Evaluation Analysis and Comparison Prediction of Initial Lining Concrete Stress in Tunnels Based on BP Neural Network
In order to determine the best prediction induced initial lining concrete stress model for a specific section of Yangjiashan mega section tunnel.In this paper,the BP neural network method is adopted to train the network using five methods with the initial lining concrete stress monitoring data of the section as the input value,and to analyse the differ-ence between the predicted and real values of the stress,giving the distribution of the prediction error,as well as the performance,validation and testing curves of the network during the training process.A comprehensive evaluation of the five training methods is also carried out based on the analysis method of multi-objective optimisation problem.The re-sults show that the order from the best to the worst is as follows:Traincgp>Traingdx>Trainbfg>Traincgb>Trainlm,and Ploak-Ribiere conjugate gradient method is the best,and the prediction accuracy reaches more than 98%,so it can be used by Ploak-Ribiere conjugate gradient method during the subsequent tunnel Therefore,the BP neural net-work can be trained by the Ploak-Ribiere conjugate gradient method in the excavation process to effectively predict the initial support and peripheral rock stresses generated in a specific section of the tunnel to ensure the construction safety.
super large tunnelinitial lining concrete stressBP neural networkcomparison of training methods
NETL
NSTL
万方数据
