Prediction of Surrounding Rock Deformation in a Highway Tunnel Using an LSTM-RF Hybrid Model
This article is part of Special Issue:
Chen Yintao,
Shao Xin,
Chang Xiangyu,
Siti Norafida Bt. Jusoh,
Lu Zhongxiang,
Bao Hong Quan,
Han Xinkai,
Xu Jun,
Chen Yintao
School of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Johor , Malaysia , utm.my
Search for more papers by this authorShao Xin
Test and Inspection Center , Zhejiang Scientific Research Institute of Transport , Hangzhou , Zhejiang , China
Search for more papers by this authorCorresponding Author
Chang Xiangyu
School of Civil Engineering , Nanyang Technological University , Singapore , Singapore , ntu.edu.sg
Search for more papers by this authorSiti Norafida Bt. Jusoh
School of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Johor , Malaysia , utm.my
Search for more papers by this authorLu Zhongxiang
School of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Johor , Malaysia , utm.my
Search for more papers by this authorBao Hong Quan
Test and Inspection Center , Zhejiang Scientific Research Institute of Transport , Hangzhou , Zhejiang , China
Search for more papers by this authorHan Xinkai
School of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Johor , Malaysia , utm.my
Search for more papers by this authorXu Jun
Engineering Management Department , Keqiao District Construction Group Co. , Ltd. , Shaoxing , Zhejiang , China
Search for more papers by this authorChen Yintao,
Shao Xin,
Chang Xiangyu,
Siti Norafida Bt. Jusoh,
Lu Zhongxiang,
Bao Hong Quan,
Han Xinkai,
Xu Jun,
Chen Yintao
School of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Johor , Malaysia , utm.my
Search for more papers by this authorShao Xin
Test and Inspection Center , Zhejiang Scientific Research Institute of Transport , Hangzhou , Zhejiang , China
Search for more papers by this authorCorresponding Author
Chang Xiangyu
School of Civil Engineering , Nanyang Technological University , Singapore , Singapore , ntu.edu.sg
Search for more papers by this authorSiti Norafida Bt. Jusoh
School of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Johor , Malaysia , utm.my
Search for more papers by this authorLu Zhongxiang
School of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Johor , Malaysia , utm.my
Search for more papers by this authorBao Hong Quan
Test and Inspection Center , Zhejiang Scientific Research Institute of Transport , Hangzhou , Zhejiang , China
Search for more papers by this authorHan Xinkai
School of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Johor , Malaysia , utm.my
Search for more papers by this authorXu Jun
Engineering Management Department , Keqiao District Construction Group Co. , Ltd. , Shaoxing , Zhejiang , China
Search for more papers by this authorFirst published: 06 May 2025
Academic Editor: Yuanxin Zhou
Abstract
Accurate tunnel deformation prediction is critical for mitigating construction risks and ensuring tunnel stability. This study introduces a novel hybrid model integrating long short-term memory (LSTM) networks and random forest (RF) to enhance the precision of tunnel deformation predictions during construction. Bayesian optimization was utilized to fine-tune model parameters, ensuring optimal performance. Validated with multidepth data from the Yangjiashan highway tunnel in China, the hybrid model demonstrates remarkable adaptability to complex geological conditions. The results show that the LSTM-RF model achieves a mean square error (MSE) of 0.0025, a root-mean-square error (RMSE) of 0.0052, and a coefficient of determination (R2) of 0.9810, outperforming individual models and other hybrid frameworks in predicting deformation trends. By effectively capturing temporal dependencies and modeling nonlinear residuals, the hybrid model provides a robust and reliable solution for improving safety and efficiency in tunneling projects. These findings emphasize the potential of hybrid approaches for geotechnical engineering, particularly in predictive maintenance and infrastructure monitoring.
Conflicts of Interest
The authors declare no conflicts of interest.
Open Research
Data Availability Statement
Data supporting the results of this study are available from the corresponding author upon reasonable request.
References
- 1
Li C. and
Zhou J., Prediction and Optimization of Adverse Responses for a Highway Tunnel After Blasting Excavation Using a Novel Hybrid Multi-Objective Intelligent Model, Transportation Geotechnics. (2024) 45, 101228, https://doi.org/10.1016/j.trgeo.2024.101228.
