Field Test and Data Simulation of Foundation Pit Support System Based on BP Neural Network

Abstract

In view of the neural network problem, a field test and data simulation study of the root pit support system is proposed. First, in some difficult cases, the BP algorithm is usually due to the low level of training, thus changing the tuition and fees. Second, the BP algorithm can mix heavy objects with special effects, but the gradient process will generate local minima, so the minimum error cannot be guaranteed. The experimental response and simulation data of the BP neural network foundation pit support system is analyzed, and all experimental data are recorded. Finally, from an empirical point of view, the results brought by the field test of the central support system are determined and ultimately, promote the development of human life. The genetics-based estimation algorithm reproduces the neural network, the estimation time is 10 s, the prediction error root floats between −0.05–0.05 mm, the maximum correlation near error is 0.36%, and the approximate IA value is higher than 0.9.