Research on tunnel engineering monitoring technology based on BPNN neural network and MARS machine learning regression algorithm

Tunnel engineering is affected by a variety of factors, which results in large detection errors in tunnel engineering. In order to improve the monitoring effect of tunnel engineering, based on BPNN and MARS machine learning regression algorithm, this research constructs a tunnel engineering monitoring and prediction model. Moreover, the gray residual BP neural network designed in this study uses a series combination, and the residuals obtained from the gray model are used as the input data of the BP neural network, and the output of the combined model is used as the prediction result. By applying the monitoring data of the convergence of the upper surrounding of the tunnel surface section and deformation of the arch subsidence, it is verified that the proposed method based on the combined model of BPNN and MASR can predict and analyze the tunnel deformation monitoring data very well.

Automated summary

This research constructs a tunnel engineering monitoring and prediction model by combining the BPNN and MARS machine learning regression algorithms, improving the accuracy of monitoring data. The serial design of the gray residual BP neural network enhances the accuracy of prediction results and effectively analyzes tunnel deformation monitoring data.