Evaluation of Water Inrush Danger in Railway Tunnel Based on PSO-RBF Neural Network Model.

Water inrush is the most frequent disaster in the process of railway tunnel construction. In order to effectively prevent water inrush accidents, reduce risk in tunnel construction and ensure the safety of construction personnel, this paper, on the basis of existing researches, selects 10 core indexes as the judgment basis for the occurrence of water inrush accident, collects 50 groups of typical tunnel water inrush cases data as the research samples of water inrush risk assessment, uses particle swarm optimization algorithm (PSO) to optimize radial basis function neural network (RBF), trains and tests the sample data, and establishes the PSO-RBF neural network railway tunnel water inrush risk assessment model. Finally, the model is applied to Jingjiashan Tunnel to verify its practicability. The case study shows that the PSO-RBF model can accurately determine the risk of water inrush in Jingjiashan Tunnel, and the PSO-RBF neural network model has higher accuracy and faster iterative speed in comparison with the RBF neural network improved by the gradient descent method. [ABSTRACT FROM AUTHOR]