Safety Assessment of Tunnel Lining Structure with Underlying Cavities Based on Fuzzy Comprehensive Evaluation in Mudstone Stratum


  • Yiming Wang Universiti Teknologi MARA (UiTM)
  • Haoxuan Wang Luoyang Polytechnic



Fuzzy Comprehensive Evaluation, Underlying Cavity, Field Data Analysis, Numerical Analysis


This paper presents a study on the structural safety assessment of tunnel linings with underlying cavities based on a fuzzy comprehensive evaluation model in mudstone stratum. The weight and membership degree are determined using an improved method: field data analysis and numerical simulation. Field data analysis revealed that the proportion of cavities in the surrounding rocks of class ? and at the vault was the largest. Cavity length between 1m and 3m and cavity depth between 20cm and 40cm occupied the most significant proportion. Additionally, the impact of defect parameter changes on structural safety was investigated through numerical simulation. It is well known that the lining safety factors are greatly impacted by changes in surrounding rock classifications, cavity locations and depths. In contrast, changes in cavity lengths do not significantly affect the lining safety. The developed fuzzy comprehensive evaluation model consists of factor set, comment set, membership degree and weight set. They are determined according to the previous field data analysis and numerical analysis results. The developed evaluation model is validated by means of the numerical simulation based on the evaluation work of the specific engineering case.


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Aliahmadi, A, Sadjadi, S., & Eskandari, M. (2011). Design a new intelligence expert decision making using game theory and fuzzy ahp to risk management in design, construction, and operation of tunnel projects (case studies: Resalat Tunnel). The International Journal of Advanced Manufacturing Technology, 53: 789-798.

An, Y., Li, J., Zhao, D., Zhang, Y., & Yang, G. (2020). Comprehensive extension assessment on tunnel structure health. Journal of Railway Science and Engineering, 17(2) :422-428.

Chen, B., Tian, Z., Chen, Z., Zhang, Z., & Sun, W. (2018). Structural safety evaluation of in-service tunnels using an adaptive neuro-fuzzy inference system. Journal of Aerospace Engineering, 31(5): 04018073.

Chen, J., & Zhang, Y. (2015). Quantitative risk assessment model of tunnel construction under passing existing bridges. Journal of Central South University (Science and Technology), 46(5) :1862-1868.

China Railway Eryuan Engineering Group Co. Ltd. (2016). Code for Design of Railway Tunnel (TB 10003-2016). Beijing, China: China Railway Publishing House Co., Ltd.

Fattahi, H. Farsangi, M.A.E., Shojaee, S., & Mansouri, H. (2014).Selection of a suitable method for the assessment of excavation damage zone using fuzzy AHP in aba saleh almahdi tunnel, iran. Arabian Journal of Geosciences, 8(5): 2863–2877.

Gong, J., Wang, W., Li, X., Zhu, Y. (2023). Statistics on railway tunnels in China by the end of 2022 and overview of key tunnels of projects newly put into operation in 2022. Tunnel Construction, 43(4), 721-738.

Han, W., Jiang, Y., Li, N., Koga, D., Sakaguchi, O. & Chen, H. (2021). Safety evaluation and failure behavior of degraded tunnel structure with compound diseases of voids and lining defects. Arabian Journal of Geosciences. 14: 1531.

Hou, J., Yang, Q., & Guo, C. (2015). Study on Comprehensive Evaluation of Operation Safety of Long Highway Tunnel. Railway standard design, 59(11) :88-91. 2954.2015.11.021

Hu, Q., Zhou, B., Wang, F. & Niu, Z. (2018). Structural safety assessment technology of long highway tunnel based on fuzzy analytic hierarchy process. Journal of Natural Disasters, 27(4) :41-49.

Lai, J., Qiu, J., Fan, H., Chen, J., Hu, Z., Zhang, Q., & Wang, J. (2017). Structural safety assessment of existing multiarch tunnel: a case study. Advances in Materials Science and Engineering, 1-11.

Li, K. & Wang, Z. (2017). Study of disaster types and prevention methods of railway tunnel during operation period based on statistical theory. Tunnel Construction, 37(2),150-159.

Li, S., Zhang, Y., & Han, S. (2021). Safety inspection system and comprehensive evaluation method for concrete structure of gas pipeline tunnel based on fuzzy mathematics. Advances in Mechanical Engineering .13(9): 168781402110460.