10.1016/j.trgeo.2024.101228 Google Scholar
- 2 Liu W., Zhai S., and Liu W., Predictive Analysis of Settlement Risk in Tunnel Construction: A Bow-Tie-Bayesian Network Approach, Advances in Civil Engineering. (2019) 2019, no. 1, 2045125, https://doi.org/10.1155/2019/2045125, 2-s2.0-85069777615.
- 3
Ma D.,
Duan H.,
Li Q.,
Wu J.,
Zhong W., and
Huang Z., Water–Rock Two-Phase Flow Model for Water Inrush and Instability of Fault Rocks During Mine Tunnelling, Geofluids. (2023) 10, no. 1, https://doi.org/10.1007/s40789-023-00612-6.
10.1007/s40789-023-00612-6 Google Scholar
- 4
Chen M.,
Zhang C.,
Canbulat I.,
Saydam S.,
Fan G., and
Zhang D., Assessment of Factors and Mechanism Contributing to Groundwater Depressurisation Due to Longwall Mining, Geofluids. (2024) 11, no. 1, https://doi.org/10.1007/s40789-024-00716-7.
10.1007/s40789-024-00716-7 Google Scholar
- 5
Li J.,
Zhang M.,
Wang C.,
Liao C., and
Zhang B., Failure Characteristics and Fracture Mechanism of Overburden Rock Induced by Mining: A Case Study in China, Geofluids. (2024) 11, no. 1, https://doi.org/10.1007/s40789-024-00693-x.
10.1007/s40789-024-00693-x Google Scholar
- 6
Chen Y. and
Xiao H., State-of-the-Art on the Anchorage Performance of Rock Bolts Subjected to Shear Load, Geofluids. (2024) 11, no. 1, https://doi.org/10.1007/s40789-023-00643-z.
10.1007/s40789-023-00643-z Google Scholar
- 7
He J.,
Serati M.,
Veidt M., and
De Alwis A., Determining Rock Crack Stress Thresholds Using Ultrasonic Through-Transmission Measurements, Geofluids. (2024) 11, no. 1, https://doi.org/10.1007/s40789-024-00669-x.
10.1007/s40789-024-00669-x Google Scholar
- 8
Liu L.,
Zhou W., and
Gutierrez M., Effectiveness of Predicting Tunneling-Induced Ground Settlements Using Machine Learning Methods With Small Datasets, Journal of Rock Mechanics and Geotechnical Engineering.(2022) 14, no. 4, 1028–1041, https://doi.org/10.1016/j.jrmge.2021.08.018.
10.1016/j.jrmge.2021.08.018 Google Scholar
- 9 Xu C., Liu X., Wang E., and Wang S., Prediction of Tunnel Boring Machine Operating Parameters Using Various Machine Learning Algorithms, Tunnelling and Underground Space Technology. (2021) 109, 103699, https://doi.org/10.1016/j.tust.2020.103699.
- 10
Chen H.,
Xiao C.,
Yao Z.,
Jiang H.,
Zhang T., and
Guan Y., Prediction of TBM Tunneling Parameters Through an LSTM Neural Network, Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2019, IEEE, 702–707, https://doi.org/10.1109/ROBIO49542.2019.8961809.
10.1109/ROBIO49542.2019.8961809 Google Scholar
- 11
He Y. and
Chen Q., Construction and Application of LSTM-Based Prediction Model for Tunnel Surrounding Rock Deformation, Sustainability.(2023) 15, no. 8, https://doi.org/10.3390/su15086877.
10.3390/su15086877 Google Scholar
- 12
Ye X. W.,
Ma S. Y.,
Liu Z. X.,
Chen Y. B.,
Lu C. R.,
Song Y. J.,
Li X. J., and
Zhao L. A., LSTM-Based Deformation Forecasting for Additional Stress Estimation of Existing Tunnel Structure Induced by Adjacent Shield Tunneling, Tunnelling and Underground Space Technology. (2024) 146, 105664, https://doi.org/10.1016/j.tust.2024.105664.
