TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass

Abstract

The layout of underground engineering objects significantly influences the stability of the surrounding rock mass and construction safety. Despite advancements toward intellectualization and informatization in design optimization and safety assessments, mechanical analysis-based engineering computations still face certain impediments. Consequently, this paper proposes a comprehensive framework integrating tunnel information modelling (TIM), finite element method (FEM) and machine learning (ML) technology to optimize the tunnel longitudinal orientation. It also delves into the specifics of addressing the challenges associated with each technology. The framework encompasses three phases: parametric modelling based on TIM, automatic numerical simulation based on FEM, and intelligent optimization leveraging ML. Initially, geometric models of the geological formations and engineering structures are constructed on the TIM platform. Subsequently, data conversion is facilitated through the proposed transformation interface. Python codes are programmed to realize automatic processing of numerical simulation and results are extracted to the ML algorithm for the prediction model. An optimization algorithm is implanted in the numerical stream file to retrieve the optimal relative intersection angle between the tunnel axis and the trend of rocks. A case study is conducted to evaluate the feasibility of the proposed framework. Results demonstrate a substantial improvement in design and optimization accuracy and efficiency. This framework holds immense potential to propel the intellectualization and informatization of underground engineering.

Keywords

Underground engineering / Tunnel information modeling / Machine learning / Design optimization / Layered rock mass

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Hui Li: Writing - original draft, Validation, Methodology, Funding acquisition, Formal analysis, Data curation. Weizhong Chen: Writing - review & editing, Supervision, Resources, Funding acquisition, Conceptualization. Xiaoyun Shu: Writing - original draft, Validation, Investigation, Data curation. Xianjun Tan: Writing - review & editing, Visualization, Supervision, Conceptualization. Qun Sui: Validation, Methodology.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work is supported by the National Natural Science Foundation of China (Grant Nos. 51991392, 51922104, 52179116, and 42407262), the support of Key Deployment Projects of Chinese Academy of Sciences (Grant No. ZDRW-ZS-2021-3), and the support of the National Key Research and Development Program of China (Grant No. 2021YFC3100800).

References

[1] Al-Thanoon, N. A., Qasim, O. S., & Algamal, Z. Y. (2019). A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics. Chemometrics and Intelligent Laboratory Systems, 184, 142-152.

[2] Bai, X. D., Cheng, W. C., Ong, D. E. L., & Li, G. (2021). Evaluation of geological conditions and clogging of tunneling using machine learning. Geomechanics and Engineering, 25(1), 59-73.

[3] Bruland, A. (1998). Hard rock tunnel boring. [Doctoral dissertation, Norwegian University of Science and Technology, Norway].

[4] Bui, H. G., Cao, B. T., Freitag, S., Hackl, K., & Meschke, G. (2023). Surrogate modeling for interactive tunnel track design using the cut finite element method. Engineering with Computers, 39, 4025-4043.

[5] Bui, H. G., Schillinger, D., Zendaki, Y., & Meschke, G. (2022). A cutfembased framework for numerical simulations of machine-driven tunnels with arbitrary alignments. Computers and Geotechnics, 144, 104637.

[6] Cao, B. T., Obel, M., Freitag, S., Heußner, L., Meschke, G., & Mark, P. (2022). Real-time risk assessment of tunneling-induced building damage considering polymorphic uncertainty. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 8(1), 04021069.

[7] Cheng, J. C. P., Lu, Q. Q., & Deng, Y. C. (2016). Analytical review and evaluation of civil information modeling. Automation in Construction, 67, 31-47.

[8] Claesson, J., & Bohloli, B. (2002). Brazilian test: stress field and tensile strength of anisotropic rocks using an analytical solution. International Journal of Rock Mechanics and Mining Sciences, 39(8), 991-1004.

[9] Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. In Proceedings of the 10th International Conference on Neural Information Processing Systems (pp.155-161). Denver, Colorado.

[10] Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2008). BIM handbook:a guide to building information modeling for owners, managers, designers, engineers and contractors (first ed.). Hoboken, New Jersey: John Wiley & Sons Inc..

[11] Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2011). BIM handbook:A guide to building information modeling for owners, managers, designers, engineers, and contractors ( 2nd ed.). Hoboken, New Jersey: John Wiley & Sons Inc..

[12] Fabozzi, S., Biancardo, S. A., Veropalumbo, R., & Bilotta, E. (2021). IBIM based approach for geotechnical and numerical modelling of a conventional tunnel excavation. Tunnelling and Underground Space Technology, 108, 103723.

[13] Fossum, A. F. (1985). Effective elastic properties for a randomly jointed rock mass. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 22(6), 467-470.

