Influence of soil parameters on dynamic compaction: numerical analysis and predictive modeling using GA-optimized BP neural networks - Yu Zhang; Xueshui Chen; Huakang Ge; Zhigang Guo; Xu Li (Frontiers in Materials)
This study analyses the effect of soil parameters (angle of internal friction, cohesion, modulus of elasticity, Poisson’s ratio, density) on the amount of ramming settlement of rammed reinforced foundations and proposes a prediction model for the depth of ramming reinforcement based on GA-BP neural network. Based on the finite element method, a numerical model of dynamic consolidation foundation is established, and the model is verified by field test results. Orthogonal experimental design and single factor analysis were used to quantify the influence of each parameter on the compaction volume. In order to improve the prediction accuracy, this paper introduces genetic algorithm (GA) to optimize the BP neural network model, constructs a multi-factor dynamic compaction prediction model, and compares it with the traditional BP model. The results show that the compaction rate is most sensitive to the internal friction Angle and cohesive force. Compared with the traditional BP model, GA-BP model has better prediction accuracy and generalization ability, and the fitting accuracy reaches 0.974. GA optimization improves the convergence speed of the model and the ability to solve the global optimal solution. The GA-BP model used in this paper provides a high-precision tool for the prediction of dynamic compaction foundation treatment and has important engineering application value.