Abstract
In order to ensure construction efficiency and stability during tunnel excavation, it is essential to predict geological risks ahead of tunnel faces. In this study, a geological risk prediction model was developed based on a machine learning (ML) algorithm. The database used to implement the ML model was synthetically acquired from a series of finite-element (FE) numerical analyses, which could simulate electrical resistivity surveys during tunnel excavation. The developed FE model helped obtain resistivity data representing various risky ground conditions (such as typical fault zones, water intrusion, mixed ground, geological transitions, and cavities) encountered during tunnel advancement. Four ML algorithms (support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting) were used to develop the prediction model. The evaluation results showed that the proposed ML prediction models produced highly accurate results. Among the ML algorithms, the prediction model based on the random forest (RF) algorithm exhibited superior performance, with an accuracy of 97.33%. Given the feasibility and efficiency of recognizing hazardous ground conditions, the proposed model is expected to serve as a reliable approach for risk management. Finally, an engineering flowchart was proposed to assist in the application of the study results to actual tunneling sites.
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References
Abidin MZ, Wijeyesekera D, Saad R, Ahmad F (2013) The influence of soil moisture content and grain size characteristics on its field electrical resistivity. Electronic Journal of Geotechnical Engineering 18:699–705, DOI: https://doi.org/10.1088/1742-6596/495/1/012014
Alsharari B, Olenko A, Abuel-Naga H (2020) Modeling of electrical resistivity of soil based on geotechnical properties. Expert Systems with Applications 141:112966, DOI: https://doi.org/10.1016/j.eswa.2019.112966
Arumugam K, Naved M, Shinde P, Leiva-Chauca O, Huaman-Osorio A, Gonzales-Yanac T (2023) Multiple disease prediction using Machine learning algorithms. Materials Today: Proceedings 80:3682–3685, DOI: https://doi.org/10.1016/j.matpr.2021.07.361
Bai XD, Cheng WC, Ong DE, Li G (2021) Evaluation of geological conditions and clogging of tunneling using machine learning. Geomechanics and Engineering 25(1):59–73, DOI: https://doi.org/10.12989/gae.2021.25.1.059
Banton O, Cimon M, Seguin M (1997) Mapping field-scale physical properties of soil with electrical resistivity. Soil Science Society of America Journal 61(4):1010–1017, DOI: https://doi.org/10.2136/sssaj1997.03615995006100040003x
Bayati M, Hamidi JK (2017) A case study on TBM tunnelling in fault zones and lessons learned from ground improvement. Tunnelling and Underground Space Technology 63:162–170, DOI: https://doi.org/10.1016/j.tust.2016.12.006
Broere W (2016) Urban underground space: Solving the problems of today’s cities. Tunnelling and Underground Space Technology 55: 245–248, DOI: https://doi.org/10.1016/j.tust.2015.11.012
Bryson LS (2005) Evaluation of geotechnical parameters using electrical resistivity measurements. In Earthquake Engineering and Soil Dynamics, 1–12, DOI: https://doi.org/10.1061/40779(158)10
Carrière SD, Chalikakis K, Sénéchal G, Danquigny C, Emblanch C (2013) Combining electrical resistivity tomography and ground penetrating radar to study geological structuring of karst unsaturated zone. Journal of Applied Geophysics 94:31–41, DOI: https://doi.org/10.1016/j.jappgeo.2013.03.014
Chung H, Park J, Kim BK, Kwon K, Lee IM, Choi H (2021) A causal network-based risk matrix model applicable to shield TBM tunneling projects. Sustainability 13(9):4846, DOI: https://doi.org/10.3390/su13094846
