Knowledge-based intelligence method for controlling segment floating by optimizing shield tail grouting parameters

Extensive segment floating will result in segment dislocation, crack, and leakage, posing significant risks of engineering accidents. It is important to control the segment floating based on adjusting shield operational parameters finely. A knowledge-based intelligence method designed for controlling segment floating is proposed in this study. Leveraging prior knowledge in segment floating, the framework of the intelligence method is constructed. This framework consists of a segment floating prediction model along with two auxiliary models. The segment floating prediction model considers the spatial and temporal characteristics of the shield operational parameters, including the early activation of the shield excavation parameters and the hysteretic nature of tail grouting parameters. The segment floating prediction model is the basis of the knowledge-based intelligence method. A multi-ring optimization strategy is designed to solve the conflict between the optimization results of adjacent rings. The case study shows that the segment floating prediction model has high prediction accuracy due to consideration of the spatial and temporal characteristics of the shield operational parameters. Considering the performance and computation cost, the optimal parameter configuration is figured out.

In this paper, we proposed a knowledge-based intelligence method for controlling segment floating based on adjusting and optimizing shield tail grouting parameters. Based on prior knowledge in controlling segment floating, we construct the framework of the intelligence method, comprising a main model and two auxiliary models. The main model serves as a segment floating prediction model, while the two auxiliary models are the torque and thrust predicting model and the grouting pressure predicting model, respectively. The segment floating prediction model considers the intricate interplay between active control and passive response parameters, leveraging geometrical, geological, and shield operational parameters to predict segment floating behavior accurately. The segment floating prediction model also considers the spatial and temporal characteristics of the shield operational parameters, including the early activation of the shield excavation parameters and the hysteretic nature of tail grouting parameters. To address the conflicts between the optimization results of adjacent rings, a multi-rings optimization strategy is developed. The influence of the optimization algorithms and the multi-rings optimization strategy is investigated with filed data.

This paper ” Knowledge-based intelligence method for controlling segment floating by optimizing shield tail grouting parameters” was published in Smart Construction.
Wang, G., Fang, Q., Wang, J., Zheng, G., Li, Q., Wei, J., 2025. Knowledge-based intelligence method for controlling segment floating by optimizing shield tail grouting parameters. Smart Construction. https://doi.org/10.55092/sc20250008