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Prediction of Grouting Uplift in the Bottom of Shield Tunnels Based on LSTM
Uneven settlement and associated issues like joint water leakage in shield tunnels operating in soft clay are prevalent, significantly impacting the safety of tunnel operations and the structural service life. The accuracy of idealized theoretical models in predicting the uplift-settlement of shield tunnels is limited. This paper focuses on the calculation method for the above-ground uplift of the overlying shield tunnel caused by grouting. It conducts parameter analysis for expansion, strata, and tunnel parameters regarding the longitudinal deformation calculation method. The study establishes a database of final uplift values for shield tunnels, utilizes the Keras deep learning framework to develop an LSTM model, and applies it to predict the uplift in a grouting case study in Ningbo Metro 2. The predictive analysis results demonstrate that the predicted curve closely matches the measured values, indicating a certain level of effectiveness. The peak value of the calculated curve is more consistent with the measured than predicted. Outside the grouting range, the predicted results are slightly higher than the measured values, while the calculated results are slightly lower. Considering both the predicted and calculated results better reflects the actual situation.
Prediction of Grouting Uplift in the Bottom of Shield Tunnels Based on LSTM
Uneven settlement and associated issues like joint water leakage in shield tunnels operating in soft clay are prevalent, significantly impacting the safety of tunnel operations and the structural service life. The accuracy of idealized theoretical models in predicting the uplift-settlement of shield tunnels is limited. This paper focuses on the calculation method for the above-ground uplift of the overlying shield tunnel caused by grouting. It conducts parameter analysis for expansion, strata, and tunnel parameters regarding the longitudinal deformation calculation method. The study establishes a database of final uplift values for shield tunnels, utilizes the Keras deep learning framework to develop an LSTM model, and applies it to predict the uplift in a grouting case study in Ningbo Metro 2. The predictive analysis results demonstrate that the predicted curve closely matches the measured values, indicating a certain level of effectiveness. The peak value of the calculated curve is more consistent with the measured than predicted. Outside the grouting range, the predicted results are slightly higher than the measured values, while the calculated results are slightly lower. Considering both the predicted and calculated results better reflects the actual situation.
Prediction of Grouting Uplift in the Bottom of Shield Tunnels Based on LSTM
Springer Ser.Geomech.,Geoengineer.
Gutierrez, Marte (editor) / Tang, Y. F. (author) / Chen, T. (author) / Song, C. X. (author) / Meng, F. Y. (author)
International Conference on Inforatmion Technology in Geo-Engineering ; 2024 ; Golden, CO, USA
2024-11-03
8 pages
Article/Chapter (Book)
Electronic Resource
English
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