An Intelligent Model Mixing Physics Mechanism and Field Data for Ground Settlement Prediction during Pipe-Roofing Excavation

The pipe-roofing method is a new pre-supporting large-section excavation technique. Rapid and effective prediction of ground settlement for excavation adjustment and decision-making is significant to ensure safety during pipe-roofing excavation construction, especially in saturated soft soil areas. This paper develops an intelligent model mixing the physics mechanism and field data for forecasting the ground settlement caused by pipe-roofing excavation. To begin with, a data set containing features regarding physical mechanisms and field monitoring items is well prepared. For one thing, there are three kinds of physics-based features to be taken into account, including excavation and supporting condition, soil condition, and condition of steel pipe-roofing and supporting system. Based on the relationship between construction time sequence and intrinsic physical mechanism, a finite-element analysis model is introduced, which can transfer spatial structure features into data characters. For another, field monitoring data, including the distance of the excavation surface and ground settlement, also need pretreatment. After that, a proper machine learning algorithm is trained to fully learn the prepared data set where it contains the time sequence. To verify the effectiveness and practicability, the proposed model mixing physics mechanism and field data is retrospectively applied to a recent field monitoring case of Guiqiao Road Station on Shanghai Metro Line No. 14.