Knowledge-based data-driven prediction of shield tail clearance under karst geological condition

Abstract

Precise control of shield tail clearance is a critical factor influencing the safety and quality of shield tunneling construction. Although various methods exist for accurately measuring shield tail clearance, predictive capabilities remain insufficient. This study is based on a shield tunnel project in the karst region of Longgang, Shenzhen, China. By integrating geological parameters obtained from advanced geological prediction with shield construction monitoring data, a predictive calculation method for shield tail clearance is developed, grounded in the spatial relationship between the shield machine and the pipe segments. A knowledge-based data-driven prediction approach is proposed using a Transformer-LSTM deep learning model. Case analysis demonstrates that the proposed Transformer-LSTM model consistently outperformed baseline models such as GRU, LSTM, and pure Transformer. The predicted R² values for the four positions of the shield tail—top, bottom, left, and right—reached 0.990, 0.901, 0.976, and 0.908, respectively, while error indicators (MAE, RMSE, and MAPE) were also minimized. These results confirm that the proposed hybrid approach effectively captures both global dependencies and temporal dynamics, enabling accurate prediction of shield tail clearance and offering practical engineering significance for guiding shield tunneling construction.

Keywords

Shield tunnel / Geological prediction / Shield tail clearance / Data-knowledge joint-driven / Deep learning

CRediT authorship contribution statement

Wengang Zhang: Writing - original draft, Supervision, Methodology, Conceptualization. Han Han: Formal analysis, Data curation. Weixin Sun: Validation, Supervision, Methodology, Conceptualization. Yunhao Wang: Validation, Methodology. Zhihao Wu: Resources, Project administration. Peng Xiao: Resources, Project administration, Data curation. Yumiao Yan: Software, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The fist author of this paper Wengang Zhang is an Associate Editor of this Journal, and was not involved in the editorial review or the decision to publish this article.

Acknowledgements

The authors are grateful to the financial support from Chongqing Railway Investment Group Co., Ltd. (CSTB2022TIAD-KPX0101), China Railway Group Co., Ltd. (N2023G045), Guangzhou Metro Group Co., Ltd. (JT204-100111- 23001).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2025.102221.

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