Dickmann T, Sander BK (1996) Drivage concurrent tunnel seismic prediction (TSP). Felsbau 14(6):406–411
Eftekhari A, Aalianvari A, Rostami J (2018) Influence of TBM operational parameters on optimized penetration rate in schistose rocks, a case study: Golab tunnel Lot-1, Iran. Computers and Concrete 22(2):239–248, DOI: https://doi.org/10.12989/CAC.2018.22.2.239
Farrokh E, Rostami J (2009) Effect of adverse geological condition on TBM operation in Ghomroud tunnel conveyance project. Tunnelling and Underground Space Technology 24(4):436–446, DOI: https://doi.org/10.1016/j.tust.2008.12.006
Gong Q, Yin L, Ma H, Zhao J (2016) TBM tunnelling under adverse geological conditions: An overview. Tunnelling and Underground Space Technology 57:4–17, DOI: https://doi.org/10.1016/j.tust.2016.04.002
Grodner M (2001) Delineation of rockburst fractures with ground penetrating radar in the Witwatersrand Basin, South Africa. International Journal of Rock Mechanics and Mining Sciences 38(6):885–891, DOI: https://doi.org/10.1016/S1365-1609(01)00054-5
Hasanpour R, Rostami J, Schmitt J, Ozcelik Y, Sohrabian B (2020) Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks. Journal of Rock Mechanics and Geotechnical Engineering 12(1):21–31, DOI: https://doi.org/10.1016/j.jrmge.2019.04.006
Hazreek Z, Aziman M, Azhar A, Chitral W, Fauziah A, Rosli S (2015) The behaviour of laboratory soil electrical resistivity value under basic soil properties influences. IOP Conference Series: Earth and Environmental Science 23(1):012002. IOP Publishing, DOI: https://doi.org/10.1088/1755-1315/23/1/012002
Hou S, Liu Y, Zhuang W, Zhang K, Zhang R, Yang Q (2023) Prediction of shield jamming risk for double-shield TBM tunnels based on numerical samples and random forest classifier. Acta Geotechnica 18(1):495–517, DOI: https://doi.org/10.1007/s11440-022-01567-9
Hyun KC, Min S, Choi H, Park J, Lee IM (2015) Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels. Tunnelling and Underground Space Technology 49:121–129, DOI: https://doi.org/10.1016/j.tust.2015.04.007
Jung JH, Chung H, Kwon Y, Lee IM (2019) An ANN to predict ground condition ahead of tunnel face using TBM operational data. KSCE Journal of Civil Engineering 23(7):3200–3206, DOI: https://doi.org/10.1007/s12205-019-1460-9
Kafy A, Bakshi A, Saha M, Al Faisal A, Almulhim AI, Rahaman ZA, Mohammad P (2023) Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms. Science of The Total Environment 867:161394, DOI: https://doi.org/10.1016/j.scitotenv.2023.161394
Kang M, Kim S, Lee J, Choi H (2022) FE model of electrical resistivity survey for mixed ground prediction ahead of a TBM tunnel face. Geomechanics and Engineering 29(3):301–310, DOI: https://doi.org/10.12989/gae.2022.29.3.301
Kang M, Lee J, Kwon K, Park S, Choi H (2023) Laboratory simulations on hybrid non-destructive survey of electrical resistivity and induced polarization to predict geological risks ahead of a TBM tunnel. Tunnelling and Underground Space Technology 135:105066, DOI: https://doi.org/10.1016/j.tust.2023.105066
Kaus A, Boening W (2008) BEAM–Geoelectrical ahead monitoring for TBM-Drives. Geomechanik und Tunnelbau: Geomechanik Und Tunnelbau 1(5):442–449, DOI: https://doi.org/10.1002/geot.200800048
Lee KH, Park J, Park J, Lee IM, Lee SW (2019) Electrical resistivity tomography survey for prediction of anomaly in mechanized tunneling. Geomechanics and Engineering 19(1):93–104, DOI: https://doi.org/10.12989/gae.2019.19.1.093
