Risk prediction and diagnosis of water seepage in operational shield tunnels based on random forest

    Yang Liu Affiliation
    ; Hongyu Chen Affiliation
    ; Limao Zhang Affiliation
    ; Xianjia Wang Affiliation


Water seepage (WS) is a paramount defect during tunnel operation and directly affects the operational safety of tunnels. Effectively predicting and diagnosing WS are problems that urgently need to be solved. This paper presents a standard and an evaluation index system for WS grades and constructs a sample dataset from monitoring recoreds for demonstration purposes. First, we use bootstrap resampling to build a random forest (RF) seepage risk prediction model. Second, the optimal branch and parameters are selected by the 5-fold cross-validation method to establish the RF prediction training model. Additionally, to illustrate the effectiveness of the method, the operational stage of Wuhan Metro Line 3 in China is taken as a case study. The results conclude that the segment spalling area, crack width, and loss rate of the rebar cross-section have a strong influence on WS. Finally, the test data are predicted, and the prediction result error index is calculated. Compared with the predictions of some traditional machine learning methods, such as support vector machines and artificial neural networks, RF prediction has the highest accuracy and is the closest to the true value, which demonstrates the accuracy of the model and its application potential.

Keyword : operational tunnels, water seepage (WS), random forest (RF), risk prediction, risk diagnosis

How to Cite
Liu, Y., Chen, H., Zhang, L., & Wang, X. (2021). Risk prediction and diagnosis of water seepage in operational shield tunnels based on random forest. Journal of Civil Engineering and Management, 27(7), 539-552.
Published in Issue
Oct 11, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.


Behrens, C. (2020). Evaluating the joint efficiency of German trade forecasts-a nonparametric multivariate approach. Applied Economics, 52, 3732–3747.

Bhattacharya, S., & Mishra, S. (2018). Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA. Journal of Petroleum Science and Engineering, 170, 1005–1017.

Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.

Cheng, S., & Huang, H. (2014). Monitoring methods of longterm water seepage in Shield Tunnel. Chinese Journal of Underground Space and Engineering, 10, 733–738.

Ding, L. Y., Zhang, L. M., Wu, X. G., Skibniewski, M. J., & Yu, Q. H. (2014). Safety management in tunnel construction: Case study of Wuhan metro construction in China. Safety Science, 62, 8–15.

Dong, F., Fang, Q., Zhang, D., Xu, H., Li, Y., & Niu, X. (2017). Analysis on defects of operational metro tunnels in Beijing. China Civil Engineering Journal, 50, 104–113.

Gao, C. L., Zhou, Z. Q., Yang, W. M., Lin, C. J., Lia, L. P., & Wang, J. (2019). Model test and numerical simulation research of water leakage in operating tunnels passing through intersecting faults. Tunnelling and Underground Space Technology, 94, 103134.

Grushka-Cockayne, Y., Jose, V. R. R., & Lichtendahl, K. C. (2017). Ensembles of overfit and overconfident forecasts. Management Science, 63, 1110–1130.

He, B. J., Zhu, J., Zhao, D. X., Gou, Z. H., Qi, J. D., & Wang, J. S. (2019). Co-benefits approach: Opportunities for implementing sponge city and urban heat island mitigation. Land Use Policy, 86, 147–157.

Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 832–844.

Hu, Z. N., Xie, Y. L., Xu, G. P., Bin, S. L., Zhangb, H. G., Lai, H. P., Liu, H. Z., & Yan, C. G. (2018). Segmental joint model tests of immersed tunnel on a settlement platform: A case study of the Hongkong-Zhuhai-Macao Bridge. Tunnelling and Underground Space Technology, 78, 188–200.

Hu, Y., Li, Y. A., Lin, J. L., Ruan, C. K., Chen, S. J., Tang, H. M., & Duan, W. H. (2019). Towards microstructure-based analysis and design for seepage water in underground engineering: Effect of image characteristics. Tunnelling and Underground Space Technology, 93, 103086.

Huang, H., & Li, Q. (2017). Image recognition for water leakage in shield tunnel based on deep learning. Chinese Journal of Rock Mechanics and Engineering, 36, 2861–2871.

Jeyisanker, K., & Gunaratne, M. (2009). Analysis of water seepage in a pavement system using the particulate approach. Computers and Geotechnics, 36(4), 641–654.