Li, X., Ju, M., Yao, Q., Zhou, J., & Chong, Z. (2016). Numerical investigation of the effect of the location of critical rock block fracture on crack evolution in a gob-side filling wall. Rock Mechanics and Rock Engineering, 49: 1041-1058.

Liu, S., Shi, Y., Sun, R., & Yang, J. (2020). Damage behavior and maintenance design of tunnel lining based on numerical evaluation. Engineering Failure Analysis,109, 104-113.

Min, B., Zhang, X., Zhang C., Gong, Y., & Yuan, T. (2018). Mechanical behavior of double-arch tunnels under the effect of voids on the top of the middle wall. Symmetry, 10(12):703.

Qiu, W., Liu, Y., Lu, F. & Huang, G. (2020). Establishing a sustainable evaluation indicator system for railway tunnel in China. Journal of Cleaner Production, 268: 122-150.

Rao, J., Xie, T. & Liu, Y. (2015). Fuzzy evaluation model for in-service karst highway tunnel structural safety. KSCE Journal of Civil Engineering, 20: 1242-1249.

Wang, Y., Yang, J., Luo, L., & Lin, H. (2015). Statistical analysis and fuzzy synthetic evaluation of water pressure load on lining of karst tunnel. Journal of Zhengzhou University (Engineering Science), 36(2):38-42.

Xu, X., Tong, L., Liu, S. & Li, H. (2019). Evaluation model for immersed tunnel health state: a case study of Honggu Tunnel, Jiangxi Province, China. Tunnelling and Underground Space Technology,90: 239–248.

Yan, X., Li, H., Liu, F. & Liu, Y. (2021). Structural safety evaluation of tunnel based on the dynamic monitoring data during construction. Shock and Vibration, 2021: 1-11.

Ye, F., Qin, N., Liang, X., Ouyang, A., Qin, Z., Su, E., 2021. Analyses of the defects in highway tunnels in China. Tunnelling and Underground Space Technology, 107, 103658.

Zhang, G., Chen, W., Jiao, Y., Wang, H., & Wang, C. (2020). A failure probability evaluation method for collapse of drill-and-blast tunnels based on Multistate Fuzzy Bayesian Network. Engineering Geology, (276): 105-112.

Zhang, J, Liu, X, Ren, T, & Mang, A. (2019) Structural behavior of reinforced concrete segments of tunnel linings strengthened by a steel-concrete composite. Composites Part B Engineering, 107444.

Zhang, S., Zhang, D., & Liu, C. et al. (2017). Long-term monitoring and analysis of lining cracks in operating highway tunnels. Modern Tunnelling Technology, 54 (3): 17-25.

Zhang, X., Nguyen, H., Bui, X., Le, H. A., Nguyen-Thoi, T., Moayedi, H., & Mahesh, V. (2020). Evaluating and predicting the stability of roadways in tunnelling and underground space using Artifificial Neural Network-Based Particle Swarm Optimization. Tunnelling and Underground Space Technology, (103) :517-529.

Zhang, X., Ye, Z., Min, B., & Xu, Y. (2019). Effect of voids behind lining on the failure behavior of symmetrical double-arch tunnels. Symmetry, 11(10): 1321-1326.

Zhang, Y., Shi,Y., Zhao,Y., Fu, L., & Yang,J. (2017). Determining the cause of damages in a multiarch tunnel structure through field investigation and numerical analysis. Journal of Performance of Constructed Facilities,31(3), 04016104.

Zhao, Y., Liu, C., Zhang, Y., Yang, J., & Feng, T. (2019). Damaging behavior investigation of an operational tunnel structure induced by cavities around surrounding rocks, Engineering Failure Analysis, 99, 203–209.

Zhou, J. (2018). Defect investigation and operation management of highway tunnels in Henan Province. Modern Tunnelling Technology. 55(2) :1-10.




How to Cite

Wang, Y., & Wang, H. (2024). Safety Assessment of Tunnel Lining Structure with Underlying Cavities Based on Fuzzy Comprehensive Evaluation in Mudstone Stratum . Journal of Applied Engineering and Technological Science (JAETS), 5(2), 772–790.