Research on loess tunnel stability analysis based on artificial intelligence

Wuzhen Wang; Yutai Sun; Haifeng Pan; Yi Li

doi:10.1117/12.3025440

28 February 2024 Research on loess tunnel stability analysis based on artificial intelligence

Wuzhen Wang, Yutai Sun, Haifeng Pan, Yi Li

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 1307141 (2024) https://doi.org/10.1117/12.3025440
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wuzhen Wang, Yutai Sun, Haifeng Pan, and Yi Li "Research on loess tunnel stability analysis based on artificial intelligence", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 1307141 (28 February 2024); https://doi.org/10.1117/12.3025440

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KEYWORDS

Education and training

Artificial neural networks

Artificial intelligence

Analytical research

Design

Engineering

Neural networks

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