. BIM model design of deep foundation pit engineering based on BP network. Applied Mathematics and Nonlinear Sciences. 2023;8(1):2607–2616. doi:10.2478/amns.2023.1.00441.
Chenghui, W., Hao, C. and Di, W. (2023), ‘BIM model design of deep foundation pit engineering based on BP network’, Applied Mathematics and Nonlinear Sciences, 8(1), pp. 2607–2616. Available at: https://doi.org/10.2478/amns.2023.1.00441.
Chenghui, Wei, et al. “BIM Model Design of Deep Foundation Pit Engineering Based on BP Network.” Applied Mathematics and Nonlinear Sciences, vol. 8, no. 1, 2023, pp. 2607–2616. https://doi.org/10.2478/amns.2023.1.00441.
Chenghui, Wei, Chen Hao, and Wu Di. “BIM Model Design of Deep Foundation Pit Engineering Based on BP Network.” Applied Mathematics and Nonlinear Sciences 8, no. 1 (2023): 2607–2616. https://doi.org/10.2478/amns.2023.1.00441.
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Published by: Engineering Journals