Lee HL, Song K, Qi C, Kim KY (2022) Sequential prediction of TBM penetration rate using a gradient boosted regression tree during tunneling. Geomechanics and Engineering 29(5):523–533, DOI: https://doi.org/10.12989/GAE.2022.29.5.523
Lee S, Moon JS (2020) Excessive groundwater inflow during TBM tunneling in limestone formation. Tunnelling and Underground Space Technology 96:103217, DOI: https://doi.org/10.1016/j.tust.2019.103217
Li JB, Chen ZY, Li X, Jing LJ, Zhang YP, Xiao HH, Wang SJ, Yang WK, Wu LJ, Li PY, Li HB, Yao M, Fan LT (2023) Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods. Underground Space 11:1–25, DOI: https://doi.org/10.1016/j.undsp.2023.01.001
Liu B, Guo Q, Liu Z, Wang C, Nie L, Xu X, Chen L (2019) Comprehensive ahead prospecting for hard rock TBM tunneling in complex limestone geology: A case study in Jilin, China. Tunnelling and Underground Space Technology 93:103045, DOI: https://doi.org/10.1016/j.tust.2019.103045
Liu Q, Wang X, Huang X, Yin X (2020) Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data, Tunnelling and Underground Space Technology 106:103595, DOI: https://doi.org/10.1016/j.tust.2020.103595
Mahmoodzadeh A, Nejati H, Ibrahim H, Ali H, Mohammed A, Rashidi S, Majeed M (2022a) Several models for tunnel boring machine performance prediction based on machine learning, Geomechanics and Engineering 30(1):75–91, DOI: https://doi.org/10.12989/GAE.2022.30.1.075
Mahmoodzadeh A, Nejati H, Mohammadi M, Mohammed A, Ibrahim H, Rashidi S (2022b) Numerical and Machine learning modeling of hard rock failure induced by structural planes around deep tunnels. Engineering Fracture Mechanics 271:108648, DOI: https://doi.org/10.1016/j.engfracmech.2022.108648
McDowell PW, Barker RD, Butcher AP, Culshaw MG, Jackson PD, McCann DM, Arthur JCR (2002) Geophysics in engineering investigations. Ciria, London, UK, 61–68
Méndez M, Merayo MG, Núñez M (2023) Machine learning algorithms to forecast air quality: A survey. Artificial Intelligence Review, 1–36, DOI: https://doi.org/10.1007/s10462-023-10424-4
Mifkovic M, Swidinsky A, Mooney M (2021) Imaging ahead of a tunnel boring machine with DC resistivity: A laboratory and numerical study. Tunnelling and Underground Space Technology 108:103703, DOI: https://doi.org/10.1016/j.tust.2020.103703
Pallathadka H, Wenda A, Ramirez-Asís E, Asís-López M, Flores-Albornoz J, Phasinam K (2023) Classification and prediction of student performance data using various machine learning algorithms. Materials Today: Proceedings 80:3782–3785, DOI: https://doi.org/10.1016/j.matpr.2021.07.382
Pandey L, Shukla S, Habibi D (2015) Electrical resistivity of sandy soil. Géotechnique Letters 5(3):178–185, DOI: https://doi.org/10.1680/jgele.15.00066
Park J, Lee KH, Kim BK, Choi H, Lee IM (2017) Predicting anomalous zone ahead of tunnel face utilizing electrical resistivity: II. Field tests. Tunnelling and Underground Space Technology 68:1–10, DOI: https://doi.org/10.1016/j.tust.2017.05.017
Park J, Lee KH, Park J, Choi H, Lee IM (2016) Predicting anomalous zone ahead of tunnel face utilizing electrical resistivity: I. Algorithm and measuring system development. Tunnelling and Underground Space Technology 60:141–150, DOI: https://doi.org/10.1016/j.tust.2016.08.007
Park J, Ryu J, Choi H, Lee IM (2018) Risky ground prediction ahead of mechanized tunnel face using electrical methods: Laboratory tests. KSCE Journal of Civil Engineering 22(9):3663–3675, DOI: https://doi.org/10.1007/s12205-018-1357-z
Rafie M, Namin FS (2015) Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system. International Journal of Mining Science and Technology 25(4):655–663, DOI: https://doi.org/10.1016/j.ijmst.2015.05.021
