Surrounding rock stress is a one of significant feedback information after tunnel excavation. It is also an important factor influencing the stability of the tunnel, and the premise to determine accurately the tunnel stability. For tunnel surrounding rocks stress determined by many uncertain factors, it is difficult to accurately predict. The BP neural network model for predicting on the stress of the tunnel surrounding rock stress is created. Comparative analyze the results of the surrounding rocks stress prediction on expressway tunnel by momentum gradient algorithm, L-M algorithm and bayesian regularization algorithm. Results show that the use of bayesian regularization algorithm for neural network model has higher forecast accuracy. It can be applied in engineering practice.
Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points
