Abstract
In today’s urban development, Earth Pressure Balance (EPB) Tunnel Boring Machines (TBMs) play a vital role. It’s crucial to design a comprehensive monitoring system to control surface settlement and prevent damage to surface structures. This study focuses on creating prediction models for estimating ground surface settlement. Two soft computing techniques, namely ANN-CFB and ANN-BP, were used for this purpose. The models were validated using operational data from the Qom metro Line A, specifically the section between A14 and A10 stations. Additional input parameters were incorporated using an image processing approach to include soil properties for each segment. As a result, the most accurate ANN technique was employed to predict ground surface settlements for the mentioned project. The correlation coefficients for training, testing, validation, and the overall result were found to be 0.99439, 0.97873, 0.96381, and 0.98824, respectively. Through sensitivity analysis, the study explored the connections between different parameters and ground surface settlement. The outcomes reveal strong agreement between predicted values and real data. Notably, the parameter ‘cutter head torque’ exhibited the highest impact on surface settlement (8.48%), while ‘Pressiometric Modulus (Ep)’ had the least impact (4.24%).
Access this article
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Instant access to the full article PDF.
References
-
Ahangari K, Moeinossadat S, Behnia D (2015) Estimation of tunnelling-induced settlement by modern intelligent methods. Soils and Foundations 55:737–748,, DOI: https://doi.org/10.1016/j.sandf.2015.06.006
-
Ahmed M, Mahrous A, Gaofeng R, Jong-Gwan K, Mohamed A (2022) Application of cascade forward backpropagation neural networks for selecting mining methods. Sustainability 14:635, DOI: https://doi.org/10.3390/su14020635
-
Andon A, Covatario G (2021) A study on image processing using artificial neural networks in civil engineering. Bulletin of the Polytechnic Institute of Iaşi. Construction. Architecture Section 67(71), DOI: https://doi.org/10.2478/bipca-2021-0027
-
Attewell P, Hurrell MR (1985) Settlement development caused by tunnelling in soil. Ground engineering 18:17–20
-
Bobet A (2001) Analytical solutions for shallow tunnels in saturated ground. Journal of Engineering Mechanics 127:1258–1266
-
Bouayad D, Emeriault F (2017) Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method. Tunnelling and Underground Space Technology 68:142–152, DOI: https://doi.org/10.1016/j.tust.2017.03.011
-
Celestino T, Gomes R, Bortolucci A (2000) Errors in ground distortions due to settlement trough adjustment. Tunnelling and Underground Space Technology 15:97–100
-
Chen R, Zhang P, Kang X, Zhong Z, Liu Y, Wu H (2018) Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods. Soils and Foundations 59:284–295, DOI: https://doi.org/10.1016/j.sandf.2018.11.005
-
Chen R, Zhang P, Wu H, Wang Z, Zhong Z (2019) Prediction of shield tunneling-induced ground settlement using machine learning techniques. Frontiers of Structural and Civil Engineering 13:1363–1378, DOI: https://doi.org/10.1007/s11709-019-0561-3
-
Chermant J (2001) Why automatic image analysis? An introduction to this issue. Cement and Concrete Composites 23:127–13
-
Cui M, Hong B, Fang Q (2011) Application of digital image processing technology in geotechnical engineering. International Conference on Transportation, Mechanical, and Electrical Engineering
-
De Jesus O, Hagan MT (2007) Backpropagation algorithms for a broad class of dynamic networks. IEEE Transactions on Neural Networks 18:14–27, DOI: https://doi.org/10.1109/TNN.2006.882371
-
Elbisy M, Hatem M, Abd-Elall M, Turki M (2014) The use of feed-forward back propagation and cascade correlation for the neural network prediction of surface water quality parameters. Water Resources 41:709–718, DOI: https://doi.org/10.1134/S0097807814060153
-
Filik U, Mehmet K (2007) A new approach for the short-term load forecasting with autoregressive and Artificial Neural Network Models. International Journal of Computational Intelligence Research 3:66–71, DOI: https://doi.org/10.5019/j.ijcir.2007.88
-
Howard D, Beale M (1992) Neural network toolbox user’s guide, The Math Works Inc.: Portola Valley CA, USA, 103
-
Hussaine S, Linlong M (2022) Intelligent prediction of maximum ground settlement induced by EPB shield tunneling using automated machine learning techniques. Mathematics 10:4637, DOI: https://doi.org/10.3390/math10244637
-
Jani D, Mishra M, Sahoo P (2017) Application of artificial neural network for predicting performance of solid desiccant cooling systems. Renewable and Sustainable Energy Reviews 80:352–366, DOI: https://doi.org/10.1016/j.rser.2017.05.169
-
Jinkui Li, Yan B, Shi Y (2013) The monitoring and analysis of surface subsidence of soft soil rock large section of subway tunnel shield construction. Advanced Materials Research 848:78–82, DOI: https://doi.org/10.4028/www.scientific.net/AMR.848.78
-
Kim CY, Bae G, Hong S, Park C, Moon H, Shin H (2001) Neural Network-Based prediction of ground surface settlements due to tunneling. Computers and Geotechnics 28:517–547