Reynolds JM (2011) sAn introduction to applied and environmental geophysics–2nd edition. John Wiley & Sons, Inc., Hoboken, NJ, USA, 61–68
Santamarina JC, Klein KA, Fam MA (2001) Soils and waves: Particulate materials behavior, characterization and process monitoring. Journal of Soils and Sediments 1(2):130–130, DOI: https://doi.org/10.1007/BF02986486
Schaeffer K, Mooney MA (2016) Examining the influence of TBM-ground interaction on electrical resistivity imaging ahead of the TBM. Tunnelling and Underground Space Technology 58:82–98, DOI: https://doi.org/10.1016/j.tust.2016.04.003
Sebbeh-Newton S, Ayawah P, Azure W, Kaba A, Ahmad F, Zainol Z, Zabidi H (2021) Towards TBM automation: On-the-fly characterization and classification of ground conditions ahead of a TBM using data-driven approach, Applied Sciences 11(3):1060, DOI: https://doi.org/10.3390/app11031060
Shang Y, Xue J, Wang S, Yang Z, Yang J (2004) A case history of Tunnel Boring Machine jamming in an inter-layer shear zone at the yellow river diversion project in China. Engineering Geology 71(3–4):199–211, DOI: https://doi.org/10.1016/S0013-7952(03)00134-0
Sharafat A, Latif K, Seo J (2021) Risk analysis of TBM tunneling projects based on generic bow-tie risk analysis approach in difficult ground conditions. Tunnelling and Underground Space Technology 111: 103860, DOI: https://doi.org/10.1016/j.tust.2021.103860
Shi M, Sun W, Zhang T, Liu Y, Wang S, Song X (2019) Geology prediction based on operation data of TBM: Comparison between deep neural network and soft computing methods. In 2019 1st International Conference on Industrial Artificial Intelligence (IAI), IEEE, DOI: https://doi.org/10.1109/ICIAI.2019.8850794
Telford WM, Geldart LP, Sheriff RE (1990) Applied geophysics–2nd edition. Cambridge University Press, Cambridge, UK, 103–107
Tóth Á, Gong Q, Zhao J (2013) Case studies of TBM tunneling performance in rock–soil interface mixed ground. Tunnelling and Underground Space Technology 38:140–150, DOI: https://doi.org/10.1016/j.tust.2013.06.001
Xu ZH, Yu TF, Lin P, Wang WY, Shao RQ (2022) Integrated geochemical, mineralogical, and microstructural identification of faults in tunnels and its application to TBM jamming analysis. Tunnelling and Underground Space Technology 128:104650, DOI: https://doi.org/10.1016/j.tust.2022.104650
Yazdani-Chamzini A (2014) Proposing a new methodology based on fuzzy logic for tunnelling risk assessment. Journal of Civil Engineering and Management 20(1):82–94, DOI: https://doi.org/10.3846/13923730.2013.843583
Zhao J, Gong QM, Eisensten Z (2007) Tunnelling through a frequently changing and mixed ground: A case history in Singapore. Tunnelling and Underground Space Technology 22(4):388–400, DOI: https://doi.org/10.1016/j.tust.2006.10.002
Zhao K, Janutolo M, Barla G, Chen G (2014) 3D simulation of TBM excavation in brittle rock associated with fault zones: The brenner exploratory tunnel case. Engineering Geology 181:93–111, DOI: https://doi.org/10.1016/j.enggeo.2014.07.002
Acknowledgments
This research was supported by the National R&D Project for Smart Construction Technology (No. RS-2020-KA157074) funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure, and Transport, and managed by the Korea Expressway Corporation.
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Kang, M., Pham, K., Kwon, K. et al. A Hybrid Numerical-ML Model for Predicting Geological Risks in Tunneling with Electrical Methods. KSCE J Civ Eng (2024). https://doi.org/10.1007/s12205-024-0066-z
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DOI: https://doi.org/10.1007/s12205-024-0066-z