Intelligent Feedback Analysis of Fluid–Solid Coupling of Surrounding Rock of Tunnel in Water-Rich Areas
Source abstract
Abstract
To realize parameter feedback optimization of tunnel construction in water-rich areas, a feedback analysis method for tunnel parameters under fluid–solid coupling conditions was established based on an intelligent optimization algorithm. Firstly, the numerical calculation model was established and solved using the fluid–solid coupling model. In orthogonal design analysis, the displacement of surrounding rock and pore water pressure distribution in different rock mass parameter combinations were obtained, and the learning samples needed for machine learning were established. The input group was surrounding rock displacement and pore water pressure, and the output was rock mass parameters. Then, the Gaussian process algorithm was used to obtain the nonlinear mapping relationship contained in the learning samples. A differential evolution algorithm was used to optimize the critical parameters involved in this process. Furthermore, according to the established regression model and the measured displacement and pore water pressure in the research area, differential evolution was used again to optimize the rock mass parameters and obtain the parameter feedback analysis results. Finally, the inversion values were compared with the actual measured values, and the reliability of the surrounding rock parameters obtained from the feedback analysis was verified, providing an effective method for obtaining surrounding rock parameters for similar projects.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geotechnical Engineering and Analysis · Rock Mechanics and Modeling · Tunneling and Rock Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Nanchang Rail Transit Group Co., Ltd., Nanchang 330013, China
Highway and Bridge Institute, Dalian Maritime University, Dalian 116026, China
Funding
National Natural Science Foundation of China · Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University
References (32)
Show source-provided references
1. Size effect of rock messes and associated representative element properties
2. Xu, K., Shen, C.H., and Hu, Y.T. (2017). The Optimization Research on the Foundation Treatment of Sand Drain on Vietnam Highway. Highw. Eng., 240–243.
3. Information Feedback Analysis in Deep Excavations10.1061/(ASCE)1532-3641(2008)8:1(91) ↗
4. Zeng, Y.Z., Hu, L., and Huang, M. (2018). Research of Surrounding Rock Parametric Inversion on Mountain Tunnel. Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/J. Wuhan Univ. Technol. (Transp. Sci. Eng.), 72–76.
5. Finite element applications in the characterization of elastic solids10.1016/0020-7683(71)90015-1 ↗
6. Rock parameters inversion for estimating the maximum heights of two failure zones in overburden strata of a coal seam10.1016/j.mstc.2010.12.008 ↗
7. Ge, H.W., Liang, Y.C., and Liu, W. (2004). Applications of artificial neural networks and genetic algorithms to rock mechanics. Chin. J. Rock Mech. Eng., 1542–1550.
8. Zhang, X., Yang, F., Ren, X.H., and Zhang, D.F. (2018). Back Analysis of Surrounding Rock Mechanical Parameters of Underground Caverns Based on Genetic Algorithm. Mod. Tunn. Technol., 53–58.
9. Zhang, T.J., and Xu, L. (2014). Inversion of Mechanical Parameters of Slope Rock Based on Asynchronous Particle Swarm Optimization. J. China Three Gorges Univ., 38–41.
10. Evaluating stability of underground entry-type excavations using multivariate adaptive regression splines and logistic regression10.1016/j.tust.2017.07.013 ↗
11. Probabilistic evaluation of ground-support interaction for deep rock excavation using artificial neural network and uniform design10.1016/j.tust.2012.04.014 ↗
12. Estimation of strength parameters of rock using artificial neural networks10.1007/s10064-010-0301-3 ↗
13. Study on genetic-neural net work method of displacement back analysis
14. Application of BP networkand genetic algorithm to displacement back analysis of rock slopes
15. Application of Genetic Algorithm Based BP Neural Network to Parameter Inversion of Surrounding Rock and Deformation Prediction
16. Wang, J.X., Dong, J.H., and Chen, S.L. (2018). Multi-parameters Inversion of Stress-Seepage-Damage Coupling Model Based on DEPSO Intelligent Algorithm. J. Basic Sci. Eng., 872–887.
17. Prediction of Steering Behaviors on Curves Based on BP Neural Network Optimized by Modified PSO
18. Rasmussen, C.E., and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning, MIT Press.10.7551/mitpress/3206.001.0001 ↗
19. Study of determining permeability coefficient in water inrush numerical calculation of subsea tunnel
20. Probabilistic back analysis of rock mechanical parameters based on displacement and relaxation depth
21. Zhang, Y.X., and Hou, Z.J. (2022). Back analysis of rock mass parameters based on support vector machine and Bayesian method. Yangtze River, 186–192.
22. Zhang, Y.X. (2022). Probabilistic Inverse Analysis of Geotechnical Parameters and Deformation Prediction based on Bayesian Theory, Zhejiang University.
23. Sun, Y. (2019). Study on Back Analysis Method Based on Multi-Objective Optimization and Bayesian Theory for Geotechnical Engineering, Wuhan University.
24. Differential Evolution—A Simple and Efficient Heuristic for global Optimization over Continuous Spaces10.1023/A:1008202821328 ↗
25. An effective differential evolution with level comparison for constrained engineering design10.1007/s00158-009-0454-5 ↗
26. Luo, Q.K., Wu, J.F., Yang, Y., Wu, J.C., and Ma, S.F. (2016). Identification of the spatial variability of aquifer hydraulic conductivity. J. Nanjing Univ. (Nat. Sci.), 448–455.
27. Analysis of the Uncertainty of Groundwater Numerical Simulation Based on the DREAM Algorithm
28. Zhang, W.G., Gu, H.L., Zhang, Q., Wang, L., and Wang, L.Q. (2022). Probabilistic back analysis of soil parameters and displacement prediction of unsaturated slopes using Bayesian updating. Rock Soil Mech., 1112–1122.
29. Wu, F., Zhang, L.L., Zheng, W.T., and Wei, X. (2018). Probabilistic back analysis method for unsaturated soil slopes with fluid-solid coupling process based on polynomial chaos expansion. Chin. J. Geotech. Eng., 2215–2222.
30. Inverse analysis on seepage of dams based on fluid-solid coupling
31. Multi-objective probabilistic inverse analysis of rainfall-induced landslide based on time-varied data
32. Xu, C.Y., Han, L.J., Tian, M.L., and Wang, Y.J. (2020). Coupled Grouting Reinforcement Mechanism and Displacement Back Analysis of Mechanical Parameters of Surrounding Rock. Saf. Coal Mines, 155–160.