TBM Net Advance Rate Prediction Model Based on Ridge Regression Analysis
[Objective] Accurate prediction of TBM (tunnel boring machine) net advance rate is of significant reference value for selecting urban tunneling methods, planning construction schedules, and controlling construction costs. [Method] Taking the dual-shield TBM construction of Qingdao Metro Line 1 as th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Urban Mass Transit Magazine Press
2025-06-01
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| Series: | Chengshi guidao jiaotong yanjiu |
| Subjects: | |
| Online Access: | https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.20230594.html |
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| Summary: | [Objective] Accurate prediction of TBM (tunnel boring machine) net advance rate is of significant reference value for selecting urban tunneling methods, planning construction schedules, and controlling construction costs. [Method] Taking the dual-shield TBM construction of Qingdao Metro Line 1 as the background, feature selection is conducted for the input variables of the TBM net advance rate prediction model. The correlations between the TBM net advance rate and various input variables are analyzed, and collinearity diagnostics are performed on the input variables. A TBM net advance rate prediction model based on ridge regression analysis (hereinafter referred to as the ′ridge regression prediction model′) is established, and its predictive performance is validated. [Result & Conclusion] The TBM net advance rate shows a positive and relatively strong correlation with uniaxial compressive strength of the rock, rock integrity coefficient, cutterhead thrust, and cutterhead rotation speed. There exists a certain degree of multicollinearity among the input variables of the prediction model, which affects the estimation of partial regression coefficients, rendering some of them statistically insignificant. Although the prediction accuracy of the ridge regression prediction model is slightly lower, its estimated partial regression coefficients tend to be more reasonable, resulting in higher model stability. The absolute prediction error of the ridge regression prediction model is within 5 mm/min, which meets the requirements of engineering prediction. |
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| ISSN: | 1007-869X |