Li, Q. M., Song, L. L., List, G. F., Deng, Y. L., Zhou, Z. P., & Liu, P. (2017a). A new approach to understand metro operation safety by exploring metro operation hazard network (MOHN). Safety Science, 93, 50–61.

Li, X. J., Lin, X. D., Zhu, H. H., Wang, X. Z., Liu, Z. M. (2017b). Condition assessment of shield tunnel using a new indicator: The tunnel serviceability index. Tunnelling and Underground Space Technology, 67, 98–106.

Li, X., Zhou, S. H., Di, H. G., & Wang, P. X. (2018). Evaluation and experimental study on the sealant behaviour of double gaskets for shield tunnel lining. Tunnelling and Underground Space Technology, 75, 81–89.

Li, Z. W., Yang, X. L., & Li, T. Z. (2019). Face stability analysis of tunnels under steady unsaturated seepage conditions. Tunnelling and Underground Space Technology, 93, 103095.

Li, L. P., Sun, S. Q., Wang, J., Song, S. G., Fang, Z. D., & Zhang, M. G. (2020). Development of compound EPB shield model test system for studying the water inrushes in karst regions. Tunnelling and Underground Space Technology, 101, 103404.

Liu, J. L., Hamza, O., Davies-Vollum, K. S., & Liu, J. Q. (2018a). Repairing a shield tunnel damaged by secondary grouting. Tunnelling and Underground Space Technology, 80, 313–321.

Liu, W. L., Wu, X. G., Zhang, L. M., Wang, Y. Y., Teng, J. Y. (2018b). Sensitivity analysis of structural health risk in operational tunnels. Automation in Construction, 94, 135–153.

Liu, X., Zhang, Y. M., & Bao, Y. H. (2020). Full-scale experimental investigation on stagger effect of segmental tunnel linings. Tunnelling and Underground Space Technology, 102, 103423.

Mao, Z. J., Wang, X. K., An, N., Li, X. J., Wei, R. Y., Wang, Y. Q., & Wu, H. (2020). Water leakage susceptible areas in loess multi-arch tunnel operation under the lateral recharge conditions. Environmental Earth Sciences, 79, 368.

Mueller, S. Q. (2020). Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach. Applied Economics, 52, 4512–4528.

Pan, Y., & Zhang, L. M. (2020). Data-driven estimation of building energy consumption with multi-source heterogeneous data. Applied Energy, 268, 114965.

Pan, Y., & Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, 103517.

Pan, Y., Liu, H. M., Metsch, L. R., & Feaster, D. J. (2017). Factors associated with HIV testing among participants from substance use disorder treatment programs in the US: A machine learning approach. Aids and Behavior, 21, 534–546.

Pan, Y., Ou, S. W., Zhang, L. M., Zhang, W. J., Wu, X. G., & Li, H. (2019a). Modeling risks in dependent systems: A CopulaBayesian approach. Reliability Engineering & System Safety, 188, 416–431.

Pan, Y., Zhang, L. M., Wu, X. G., Zhang, K. N., & Skibniewski, M. J. (2019b). Structural health monitoring and assessment using wavelet packet energy spectrum. Safety Science, 120, 652–665.

Pan, Y., Zhang, L. M., Wu, X. G., & Qin, W. W., & Skibniewski, M. J. (2019c). Modeling face reliability in tunneling: A copula approach. Computers and Geotechnics, 109, 272–286.

Pan, Y., Wu, G., Zhao, Z., & He, L. (2020). Analysis of rock slope stability under rainfall conditions considering the water-induced weakening of rock. Computers and Geotechnics, 128, 103806.

Qi, C. C., & Tang, X. L. (2018). Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study. Computers & Industrial Engineering, 118, 112–122.

Qian, W. P., Qi, T. Y., Yi, H. Y., Liang, X., Jin, Z. Y., Lei, B., Li, Y., & Li, Z. Y. (2019). Evaluation of structural fatigue properties of metro tunnel by model test under dynamic load of highspeed railway. Tunnelling and Underground Space Technology, 93, 103099.

Qiu, J. L., Lu, Y. Q., Lai, J. X., Zhang, Y. W., Yang, T., & Wang, K. (2020). Experimental study on the effect of water gushing on loess metro tunnel. Environmental Earth Sciences, 79, 261.

Rahim, H., Enieb, M., Khalil, A. A., & Ahmed, A. S. H. (2015). Twin tunnel configuration for Greater Cairo metro line No. 4. Computers and Geotechnics, 68, 66–77.

