The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels
Abstract
:1. Introduction
2. Background Projects and Database Construction
2.1. Project Overview
2.1.1. Engineering Background
2.1.2. Monitoring Data
2.2. Categories of Factors Related to Primary Support Deformation
2.2.1. Dataset for Deformation Prediction
2.2.2. Geometrical and Support Parameters
2.2.3. Geological Parameters
2.2.4. Construction Parameters
3. Methodology
3.1. Based Method
3.1.1. Deep Neural Network
3.1.2. XGboost
3.1.3. Random Forest
3.2. Tunnel Primary Support Deformation Prediction Model
4. Analysis and Comparison of the Model Performance
4.1. Analysis of the Prediction Model
4.2. Comparative Analysis of Multi-Types Model
5. Conclusions
- (1)
- Compared to the DNN, XGBoost, DT, and RTR models, the FDNN model demonstrates better predictive performance in terms of deformation prediction. The fusion of various data enhances the model’s robustness;
- (2)
- The training data composition had a large impact on the results. The degree of data influence is in the order of geological data, support data, and construction data;
- (3)
- The performance of the model gradually rises with the increase in data types. Different tunnel feature data overlap and are superpositioned for model performance enhancement.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Tunnel | Profile | Length | Length of Surrounding Rock Grade | ||
---|---|---|---|---|---|
III | IV | V | |||
Yangjiawopu tunnel (YT) | 5054 | 2240 | 2240 | 574 | |
Daweitang 1 tunnel (DT) | 1736 | 1220 | 380 | 136 | |
Fangkuangzi tunnel (FT) | 1834 | 520 | 860 | 454 | |
Nanhuodao tunnel (NT) | 3874 | 2731 | 260 | 883 |
No. | Parameter | Unit | Notation |
---|---|---|---|
1 | Tunnel area | m2 | A |
2 | Depth of tunnel | m | H |
3 | Width of tunnel | m | W |
4 | High of tunnel | m | Q |
5 | Stiffness of primary support | MPa | Ep |
6 | Stiffness of secondary support | MPa | Es |
No. | Parameters | Notation |
---|---|---|
1 | Rock type | Rt |
2 | Surrounding rock grade | Sr |
3 | Basic quality | BQ |
4 | Rock mass elastic wave velocity | kv |
Model | Key Parameters |
---|---|
DNN | Input-BN-FC(176)-FC(100)-FC(80)-FC(40)-FC(20)Dropout(0.1)-Output |
XGBoost | max_depth: 7; learning rate: 0.1005; n_estimators: 36; objective: ‘reg: squarederror’; booster: ‘gbtree’ |
DTR | max_depth: 10; random_state: 30 |
RFR | max_depth: 7; random_state: 30 |
Model | Training Set | Test Set | ||||||
---|---|---|---|---|---|---|---|---|
MAE | MSE | RMSE | R2 | MAE | MSE | RMSE | R2 | |
FDNN | 0.046 | 0.004 | 0.059 | 0.938 | 0.043 | 0.003 | 0.053 | 0.910 |
DNN | 0.054 | 0.005 | 0.072 | 0.908 | 0.058 | 0.007 | 0.081 | 0.821 |
XGBoost | 0.054 | 0.006 | 0.075 | 0.987 | 0.143 | 0.049 | 0.222 | 0.779 |
DTR | 0.115 | 0.126 | 0.354 | 0.991 | 1.114 | 2.954 | 1.721 | 0.687 |
RFR | 0.482 | 0.447 | 0.661 | 0.965 | 0.943 | 2.182 | 1.485 | 0.768 |
Dataset | Parameters |
---|---|
DS1 | Geometrical parameters, geological parameters |
DS2 | Geometrical parameters, support parameters |
DS3 | Geometrical parameters, construction parameters |
DS4 | Geometrical parameters, geological parameters, support parameters |
DS5 | Geometrical parameters, geological parameters, construction parameters |
DS6 | Geometrical parameters, support parameters, construction parameters |
DS7 | Geometrical parameters, geological parameters, support parameters, construction parameters |
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Zhang, J.; Mei, M.; Wang, J.; Shang, G.; Hu, X.; Yan, J.; Fang, Q. The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels. Appl. Sci. 2024, 14, 912. https://doi.org/10.3390/app14020912
Zhang J, Mei M, Wang J, Shang G, Hu X, Yan J, Fang Q. The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels. Applied Sciences. 2024; 14(2):912. https://doi.org/10.3390/app14020912
Chicago/Turabian StyleZhang, Junling, Min Mei, Jun Wang, Guangpeng Shang, Xuefeng Hu, Jing Yan, and Qian Fang. 2024. "The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels" Applied Sciences 14, no. 2: 912. https://doi.org/10.3390/app14020912
APA StyleZhang, J., Mei, M., Wang, J., Shang, G., Hu, X., Yan, J., & Fang, Q. (2024). The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels. Applied Sciences, 14(2), 912. https://doi.org/10.3390/app14020912