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Study on Grouped Prediction of Shield Posture Based on EMD-GRU Model with Fixed Component Number
(1. The Third Engineering Co.,Ltd of China Railway Seventh Group, Xi′an 710000; 2. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering (Hohai University), Nanjing 210098; 3. School of Civil Engineering, Sun Yat-sen University, Zhuhai 519082; 4. Key Laboratory for Tunnel Engineering, Guangzhou 510275)
Abstract Excessive shield posture deviations can severely compromise the safety of shield construction and project quality. To accurately predict shield posture deviations and provide decision support for early correction, a machine learning algorithm combining Empirical Mode Decomposition (EMD) with fixed component number and Gate Recurrent Unit (GRU) is proposed for grouped prediction of shield posture deviations in different directions. The results show that EMD can decompose irregular shield posture deviation signals into several regular components, and GRU can extract temporal dependencies in the input data, enabling precise prediction of shield posture deviations in different directions. Using a fixed number of EMD decomposition components and grouped prediction method ensures the independence of the prediction and training sets. After comparison with other models, the EMD-GRU model proved to be the optimal model. The analysis of the effects of different input parameter combinations on prediction results verifies the interdependence of posture deviations in the same direction (horizontal or vertical).
Abstract：
Excessive shield posture deviations can severely compromise the safety of shield construction and project quality. To accurately predict shield posture deviations and provide decision support for early correction, a machine learning algorithm combining Empirical Mode Decomposition (EMD) with fixed component number and Gate Recurrent Unit (GRU) is proposed for grouped prediction of shield posture deviations in different directions. The results show that EMD can decompose irregular shield posture deviation signals into several regular components, and GRU can extract temporal dependencies in the input data, enabling precise prediction of shield posture deviations in different directions. Using a fixed number of EMD decomposition components and grouped prediction method ensures the independence of the prediction and training sets. After comparison with other models, the EMD-GRU model proved to be the optimal model. The analysis of the effects of different input parameter combinations on prediction results verifies the interdependence of posture deviations in the same direction (horizontal or vertical).
WANG Heng1 HU Jinjian2 SUN Chengguo1 ZHANG Jian2 LIANG Yu3,
4 FENG Tugen2
.Study on Grouped Prediction of Shield Posture Based on EMD-GRU Model with Fixed Component Number[J] MODERN TUNNELLING TECHNOLOGY, 2025,V62(2): 132-140
2025, Vol. 62 