10.1016/j.tust.2024.105664 Google Scholar
- 13 Zhou J., Shi X., Du K., Qiu X., Li X., and Mitri H. S., Feasibility of Random-Forest Approach for Prediction of Ground Settlements Induced by the Construction of a Shield-Driven Tunnel, International Journal of Geomechanics. (2017) 17, no. 6, 04016129, https://doi.org/10.1061/(ASCE)GM.1943-5622.0000817, 2-s2.0-85017509463.
- 14
Gokceoglu C.,
Bal C., and
Aladag C., Modeling of Tunnel Boring Machine Performance Employing Random Forest Algorithm, Geotechnical Engineering. (2023) 41, no. 7, 4205–4231, https://doi.org/10.1007/s10706-023-02516-3.
10.1007/s10706-023-02516-3 Google Scholar
- 15 Liu Y., Chen H., Zhang L., and Wang X., Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest, Journal of Civil Engineering and Management. (2021) 27, no. 7, 539–552, https://doi.org/10.3846/jcem.2021.14901.
- 16 Dhiman H. S., Deb D., and Guerrero J. M., Hybrid Machine Intelligent SVR Variants for Wind Forecasting and Ramp Events, Renewable and Sustainable Energy Reviews. (2019) 108, 369–379, https://doi.org/10.1016/j.rser.2019.04.002, 2-s2.0-85063956738.
- 17
Mahmoodzadeh A.,
Nejati H. R.,
Mohammadi M.,
Ibrahim H. H.,
Rashidi S., and
Ibrahim B. F., Forecasting Face Support Pressure During EPB Shield Tunneling in Soft Ground Formations Using Support Vector Regression and Meta-Heuristic Optimization Algorithms, Rock Mechanics and Rock Engineering. (2022) 55, no. 10, 6367–6386, https://doi.org/10.1007/s00603-022-02977-7.
10.1007/s00603-022-02977-7 Google Scholar
- 18
Meng G.,
Li H.,
Wu B.,
Liu G.,
Ye H., and
Zuo Y., Prediction of the Tunnel Collapse Probability Using SVR-Based Monte Carlo Simulation: A Case Study, Sustainability. (2023) 15, no. 9, https://doi.org/10.3390/su15097098.
10.3390/su15097098 Google Scholar
- 19
Chang X.,
Wang H.,
Zhang Y.,
Wang F., and
Li Z., Bayesian Prediction of Tunnel Convergence Combining Empirical Model and Relevance Vector Machine, Measurement. (2022) 188, 110621, https://doi.org/10.1016/j.measurement.2021.110621.
10.1016/j.measurement.2021.110621 Google Scholar
- 20
Zheng M.,
Li S.,
Zhao H.,
Huang X., and
Qiu S., Probabilistic Analysis of Tunnel Displacements Based on Correlative Recognition of Rock Mass Parameters, Geoscience Frontiers. (2021) 12, no. 4, 101136, https://doi.org/10.1016/j.gsf.2020.12.015.
10.1016/j.gsf.2020.12.015 Google Scholar
- 21 Mahmoodzadeh A., Mohammadi M., Ibrahim H. H., Rashid T. A., Aldalwie A. H. M., Ali H. F. H., and Daraei A., Tunnel Geomechanical Parameters Prediction Using Gaussian Process Regression, Machine Learning With Applications. (2021) 3, 100020, https://doi.org/10.1016/j.mlwa.2021.100020.
- 22
He P.,
Li L. P.,
Zhang Q. Q.,
Xu F.,
Hu J., and
Zhang J., Gaussian Process Model of an Advanced Surrounding Rock Classification Based on Tunnel Seismic Predictions, Proceedings of the Geo-China 2016, 2016, American Society of Civil Engineers (ASCE), 210–217, https://doi.org/10.1061/9780784480038.026, 2-s2.0-84983028357.
10.1061/9780784480038.026 Google Scholar
- 23
Cao W.,
Jiang Y.,
Sakaguchi O.,
Li N., and
Han W., Predication of Displacement of Tunnel Rock Mass Based on the Back-Analysis Method-BP Neural Network, Geotechnical Engineering. (2022) 40, no. 2, 531–544, https://doi.org/10.1007/s10706-021-01874-0.
