Prediction of Surface Settlement in Shield-Tunneling Construction Process Using PCA-PSO-RVM Machine Learning
Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 3
Abstract
Surface settlement is one of the key engineering issues during shield construction process. In order to accurately predict surface settlement, this paper proposes a new machine learning method based on relevance vector machine (RVM), principal component analysis (PCA), and particle swarm optimization (PSO). Taking Beijing Metro Line 6 as a case study, the PCA-PSO-RVM model is used to make the prediction and compared with the prediction results of the RVM model using the same samples. In order to evaluate the reliability of the model, three evaluation indexes including mean relative error (MRE), root mean square error (RMSE), and Theil inequality coefficient (TIC) were calculated, and sensitivity analysis was carried out on them. The results show that the minimum relative error between PCA-PSO-RVM and the actual value is only 0.06%. The calculated MRE, RMSE, and TIC are 0.17%, 0.0714 mm, and 0.027%, respectively, which shows that PCA-PSO-RVM model has higher prediction accuracy, smaller deviations, and higher reliability compared with the three other models. Through sensitivity analysis, it is found that the weighted average internal friction angle () has the most significant impact on the surface settlement, which should be focused on in relevant research.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to acknowledge the financial support from the National Natural Science Foundation of China under Grant No. 52068016. The work in this paper was also supported by the Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering (Grant No. 19-Y-21-9, 20-Y-XT-01), the High Level Innovation Team and Outstanding Scholar Program of Universities in Guangxi Province (Grant No. 202006), and the Guangxi Natural Science Foundation under Grant Nos. 2020GXNSFAA297118 and 2020GXNSFAA159125.
References
Azhdar, R., and A. Nazemi. 2020. “Modeling of incentive-based policies for demand management for the Tehran subway.” Travel Behav. Soc. 20 (Jul): 174–180. https://doi.org/10.1016/j.tbs.2020.03.014.
Borthakur, N., and A. Dey. 2020. “Evaluation of group capacity of micropile in soft clayey soil from experimental analysis using SVM-based prediction model.” Int. J. Geomech. 20 (3): 04020008. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001606.
Chen, R., P. Zhang, X. Kang, Z. Zhong, Y. Liu, and H. Wu. 2019. “Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods.” Soils Found. 59 (2): 284–295. https://doi.org/10.1016/j.sandf.2018.11.005.
Chou, W., and A. Bobet. 2002. “Predictions of ground deformations in shallow tunnels in clay.” Tunnelling Underground Space Technol. 17 (1): 3–19. https://doi.org/10.1016/S0886-7798(01)00068-2.
Du, R., and S. Zheng. 2020. “Agglomeration, housing affordability, and new firm formation: The role of subway network.” J. Hous. Econ. 48 (Jun): 101668. https://doi.org/10.1016/j.jhe.2020.101668.
Galuzio, P., E. de Vasconcelos Segundo, L. Coelho, and V. Mariani. 2020. “MOBOpt—Multi-objective Bayesian optimization.” SoftwareX 12 (Jul): 100520. https://doi.org/10.1016/j.softx.2020.100520.
Hao, R., Y. Ji, and Z. Ni. 2015. “Study on predicting the surface settlement for shield tunneling based on DEACO-WNN.” J. Railway Eng. Soc. 32 (1): 12–16.
Kasper, T., and G. Meschke. 2006. “On the influence of face pressure, grouting pressure and TBM design in soft ground tunneling.” Tunnelling Underground Space Technol. 21 (2): 160–171. https://doi.org/10.1016/j.tust.2005.06.006.
Kennedy, J., and R. Eberhart. 1995. “Particle swarm optimization.” In Vol. 4 of Proc., ICNN‘95—Int. Conf. on Neural Networks, 1942–1948. New York: IEEE.
Li, S. H., M. J. Zhang, and P. F. Li. 2021. “nalytical solutions to ground settlement induced by ground loss and construction loadings during curved shield tunneling.” J. Zhejiang Univ.-Sci. A 22 (4): 296–313. https://doi.org/10.1631/jzus.A2000120.
Liu, B., Z. Yu, Y. Han, Z. Wang, R. Zhang, and S. Wang. 2020. “Analytical solution for the response of an existing tunnel induced by above-crossing shield tunneling.” Comput. Geotech. 124 (Apr): 103624. https://doi.org/10.1016/j.compgeo.2020.103624.
Ma, Z., and T. Hanson. 2020. “Bayesian nonparametric test for independence between random vectors.” Comput. Stat. Data Anal. 149 (Sep): 106959. https://doi.org/10.1016/j.csda.2020.106959.
Marini, F., and B. Walczak. 2015. “Particle swarm optimization (PSO). A tutorial.” Chemom. Intell. Lab. Syst. 149 (Dec): 153–165. https://doi.org/10.1016/j.chemolab.2015.08.020.
