Clogging Risk Early Warning for Slurry Shield Tunneling in Mixed Mudstone–Gravel Ground: A Real-Time Self-Updating Machine Learning Approach
Abstract
:1. Introduction
2. Literature Review
2.1. Clogging Judgment Criterion and Tunneling Parameter Variation Due to Clogging
2.2. Application of RF-Based Prediction Model
3. Methodology and Material
3.1. Methodology
3.1.1. Random Forest (RF) Classifier
3.1.2. Real-Time Clogging Early Warning Process
3.2. Data Collection—Nanning Metro Line 1 Project
4. Statistical Analysis of The Tunneling Parameters in The BR Section
4.1. Statistical Analysis of Tunneling Parameters
4.2. Determination of Clogging Based on Tunneling Parameters
- The SPFI of the whole ring was larger than 0.15;
- The average value of the TOR was larger than 1 MN·m;
- The average value of the AR was smaller than 15 mm/min.
5. Clogging Prediction Results in the BR Section
5.1. Prediction Results
5.2. Discussion
5.2.1. Model Performance without Training Data Updating
5.2.2. Influence of Input Features on Clogging Prediction
6. Conclusions and Future Work
- The SPB shield tunneling in the mudstone-rich area was frequently subjected to clogging, making the statistical characteristics of the tunneling parameters quite different from the normal conditions. Furthermore, clogging was generally accompanied by high fluctuations of SPE and TOR, and low values of AR, which was harmful to tunneling safety and efficiency;
- Data allowed us to establish the following three clogging criteria for tunnels constructed in mixed ground of round gravel and mudstone: (i) SPFI of the whole ring larger than 0.15 (); (ii) average value of the TOR higher than 1 MN·m; and (iii) an average value of the AR smaller than 15 mm/min. Clogging occurred when two of these conditions were satisfied;
- The RF model provided a good prediction for the early warning of clogging using four minutes of tunneling data with an accuracy of 95%. In addition, the RF model yielded the best performance compared with the KNN model, the SVC model, and the MLP model;
- Feature importance analysis showed the crucial role of the TOR and PR in clogging. Furthermore, with the self-updating mechanism, the RF model can make a good prediction of clogging from the early beginning of one ring, which is beneficial for clogging early warnings.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Soil | |||||||
---|---|---|---|---|---|---|---|
g/cm3 | kPa | ° | MPa | m/d | |||
Round gravel | 2.05 | 0.27 | 0.0 | 35.0 | 25.0 | 90 | 0.37 |
Mudstone | 2.15 | 0.20 | 90.0 | 21.0 | 30.0 | 0.01 | 0.25 |
Sample | |||||||
---|---|---|---|---|---|---|---|
% | % | % | % | % | % | % | |
Sample 1 | 14.9 | 35.6 | 19.6 | 16.0 | 20.7 | 4.7 | 1.29 |
Sample 2 | 15.2 | 38.4 | 22.0 | 16.4 | 23.2 | 6.8 | 1.41 |
Parameters\Indexes | Mean | Std | Max | Range | ||||
---|---|---|---|---|---|---|---|---|
Normal | Clogging | Normal | Clogging | Normal | Clogging | Normal | Clogging | |
SPE (kPa) | 163.7 | 203.5 | 5.4 | 22.2 | 179.1 | 310.9 | 30.0 | 160.0 |
SPW (kPa) | 180.2 | 181.7 | 3.6 | 7.0 | 191.9 | 210.2 | 25.1 | 53.9 |
TOR (MN·m) | 0.9 | 2.2 | 0.2 | 0.5 | 1.3 | 3.5 | 1.1 | 3.1 |
THR (MN) | 12.1 | 16.5 | 1.2 | 1.4 | 14.1 | 18.4 | 7.9 | 11.8 |
RS (rpm) | 1.0 | 1.2 | 0.03 | 0.05 | 1.1 | 1.3 | 0.2 | 0.2 |
AR (mm/min) | 26.9 | 10.1 | 8.0 | 4.4 | 37.5 | 25.2 | 37.4 | 25.2 |
Actual Class | |||
---|---|---|---|
P: Clogging | N: Normal | ||
Predicted class | P: Clogging | TP | FP |
N: Normal | FN | TN |
Tunneling Data Length (Minutes) | Error Rate (%) | Precision (%) | Recall (%) | F1 (%) | ||||
---|---|---|---|---|---|---|---|---|
Updating | No Updating | Updating | No Updating | Updating | No Updating | Updating | No Updating | |
0.5 | 16.5 | 20.7 | 80.5 | 80.2 | 77.3 | 62.8 | 78.9 | 70.4 |
1 | 11.6 | 18.6 | 88.6 | 77.5 | 81.2 | 75.2 | 84.7 | 76.3 |
2 | 9.7 | 24.5 | 89.5 | 64.3 | 85.6 | 84.5 | 87.5 | 73.0 |
3 | 6.4 | 12.5 | 82.5 | 80.1 | 91.3 | 91.2 | 86.7 | 85.3 |
4 | 5.2 | 9.7 | 93.8 | 84.6 | 93.4 | 92.7 | 93.6 | 88.5 |
5 | 4.2 | 5.4 | 95.6 | 94.1 | 93.9 | 92.8 | 94.7 | 93.4 |
6 | 4.3 | 5.5 | 95.1 | 95.2 | 93.9 | 92.6 | 94.5 | 93.9 |
8 | 4.3 | 5.2 | 95.1 | 94.2 | 94.0 | 92.8 | 94.6 | 93.5 |
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Zhai, J.; Wang, Q.; Yuan, D.; Zhang, W.; Wang, H.; Xie, X.; Shahrour, I. Clogging Risk Early Warning for Slurry Shield Tunneling in Mixed Mudstone–Gravel Ground: A Real-Time Self-Updating Machine Learning Approach. Sustainability 2022, 14, 1368. https://doi.org/10.3390/su14031368
Zhai J, Wang Q, Yuan D, Zhang W, Wang H, Xie X, Shahrour I. Clogging Risk Early Warning for Slurry Shield Tunneling in Mixed Mudstone–Gravel Ground: A Real-Time Self-Updating Machine Learning Approach. Sustainability. 2022; 14(3):1368. https://doi.org/10.3390/su14031368
Chicago/Turabian StyleZhai, Junli, Qiang Wang, Dongyang Yuan, Weikang Zhang, Haozheng Wang, Xiongyao Xie, and Isam Shahrour. 2022. "Clogging Risk Early Warning for Slurry Shield Tunneling in Mixed Mudstone–Gravel Ground: A Real-Time Self-Updating Machine Learning Approach" Sustainability 14, no. 3: 1368. https://doi.org/10.3390/su14031368