Shi, J., Liu, X., Cao, W., Chen, C., & Yang, Y. (2013). The influencing factors evaluation of leakage of water diseases by improved AHP on multi-arch tunnel. Progress in Geophysics, 28, 482–487.

Wang, Y., Jin, H., & Ouyang, L. J. (2013). Real-time prediction of seepage field during tunnel excavation. Applied Mechanics and Materials, 274, 11–16.

Wang, Z., Wang, L. Z., Li, L. L., & Wang, J. C. (2014). Failure mechanism of tunnel lining joints and bolts with uneven longitudinal ground settlement. Tunnelling and Underground Space Technology, 40, 300–308.

Wang, M. N., Dong, Y. C., Yu, L., Fang, L., Wang, X. L., & Liu, D. G. (2019). Experimental and numerical researches of precast segment under radial dislocation conditions. Tunnelling and Underground Space Technology, 92, 103055.

Wang, J., Zhang, Y., Qin, Z., Song, S., & Lin, P. (2020). Analysis method of water inrush for tunnels with damaged waterresisting rock mass based on finite element method-smooth particle hydrodynamics coupling. Computers and Geotechnics, 126, 103725.

Yu, Z., Shi, X., Zhou, J., Chen, X., & Ipangelwa, T. (2019). Prediction of blast-induced rock movement during bench blasting: use of gray wolf optimizer and support vector regression. Natural Resources Research, 29(2), 843–865.

Yu, Z., Shi, X. Z., Zhou, J., Rao, D., Chen, X., Dong, W., Miao, X., & Ipangelwa, T. (2021). Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models. Engineering with Computers, 37(2), 991–1006.

Zhang, L. M., Wu, X. G., Ding, L. Y., Skibniewski, M. J., & Lu, Y. J. (2016). BIM-based risk identification system in tunnel construction. Journal of Civil Engineering and Management, 22(4), 529–539.

Zhang, P., Yin, Z. Y., Jin, Y. F., & Chan, T. H. T. (2020a). A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest. Engineering Geology, 265, 105328.

Zhang, P., Wu, H. N., Chen, R. P., Chan, & T. H. T. (2020b). Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study. Tunnelling and Underground Space Technology, 99, 103383.

Zhang, G., He, B. J., & Dewancker, B. J. (2020c). The maintenance of prefabricated green roofs for preserving cooling performance: A field measurement in the subtropical city of Hangzhou, China. Sustainable Cities and Society, 61, 102314.

Zhang, H., Zhou, J., Armaghani, D. J., Tahir, M. M., Pham, B. T., & Huynh, V. V. (2020d). A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration. Applied Sciences, 3(10), 869.

Zho, S. T., Zhai, G. F., Lu, Y. W., & Shi, Y. J. (2019). The development of urban mega-projects in China: A case study of Nantong’s metro project. Environment and Planning B: Urban Analytics and City Science.

Zhou, J., Li, X. B., & Mitri, H. S. (2015). Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Natural Hazards, 79(1), 291–316.

Zhou, J., Shi, X. Z., Du, K., Qiu, X. Y., Li, X. B., & Mitri, H. S. (2017). Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel. International Journal of Geomechanics, 6(17), 04016129.

Zhou, J., Li, E. M., Wei, H. X., Li, C. Q., Qiao, Q. Q., & Armaghani, D. J. (2019). Random forests and cubist algorithms for predicting shear strengths of rockfill materials. Applied Sciences, 9(8), 1621.

Zhou, J., Asteris, P. G., Armaghani, D. J., & Pham, B. T. (2020a). Prediction of ground vibration induced by blasting operations through the use of the bayesian network and random forest models. Soil Dynamics and Earthquake Engineering, 139, 106390.

Zhou, J., Koopialipoor, M., Li, E. M., & Armaghani, D. J. (2020b). Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system. Bulletin of Engineering Geology and the Environment, 79(8), 4265–4279.

Zhou, J., Qiu, Y., Zhu, S., Armaghani, D. J., Li, C., Nguyen, H., & Yagiz, S. (2021a). Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Engineering Applications of Artificial Intelligence, 97, 104015.

Zhou, J., Qiu, Y. G., Armaghani, D. J., Zhang, W. G., Li, C. Q., Zhu, S. L., Tarinejad, R., & Pham, B. T. (2021b). Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques. Geoscience Frontiers, 3(12), 101091.