Research on Treatment of Retaining Wall Foundation with Geosynthetics Based on BP Neural Network

Abstract

Through the long-term load creep test of CE131 geonet and SD L25 retaining wall foundation, which are widely used in reinforced earth engineering, a large number of experimental data are obtained. On this basis, the least-squares and BP neural network are used to predict its creep variables. The principle of least squares is to find a curve in the curve family to fit the experimental data. From the sum of the squared errors σ = 0. 001 16, the fitting accuracy is higher. The BP neural network has adaptive learning and memory capabilities, especially the three-layer BP neural network model. The maximum error between the predicted value and the actual value is 0.91%, which is a lot better than the error of the least square 3.4%. This method Found a new way for creep prediction.