10.1007/s10706-021-01874-0 Google Scholar
- 24 Liu B., Wang R., Zhao G., Guo X., Wang Y., Li J., and Wang S., Prediction of Rock Mass Parameters in the TBM Tunnel Based on BP Neural Network Integrated Simulated Annealing Algorithm, Tunnelling and Underground Space Technology. (2020) 95, 103103, https://doi.org/10.1016/j.tust.2019.103103.
- 25 Aurélien G., Hands-On Machine Learning With Scikit-Learn, Keras, and Tensor Flow, 2019, O’Reilly Media, https://doi.org/10.1007/s13246-020-00913-z.
- 26 Seeger M., Gaussian Processes for Machine Learning, International Journal of Neural Systems. (2004) 14, no. 2, 69–106, https://doi.org/10.1142/S0129065704001899, 2-s2.0-12444291490.
- 27 Breiman L., Random Forests, Machine Learning. (2001) 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2-s2.0-0035478854.
- 28 Hao X., Zhang G., and Ma S., Deep Learning, International Journal of Semantic Computing. (2016) 10, no. 3, 417–439, https://doi.org/10.1142/S1793351X16500045, 2-s2.0-85028568275.
- 29 LeCun Y., Bengio Y., and Hinton G., Deep Learning, Nature. (2015) 521, no. 7553, 436–444, https://doi.org/10.1038/nature14539, 2-s2.0-84930630277.
- 30
Huang Z.,
Liao M.,
Zhang H.,
Zhang J.,
Ma S., and
Zhu Q., Predicting Tunnel Squeezing Using the SVM-BP Combination Model, Geotechnical Engineering. (2022) 40, no. 3, 1387–1405, https://doi.org/10.1007/s10706-021-01970-1.
10.1007/s10706-021-01970-1 Google Scholar
- 31 Pierre A. A., Akim S. A., Semenyo A. K., and Babiga B. J., Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches, Energies. (2023) 16, no. 12, https://doi.org/10.3390/en16124739.
- 32 Xu Q., Huang X., Zhang B., Zhang Z., Wang J., and Wang S., TBM Performance Prediction Using LSTM-Based Hybrid Neural Network Model: Case Study of Baimang River Tunnel Project in Shenzhen, China, Underground Space. (2023) 11, 130–152, https://doi.org/10.1016/j.undsp.2022.11.002.
- 33
Li L.,
Liu Z.,
Zhou H.,
Zhang J.,
Shen W., and
Shao J., Prediction of TBM Cutterhead Speed and Penetration Rate for High-Efficiency Excavation of Hard Rock Tunnel Using CNN-LSTM Model With Construction Big Data, Arabian Journal of Geosciences. (2022) 15, no. 3, https://doi.org/10.1007/s12517-022-09542-0.
10.1007/s12517-022-09542-0 Google Scholar
- 34
Huang F.,
Xiong H.,
Chen S.,
Lv Z.,
Huang J.,
Chang Z., and
Catani F., Slope Stability Prediction Based on a Long Short-Term Memory Neural Network: Comparisons With Convolutional Neural Networks, Support Vector Machines and Random Forest Models, Geofluids. (2023) 10, no. 1, https://doi.org/10.1007/s40789-023-00579-4.
10.1007/s40789-023-00579-4 Google Scholar
- 35
Huang F.,
Cao Y.,
Li W.,
Catani F.,
Song G.,
Huang J., and
Yu C., Uncertainties of Landslide Susceptibility Prediction: Influences of Different Study Area Scales and Mapping Unit Scales, Geofluids. (2024) 11, no. 1, https://doi.org/10.1007/s40789-024-00678-w.
10.1007/s40789-024-00678-w Google Scholar
- 36
Ur Rehman Z.,
Hussain S.,
Tahir M.,
Sherin S.,
Mohammad N.,
Dasti N.,
Raza S., and
Salman M., Numerical Modelling for Geotechnical Assessment of Rock Mass Behaviour and Performance of Support System for Diversion Tunnels Using Optimized Hoek-Brown Parameters, Mining Science and Technology. (2022) 16, no. 1, 1–8, https://doi.org/10.33271/mining16.01.001.
10.33271/mining16.01.001 Google Scholar
- 37 Greff K., Srivastava R. K., Koutník J., Steunebrink B. R., and Schmidhuber J., LSTM: A Search Space Odyssey, IEEE Transactions on Neural Networks and Learning Systems. (2017) 28, no. 10, 2222–2232, https://doi.org/10.1109/TNNLS.2016.2582924, 2-s2.0-84979010616, 27411231.