[14] Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). Why 70/30 or 80/20 relation between training and testing sets: A pedagogical explanation. Technical Report: UTEP-CS-18-09. Departmental Technical Reports (CS). https://scholarworks.utep.edu/cs_techrep/1209.

[15] Guo, H. W., Zhuang, X. Y., Fu, X. L., Zhu, Y. Z., & Timon, R. (2023). Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials. Computational Mechanics, 77(3), 513-524.

[16] Hao, X. J., Zhang, Q., Sun, Z. W., Wang, S. H., Yang, K., Ren, B., Yu, G. F., Zhou, W., Chen, B. L., & Zhang, X. Y. (2021). Effects of the major principal stress direction respect to the long axis of a tunnel on the tunnel stability: physical model tests and numerical simulation. Tunnelling and Underground Space Technology, 114, 103993.

[17] Huang, M. Q., Ninić J., & Zhang, Q. B. (2021). BIM, machine learning and computer vision techniques in underground construction: current status and future perspectives. Tunnelling and Underground Space Technology, 108, 103677.

[18] Huang, M. Q., Zhu, H. M., Ninić J., & Zhang, Q. B. (2022). Multi-LOD BIM for underground metro station: interoperability and design-todesign enhancement. Tunnelling and Underground Space Technology, 119, 104232.

[19] Jaeger, J. C., Cook, N. G. W., & Zimmerman, R. W. (2007). Fundamental of rock mechanics. John Wiley.

[20] Jeong, S., Hou, R., Lynch, J. P., Sohn, H., & Law, K. H. (2017). An information modeling framework for bridge monitoring. Advances in Engineering Software, 114, 11-31.

[21] Kang, F., Liu, X., Li, J. J., & Li, H. J. (2022). Multi-parameter inverse analysis of concrete dams using kernel extreme learning machinesbased response surface model. Engineering Structures, 256, 113999.

[22] Kashani, A. R., Chiong, R., Mirjalili, S., & Gandomi, A. H. (2021). Particle swarm optimization variants for solving geotechnical problems: review and comparative analysis. Archives of Computational Methods in Engineering, 28, 1871-1927.

[23] Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks (pp.1942-1948). Perth, WA, Australia.

[24] Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671-680.

[25] Li, F. T., Wang, Q. Y., Hu, Z. P., Zhang, Y. H., Ren, X., & An, X. X. (2020). The effect of intersection angle on the failure mechanism of utility tunnel. Advances in Civil Engineering, 2020, 8864676.

[26] Li, H., Chen, W. Z., Tan, X. J., & Chen, E. Y. (2022). Digital design and stability simulation for large underground powerhouse caverns with parametric model based on BIM-based framework. Tunnelling and Underground Space Technology, 123, 104375.

[27] Li, H., Chen, W. Z., Tan, X. Y., & Tan, X. J. (2023a). Back analysis of geomechanical parameters for rock mass under complex geological conditions using a novel algorithm. Tunnelling and Underground Space Technology, 136, 105099.

[28] Li, H., Chen, W. Z., & Tan, X. J. (2023b). Displacement-based back analysis of mitigating the effects of displacement loss in underground engineering. Journal of Rock Mechanics and Geotechnical Engineering, 15(10), 2626-2638.

[29] Li, H., Chen, W. Z., & Tan, X. J. (2024). Estimation of tunnel axial orientation in the interlayered rock mass using a comprehensive algorithm. Journal of Rock Mechanics and Geotechnical Engineering, 16 (7), 2579-2590.

[30] Li, S. Z., Sha, P., Wu, F. Q., & Wu, J. (2018). Anisotropic characteristics analysis of deformation of layered rock mass. Rock and Soil Mechanics, 39(S2), 366-373 (in Chinese).

[31] Liu, B. K., Lu, W. Z., Thomas, O., Zhuang, X. Y., & Timon, R. (2024a). Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites. Composite Structures, 327, 117601.

[32] Liu, B. K., Wang, Y. Z., Timon, R., Thomas, O., & Lu, W. Z. (2024b). Multi-scale modeling in thermal conductivity of polyurethane incorporated with phase change materials using physics-informed neural networks. Renewable Energy, 220, 119565.

[33] Mahmoodzadeh, A., Mohammadi, M., Abdulhamid, S. N., Ali, H. F. H., Ibrahim, H. H., & Rashidi, S. (2022). Forecasting tunnel path geology using Gaussian process regression. Geomechanics and Engineering, 28 (4), 359-374.

[34] Ninić J., Alsahly, A., Vonthron, A., Bui, H. G., Koch, C., König, M., & Meschke, G. (2021). From digital models to numerical analysis for mechanized tunnelling: a fully automated design-throughanalysis workflow. Tunnelling and Underground Space Technology, 107, 103622.

