Application Pigeon Swarm Intelligent Optimisation BP Neural Network Algorithm in Railway Tunnel Construction

Due to the uncertainty and complexity of the risk factors of the urban railway tunnel project to increase the difficulty of risk analysis, so that the traditional risk assessment methods can not accurately assess the construction risk of the urban railway tunnel project. Aiming at the problems of the existing risk assessment algorithms, the construction risk assessment method of an urban railway tunnel project based on intelligent optimisation algorithm and machine learning algorithm is proposed. Firstly, for the problem of construction risk identification and assessment of municipal railway tunnel project, a tunnel construction risk identification and assessment scheme using a combination of intelligent optimization algorithm and machine learning algorithm is designed, and the principles and functions of each module of the risk assessment system are introduced; then, for the problem of risk assessment construction, a risk assessment algorithm based on the swarm intelligent optimization algorithm to improve the BP neural network is proposed; secondly, relying on the Hangzhou Secondly, relying on Xinfeng Road underground passage close to cross the underground line 9 tunnel and the side through the Hanghai intercity tunnel project in Hangzhou, the effectiveness of the construction risk assessment algorithm is verified from monitoring data and numerical simulation, and the risk control scheme is proposed in turn. The experimental results show that the risk assessment algorithm proposed in this paper effectively solves the problem of construction risk assessment of the urban railway tunnel project, and improves the prediction performance of the risk assessment algorithm, and verifies that the risk control scheme meets the construction safety requirements.

@article{Zhou2024,
  title     = {Application Pigeon Swarm Intelligent Optimisation BP Neural Network Algorithm in Railway Tunnel Construction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Feng Zhou and Hong Ye and Jie Song and Hui Guo and Peng Liu},
  doi       = {10.14569/IJACSA.2024.0151126},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151126}
}