- 38
Siami-Namini S.,
Tavakoli N., and
Siami Namin A., The Performance of LSTM and BiLSTM in Forecasting Time Series, Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), 2019, IEEE, 3285–3292, https://doi.org/10.1109/BigData47090.2019.9005997.
10.1109/BigData47090.2019.9005997 Google Scholar
- 39 Gers F. A., Schmidhuber J., and Cummins F., Learning to Forget: Continual Prediction With LSTM, Neural Computation. (2000) 12, no. 10, 2451–2471, https://doi.org/10.1162/089976600300015015, 2-s2.0-0034293152, 11032042.
- 40 Belgiu M. and Drăguţ L., Random Forest in Remote Sensing: A Review of Applications and Future Directions, ISPRS Journal of Photogrammetry and Remote Sensing. (2016) 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2-s2.0-84961834117.
- 41
Smola A. J. and
Schölkopf B. J. S., Computing, A Tutorial on Support Vector Regression. (2004) 14, no. 3, 199–222, https://doi.org/10.1023/B:STCO.0000035301.49549.88, 2-s2.0-4043137356.
10.1023/B:STCO.0000035301.49549.88 Google Scholar
- 42 Vapnik V. N., An Overview of Statistical Learning Theory, IEEE Transactions on Neural Networks. (1999) 10, no. 5, 988–999, https://doi.org/10.1109/72.788640, 2-s2.0-0032594959.
- 43
Tipping M. E., Sparse Bayesian Learning and the Relevance Vector Machine, Journal of Machine Learning Research, 2001, 1, 211–244, https://doi.org/10.1162/15324430152748236, 2-s2.0-0001224048.
10.1162/15324430152748236 Google Scholar
- 44
Bishop C. M. J. S. G. S., Pattern Recognition and Machine Learning, 2006, Springer.
10.1007/978-0-387-45528-0 Google Scholar
- 45 Rumelhart D. E., Hinton G. E., and Williams R. J., Learning Representations by Back-Propagating Errors, Nature. (1986) 323, no. 6088, 533–536, https://doi.org/10.1038/323533a0, 2-s2.0-0022471098.
- 46
Wu J.,
Chen X. Y.,
Zhang H.,
Xiong L. D.,
Lei H., and
Deng S. H., Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization, Journal of Electronic Science and Technology. (2019) 17, no. 1, 26–40, https://doi.org/10.11989/JEST.1674-862X.80904120, 2-s2.0-85064181166.
10.11989/JEST.1674-862X.80904120 Google Scholar
- 47
Xie Y.,
Chang X.,
Mao J.,
Ni Y., and
Wang H. J. S., Prediction and Early Warning of Extreme Winds for High-Speed Railway Bridge Construction Using Machine-Learning Methods, Sustainability. (2023) 15, no. 24, 16921, https://doi.org/10.3390/su152416921.
10.3390/su152416921 Google Scholar
- 48
Sun Z. J. A. M. M., Stress Predicting on the Surrounding Rocks of Tunnel Based on BP Neural Network, Applied Mechanics and Materials. (2012) 170, 1638–1642, https://doi.org/10.4028/www.scientific.net/AMM.170-173.1638, 2-s2.0-84861829979.
10.4028/www.scientific.net/AMM.170-173.1638 Google Scholar
- 49 Yang X., Fan X., Wang K., and Zhou Z., Research on Landslide Susceptibility Prediction Model Based on LSTM-RF-MDBN, Environmental Science and Pollution Research. (2024) 31, no. 1, 1504–1516, https://doi.org/10.1007/s11356-023-31232-x.
- 50 Fan J., Zhang K., Huang Y., Zhu Y., and Chen B., Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction, Neural Computing and Applications. (2023) 35, 18, 13109, https://doi.org/10.1007/s00521-021-05958-z.
- 51 Mahjoub S., Chrifi-Alaoui L., Marhic B., and Delahoche L., Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks, Sensors. (2022) 22, no. 11, https://doi.org/10.3390/s22114062, 35684681.
