The prediction of tunnel time-dependent deformation data is of crucial significance to ensure the safety of tunnel construction. In order to improve the accuracy and reliability of tunnel deformation prediction, this research presents a procedure for building an optimal regression model, which is applied to a large-span mudstone tunnel. Our procedure performs a large number of regression calculations using sixty-four data sets and multiple types of regression models. The data characteristics of the calculation results are analyzed using box plots and grey decision-making method. In addition, two individual regression models are combined into regression ensemble model, for improving the accuracy of deformation prediction. In this procedure, the building of the model is divided into two stages: early stage and late stage of initial support construction, taking into account the importance of deformation prediction throughout the construction process. Finally, we compared four machine learning models and analyzed their differences in predicting tunnel deformation. The results show that the optimal regression model developed by this procedure have great performance for the early and later stages of tunnel deformation.

References

1Luo, Y.B., Chen, J.X., Shi, Z., Li, J.Z., Liu, W.W. “Mechanical characteristics of primary support of large span loess highway tunnel: A case study in Shaanxi Province, Loess Plateau, NW China primary.” Tunnelling and Underground Space Technology. 104, 103532 (2020).
Web of Science® Google Scholar
2Zhang, Z.L., Wang, T. “Failure modes of weak interlayers with different dip angles in red mudstone strata, Northwest China.” Bulletin of Engineering Geology and the Environment. 82 (5), 156 (2023).
Web of Science® Google Scholar
3Liu, W.W., Chen, J.X., Luo, Y.B., Chen, L.J., Zhang, L.X., He, C.L., Shi, Z., Xu, Z.L., Zhu, H.Y., Hu, T.T., Dong, F.F. “Long-term stress monitoring and in-service durability evaluation of a large-span tunnel in squeezing rock.” Tunnelling and Underground Space Technology. 127, 104611 (2022).
10.1016/j.tust.2022.104611
Web of Science® Google Scholar
4Feng, X.D., Jimenez, R., Zeng, P., Senent, S. “Prediction of time-dependent tunnel convergences using a Bayesian updating approach.” Tunnelling and Underground Space Technology. 94, 103118 (2019).
10.1016/j.tust.2019.103118
Web of Science® Google Scholar
5Hu, M., Li, W., Yan, K., Ji, Z.W., Hu, H.G. “ Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study.” Mathematical Problems in Engineering. 2019, e7057612 (2019).
Google Scholar
6Pan, Y., Chen, L., Wang, J., Ma, H.S., Cai, S.L., Pu, S.K., Duan, J.L., Gao, L., Li, E.B. “Research on deformation prediction of tunnel surrounding rock using the model combining firefly algorithm and nonlinear auto-regressive dynamic neural network.” Engineering with Computers. 37 (2), 1443–1453 (2021).
10.1007/s00366-019-00894-y
Web of Science® Google Scholar
7Chen, C.H., Wu, L., Peng, Y.X. “The Analysis Method of Rock Mass Deformation of Large Cross-section Tunnel.” Geotechnical and Geological Engineering. 34 (2), 739–743 (2016).
Google Scholar
8Wu, Y.M., Zhuang, G.Z., Liang, W.M., Huang, L. “Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring.” Applied Sciences. 13 (14), 8328 (2023).
10.3390/app13148328
CAS Google Scholar
9 China State Railway Group Co., Ltd. “ Technical Specification for Monitoring Measurement of Railway Tunnel (Q/CR 9218-2015).” China Railway Publishing House Co., Ltd. (2015) In Chinese.
Google Scholar
10 China First Highway Engineering Company Ltd. “ Technical Guidelines for Construction of Highway Tunnel (JTG/T 3660-2020).” Ministry of Transport of the People's Republic of China. (2020) In Chinese.
Google Scholar
11Chen, J.X., Wang, M.S., Xuan, J.J., Qiao, X. “Deformation rule of loess highway tunnel with two lanes. Journal of Traffic and Transportation Engineering. 12 (03): 9–18. (2012). In Chinese.
Google Scholar
12Cui, J.N., Gao, Y.T., Wang, J.Q., Li, J., Deng, F.G. “Study on the Construction Time of Secondary Lining in Loess Tunnel.” Applied Mechanics and Materials. 170–173, 1427–1431 (2012).
Google Scholar
13Feng, J.Q., Wang, X.M., Zhou, Y.X., Liu, G.F., Liu, K.D. “Study on the supporting time of the secondary lining of the highway tunnel with weak surrounding rock.” IOP Conference Series: Earth and Environmental Science. 643 (1), 012042 (2021).
Google Scholar
14Wang, T., Sun, W.N., Li, L. “Study on Surrounding Rock Deformation Combined Forecasting Method Based on Regression Analysis and Grey Theory.” Chinese Journal of Underground Space and Engineering. 13 (S1): 48–51 (2017). In Chinese.
Google Scholar
15Wang, S.T., Liang, X.Y., Zhou, Y.T. “Regression Analysis on Monitoring Data of Tunnel Works.” Tunnel Construction. 29 (06): 629–632.+663 (2009). In Chinese.
CAS Google Scholar
16Yan, J., Liu, X.Y., Bai, X.Y., Zhang, M.Y., Jiao, Y.J., Jing, D.S., Liu, H.H., Wang, Z.S., Kang, W., Vignali, V. “Research on Surface Subsidence of Long-Span Underground Tunnel.” Advances in Civil Engineering. 2021, 1–13 (2021).
CAS Web of Science® Google Scholar
17Yan, K., Dai, Y.T., Xu, M.L., Mo, Y.C. “Tunnel Surface Settlement Forecasting with Ensemble Learning.” Sustainability. 12 (1), 232 (2019).
10.3390/su12010232
Google Scholar
18Qiu, Z.F., Shen, J., Fu, X.D., Luo, H.W. “Analysis of Tunnel Monitoring Measurement Data Based on the Optimum Weighted Combinatorial Prediction Model.” Journal of Yangtze River Scientific Research Institute. 33 (05): 53–57. (2016). In Chinese.
Google Scholar
19Xing, Y.J., Ma, E.W.M., Tsui, K.L., Pecht, M. “An ensemble model for predicting the remaining useful performance of lithium-ion batteries.” Microelectronics Reliability. 53 (6), 811–820 (2013).
10.1016/j.microrel.2012.12.003
CAS Web of Science® Google Scholar
20Chen, Y.X., Wu, X.P., Gao, C.C., Lei, J., Liu, G.L., Cheng, W.G. “Optimization and application of prediction method of tunnel surrounding rock deformation.” Journal of Railway Science and Engineering. 16 (02): 426–434. (2019). In Chinese.
Google Scholar
21Yang, P.X., Yong, W.X., Li, C.Q., Peng, K., Wei, W., Qiu, Y.G., Zhou, J. “Hybrid Random Forest-Based Models for Earth Pressure Balance Tunneling-Induced Ground Settlement Prediction.” Applied Sciences. 13 (4), 2574 (2023).
10.3390/app13042574
CAS Google Scholar
22Chang, C.J., Li, D.C., Huang, Y.H., Chen, C.C. “A novel gray forecasting model based on the box plot for small manufacturing data sets.” Applied Mathematics and Computation. 265, 400–408 (2015).
10.1016/j.amc.2015.05.006
Web of Science® Google Scholar
23Liu, S.F., Xu, B., Forrest, J., Chen, Y., Yang, Y.J. “On Uniform Effect Measure Functions and a Weighted Multi-attribute Grey Target Decision Model.” Journal of Grey System. 25 (1), 1–11 (2013).
CAS Web of Science® Google Scholar