Research on Dynamic Prediction of Multi-Index Variables During Foundation Excavation Based on the Genetic Algorithm

A hybrid model of genetic algorithm combined with residual network (the GA-ResNN dynamic prediction model) for multi-index variables during deep foundation excavation and the construction risk assessment method were established by combining the back propagation (BP) artificial neural network, genetic algorithm (GA), and residual network (ResNN) to address the problems of low training efficiency of existing machine learning prediction models, the possibility of a single algorithm falling into local optima, and inability to converge. An intelligent early warning platform for excavation construction risks was developed. Research showed that the proposed GA-ResNN dynamic prediction model was of better prediction accuracy compared with the BP neural network model and GA-BP network model and could realize quantitative prediction and qualitative risk level evaluation. The intelligent warning platform for foundation excavation construction risks could present the prediction curve and warning threshold, which could improve the intelligent management and risk control level for engineering projects of foundation excavation.