Murray_smith, D. 1998. “Methods for the external validation of continuous system simulation models: A review.” Math. Comput. Modell. Dyn. Syst. 4 (1): 5–31. https://doi.org/10.1080/13873959808837066.
Ocak, I., and S. Seker. 2013. “Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes.” Environ. Earth Sci. 70 (3): 1263–1276. https://doi.org/10.1007/s12665-012-2214-x.
Peck, R. B. 1969. “Deep excavations and tunneling in soft ground.” In Proc., 7th Int. Conf. on Soil Mechanics and Foundation Engineering, 225–290. Mexico City: State of the Art Report.
Salimi, A., J. Rostami, C. Moormann, and A. Delisio. 2016. “Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs.” Tunnelling Underground Space Technol. 58 (Sep): 236–246. https://doi.org/10.1016/j.tust.2016.05.009.
Sharghi, M., H. Chakeri, and Y. Ozcelik. 2017. “Investigation into the effects of two component grout properties on surface settlements.” Tunnelling Underground Space Technol. 63 (Mar): 205–216. https://doi.org/10.1016/j.tust.2017.01.004.
Singh, D., V. Aromal, and A. Mandal. 2018. “Prediction of surface settlements in subway tunnels by regression analysis.” Int. J. Geotech. Eng. 14 (7): 836–842. https://doi.org/10.1080/19386362.2018.1477294.
Su, M., Y. Liu, Y. Xue, K. Cheng, Z. Ning, G. Li, and K. Zhang. 2021. “Detection method of karst features around tunnel construction by multi-resistivity data-fusion pseudo-3D-imaging based on the PCA approach.” Eng. Geol. 288 (Jul): 106127. https://doi.org/10.1016/j.enggeo.2021.106127.
Tipping, M. 2001a. “The relevance vector machine.” In Vol. 12 of Advances in neural information processing systems, 652–658. Cambridge, MA: MIT Press.
Tipping, M. 2001b. “Sparse Bayesian learning and the relevance vector machine.” J. Mach. Learn. Res. 1 (3): 211–244.
Verruijt, A., and J. Booker. 1998. “Surface settlements due to deformation of a tunnel in an elastic half plane.” Géotechnique 48 (5): 709–713. https://doi.org/10.1680/geot.1998.48.5.709.
Wang, F., B. Gou, and Y. Qin. 2013. “Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine.” Comput. Geotech. 54 (Oct): 125–132. https://doi.org/10.1016/j.compgeo.2013.07.004.
Wang, G., and Z. Ma. 2017. “Hybrid particle swarm optimization for first-order reliability method.” Comput. Geotech. 81 (Jan): 49–58. https://doi.org/10.1016/j.compgeo.2016.07.013.
Wang, J., A. Mohammed, E. Macioszek, M. Ali, D. Ulrikh, and Q. Fang. 2022. “A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance.” Buildings 12 (7): 919. https://doi.org/10.3390/buildings12070919.
Wen, Z., and Y. Liu. 2004. “Reactive power optimization based on PSO in a practical power system.” In IEEE power engineering society general meeting, 239–243. Piscataway, NJ: IEEE.
Yan, H., K. Liu, C. Xu, and W. Zheng. 2022. “A novel method for identifying geomechanical parameters of rock masses based on a PSO and improved GPR hybrid algorithm.” Sci. Rep. 12 (1): 1–18. https://doi.org/10.1038/s41598-022-09947-7.
Yao, Y., Y. Liu, Y. Yu, H. Xu, W. Lv, Z. Li, and X. Chen. 2013. “K-SVM: An effective SVM algorithm based on K-means clustering.” J. Comput. 8 (10): 2632–2639. https://doi.org/10.4304/jcp.8.10.2632-2639.
Zhang, W., X. Gu, L. Tang, Y. Yin, D. Liu, and Y. Zhang. 2022. “Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge.” Gondwana Res. 109 (Apr): 1–17. https://doi.org/10.1016/j.gr.2022.03.015.
Zhang, W., H. Li, C. Wu, Y. Li, Z. Liu, and H. Liu. 2021. “Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling.” Underground Space 6 (4): 353–363. https://doi.org/10.1016/j.undsp.2019.12.003.
Zhou, C., T. Kong, Y. Zhou, H. Zhang, and L. Ding. 2019. “Unsupervised spectral clustering for shield tunneling machine monitoring data with complex network theory.” Autom. Constr. 107 (Nov): 102924. https://doi.org/10.1016/j.autcon.2019.102924.
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© 2023 American Society of Civil Engineers.
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Received: Sep 23, 2022
Accepted: Dec 29, 2022
Published online: Mar 2, 2023
Published in print: Jun 1, 2023
Discussion open until: Aug 2, 2023
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