-
Koopialipoor M, Fahimifar A, Ghaleini E, Momenzadeh M, Armaghani D (2020) Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Engineering with Computers 36:345–357, DOI: https://doi.org/10.1007/s00366-019-00701-8
-
Libin T, SeonHong N (2021) Comparison of machine learning methods for ground settlement prediction with different tunneling datasets. Journal of Rock Mechanics and Geotechnical Engineering 13:1274–1289, DOI: https://doi.org/10.1016/j.jrmge.2021.08.006
-
López O, López A, Crossa J (2022) Multivariate statistical machine learning methods for genomic prediction. Springer, Switzerland
-
MMahmoodzadeh A, Mohammadi M, Daraei A, Farid A, Al-Salihi N, Dler Omer R (2020) Forecasting maximum surface settlement caused by urban tunneling. Automation in Construction 120:0926–5805, DOI: https://doi.org/10.1016/j.autcon.2020.103375
-
Meng F, Chen P, Kang X (2018) Effects of tunneling induced soil disturbance on the post-construction settlement in structured soft soil. Tunnelling and Underground Space Technology 80:53–63, DOI: https://doi.org/10.1016/j.tust.2018.06.007
-
Milne L (1995) Feature selection using neural networks with contribution measures. The Australian Conference on Artificial Intelligence, 1–8, DOI: https://doi.org/10.26190/unsworks/378
-
Moeinossadat S, Ahangari K (2018) Estimating maximum surface settlement due to EPBM tunneling by Numerical Intelligent approach: A case study Tehran subway line 7, Tehran, Iran. Transportation Geotechnics 18:92–102, DOI: https://doi.org/10.1016/j.trgeo.2018.11.009
-
Moghaddasi R, Noorian M (2018) ICA-ANN, ANN and multiple regression models for prediction of surface settlement caused by tunneling. Tunnelling and Underground Space Technology 79:197–209, DOI: https://doi.org/10.1016/j.tust.2018.04.016
-
Moghtader T, Sharafati A, Naderpour H, Nik MG (2023) Estimating maximum surface settlement caused by EPB shield tunneling utilizing an intelligent approach. Buildings 13:1051, DOI: https://doi.org/10.3390/buildings13041051
-
Nami F, Deyhimi F (2011) Prediction of activity coefficients at infinite dilution for organic solutes in ionic liquids by artificial neural network. The Journal of Chemical Thermodynamics 43:22–27, DOI: https://doi.org/10.1016/j.jct.2010.07.011
-
Pabodha K, Zhou W, Ding Z, Hong Z (2022) Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method. Journal of Rock Mechanics and Geotechnical Engineering 14:1052–1063, DOI: https://doi.org/10.1016/j.jrmge.2022.01.002
-
Pandelea A (2015) Image processing using artificial neural networks. gheorghe asachi technical university of iasiTomul, Fasc 4
-
Pourtaghi A, Lotfollahi-Yaghin M (2012) Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling. Tunnelling and Underground Space Technology 28:257–271, DOI: https://doi.org/10.1016/j.tust.2011.11.008
-
Samadi H, Hassanpour J, Farrokh E (2021) Maximum surface settlement prediction in EPB TBM tunneling using soft computing techniques. Journal of Physics: Conference Series 1973:012195, DOI: https://doi.org/10.1088/1742-6596/1973/1/012195
-
Santos O, Celestino T (2008) Artificial neural networks analysis of São Paulo subway tunnel settlement data. Tunnelling and Underground Space Technology 23:481–491, DOI: https://doi.org/10.1016/j.tust.2007.07.002
-
Suwansawat S, Einstein HH (2006) Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunnelling and Underground Space Technology 21:133–150, DOI: https://doi.org/10.1016/j.tust.2005.06.007
-
Verruijt A, Booker JR (1996) Surface settlement due to deformation of a tunnel in an elastic half plane. Geotechnique 46:753–756, DOI: https://doi.org/10.1680/geot.1998.48.5.709
-
Yavuz M, Iphar M, Once G (2008) The optimum support design selection by using AHP method for the main haulage road in WLC Tuncbilek colliery. Tunnelling and Underground Space Technology 23:111–119, DOI: https://doi.org/10.1016/j.tust.2007.02.001
-
Zhang L, Wu X, Ji W, Simaan M (2017) Intelligent approach to estimation of tunnel-induced ground settlement using wavelet packet and support vector machines. Journal of Computing in Civil Engineering 31:04016053, DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000621
-
Zhang W, Li H, Wu C, Li Y, Liu Z, Liu HL (2021) Soft computing approach for prediction of surface settlement induced by earth pressure balance shield. Tunnelling and Underground Space Technology 6:353–363, DOI: https://doi.org/10.1016/j.undsp.2019.12.003
Acknowledgments
We would like to express our sincere gratitude to all the individuals that have contributed to the publication of this research paper. We would also like to thank Behro Comprehensive Consulting Engineers for their support throughout the research process. In particular, we would like to thank S. Gravand for their valuable insights and suggestions.
Rights and permissions
About this article
Cite this article
Yazdanparast, M., Koushkgozar, H.A., Hassanpour, J. et al. Predicting Maximum Settlement Induced by EPB Shield Tunneling Through Image Processing and an Intelligent Approach. KSCE J Civ Eng (2024). https://doi.org/10.1007/s12205-024-2086-0
-
Received:
-
Revised:
-
Accepted:
-
Published:
-
DOI: https://doi.org/10.1007/s12205-024-2086-0