Parameter Sensitivity Analysis on Deformation of Composite Soil-Nailed Wall Using Artificial Neural Networks and Orthogonal Experiment

Based on the back-propagation algorithm of artificial neural networks (ANNs), this paper establishes an intelligent model, which is used to predict the maximum lateral displacement of composite soil-nailed wall. Some parameters, such as soil cohesive strength, soil friction angle, prestress of anchor cable, soil-nail spacing, soil-nail diameter, soil-nail length, and other factors, are considered in the model. Combined with the in situ test data of composite soil-nail wall reinforcement engineering, the network is trained and the errors are analyzed. Thus it is demonstrated that the method is applicable and feasible in predicting lateral displacement of excavation retained by composite soil-nailed wall. Extended calculations are conducted by using the well-trained intelligent forecast model. Through application of orthogonal table test theory, 25 sets of tests are designed to analyze the sensitivity of factors affecting the maximum lateral displacement of composite soil-nailing wall. The results show that the sensitivity of factors affecting the maximum lateral displacement of composite soil nailing wall, in a descending order, are prestress of anchor cable, soil friction angle, soil cohesion strength, soil-nail spacing, soil-nail length, and soil-nail diameter. The results can provide important reference for the same reinforcement engineering.