[35] Ninić J., Gamra, A., & Ghiassi, B. (2024). Real-time assessment of tunnelling-induced damage to structures within the building information modelling framework. Underground Space, 14, 99-117.

[36] Park, B., & Min, K. B. (2015). Bonded-particle discrete element modeling of mechanical behavior of transversely isotropic rock. International Journal of Rock Mechanics and Mining Sciences, 76, 243-255.

[37] Saiprasad, N., Kalam, A., & Zayegh, A. (2019). Triple bottom line analysis and optimum sizing of renewable energy using improved hybrid optimization employing the genetic algorithm: a case study from India. Energies, 12(3), 349.

[38] Sanguinetti, P., Abodelmohsen, S., Lee, J., Lee, J., Sheward, H., & Eastman, C. (2012). General system architecture for BIM: an integrated approach for design and analysis. Advanced Engineering Informatics, 26(2), 317-333.

[39] Sharafat, A., Khan, M. S., Latif, K., & Seo, J. (2021). BIM-based tunnel information modeling framework for visualization, management, and simulation of drill-and-blast tunneling projects. Journal of Computing in Civil Engineering, 35(2), 04020068.

[40] Su, J. H., & Jiang, B. Y. (2016). ABAQUS python secondary development guide. Posts & Telecom press (in Chinese).

[41] Tang, F. L., Ma, T., Guan, Y. S., & Zhang, Z. X. (2020). Parametric modeling and structure verification of asphalt pavement based on BIM-ABAQUS. Automation in Construction, 111, 103066.

[42] Tawelian, L. R., & Mickovski, S. B. (2016). The implementation of geotechnical data into the BIM process. Procedia Engineering, 143, 734-741.

[43] Wang, W., Fan, H. Z., & Xi, G. (2018). An extension of advancing front technique on new target surface after virtual topology operations. Advances in Engineering Software, 124, 42-52.

[44] Wu, S. H., Zhang, N., Luo, X. W., & Lu, W. Z. (2022). Multi-objective optimization in floor tile planning: coupling BIM and parametric design. Automation in Construction, 140, 104384.

[45] Xie, H. P., Zhang, R., Ren, L., Zhang, A. L., Zhang, Z. L., Deng, J. H., Xu, Z. X., Zhang, G. Z., Feng, T., Wang, D., Wang, Z. W., Yi, X. J., Lin, Z. H., Li, J. Y., Zhang, Z. T., Yuan, D., & Jia, Z. Q. (2022a). Analysis and thoughts on surrounding rock mass catastrophe of deepburied tunnels along Sichuan-Tibet railway. Advanced Engineering Sciences, 54(2), 1-20 (in Chinese).

[46] Xie, P., Zhang, R. J., Zheng, J. J., & Li, Z. Q. (2022b). Automatic safety evaluation and visualization of subway station excavation based on BIM-FEM/FEM integrated technology. Journal of Civil Engineering and Management, 28(4), 320-336.

[47] Xu, D. P., Feng, X. T., Chen, D. F., Zhang, C. Q., & Fan, Q. X. (2017). Constitutive representation and damage degree index for the layered rock mass excavation response in underground openings. Tunnelling and Underground Space Technology, 64, 133-145.

[48] Zhang, P., Jin, Y. F., & Yin, Z. Y. (2021). Machine learning-based uncertainty modelling of mechanical properties of soft clays relating to time-dependent behavior and its application. International Journal for Numerical and Analytical Methods in Geomechanics, 45(11), 1588-1602.

[49] Zhang, Y., Su, G. S., Li, Y., Wei, M. D., & Liu, B. C. (2020). Displacement back-analysis of rock mass parameters for underground caverns using a novel intelligent optimization method. International Journal of Geomechanics, 20(5), 04020035.

[50] Zhu, S., Zheng, J. H., Zhu, Z. D., Zhu, Q. Z., & Zhou, L. M. (2022). Experiments on three-dimensional flaw dynamic evolution of transparent rock-like material under osmotic pressure. Tunnelling and Underground Space Technology, 128, 104624.

[51] Zhu, S., Zhang, Y. L., Shao, J. F., Zhu, Z. D., Zhang, X., & Wu, J. Y. (2024). Experimental and the theoretical study on crack growth using rock-like resin samples containing inherent fissures and its numerical assessment. Rock Mechanics and Rock Engineering, 57, 4815-4834.

[52] Zhu, W. S., Yang, W. M., Li, X. J., Xiang, L., & Yu, D. J. (2014). Study on splitting failure in rock masses by simulation test, site monitoring and energy model. Tunnelling and Underground Space Technology, 41, 152-164.