A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Research on loess tunnel stability analysis based on artificial intelligence
This study delves into the application of artificial intelligence in the stability analysis of loess tunnels, with a focus on the principles, structure, advantages, and disadvantages of the BP neural network model and the optimization of this network using genetic algorithms. The research employs a genetic algorithm-optimized BP neural network method, utilizing numerical simulation results as training samples. Key factors such as tunnel radius, distance between tunnels, angle, deformation modulus of the surrounding rock, cohesion, internal friction angle, Poisson's ratio, and density are selected as input parameters for the neural network. The study successfully constructs a GA-BP neural network prediction model, which demonstrates excellent performance in convergence speed and prediction accuracy. This achievement not only validates the effectiveness of genetic algorithm-optimized BP neural networks in loess tunnel stability analysis but also offers a new analytical and predictive tool for related fields. The application of this model allows for more accurate prediction and analysis of tunnel stability, providing scientific decision support for tunnel design and construction, thereby enhancing the safety and reliability of tunnel engineering.
Research on loess tunnel stability analysis based on artificial intelligence
This study delves into the application of artificial intelligence in the stability analysis of loess tunnels, with a focus on the principles, structure, advantages, and disadvantages of the BP neural network model and the optimization of this network using genetic algorithms. The research employs a genetic algorithm-optimized BP neural network method, utilizing numerical simulation results as training samples. Key factors such as tunnel radius, distance between tunnels, angle, deformation modulus of the surrounding rock, cohesion, internal friction angle, Poisson's ratio, and density are selected as input parameters for the neural network. The study successfully constructs a GA-BP neural network prediction model, which demonstrates excellent performance in convergence speed and prediction accuracy. This achievement not only validates the effectiveness of genetic algorithm-optimized BP neural networks in loess tunnel stability analysis but also offers a new analytical and predictive tool for related fields. The application of this model allows for more accurate prediction and analysis of tunnel stability, providing scientific decision support for tunnel design and construction, thereby enhancing the safety and reliability of tunnel engineering.
Research on loess tunnel stability analysis based on artificial intelligence
Wei, Yonghe (editor) / Liu, Fengli (editor) / Wang, Wuzhen (author) / Sun, Yutai (author) / Pan, Haifeng (author) / Li, Yi (author)
International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023) ; 2023 ; Shenyang, China
Proc. SPIE ; 13071
2024-02-28
Conference paper
Electronic Resource
English
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