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.

隧道工程受多种因素的影响，导致隧道工程中较大的检测误差。为了提高隧道工程的监测效果，基于BPNN和MARS机器学习回归算法，构建了隧道工程监测与预测模型。另外，本研究设计的灰色残差BP神经网络采用序列组合，将从灰色模型获得的残差用作BP神经网络的输入数据，并将组合模型的输出作为预测结果。 。通过应用隧道表层上部周围的收敛性和拱形沉降变形的监测数据，