Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov model
Limao ZHANG, Ying WANG, Xianlei FU, Xieqing SONG, Penghui LIN
Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov model
The accurate estimation of geological risks is essential for preventing geohazards, and ensuring efficient and safe construction processes. This study proposes a method, the online hidden Markov model (OHMM), which combines online learning with the hidden Markov model to estimate geological risks. The OHMM is tailored for the continuous nature of observational data, allowing it to adaptively update with each new piece of data. To address the challenge of limited data in the early stages of construction, we use pre-construction borehole samples as additional data. This approach extends the short sequence of observed data to match the length of a complete sequence through an observation extension mechanism. The effectiveness of the OHMM, equipped with this observation extension mechanism, is demonstrated in a case study that models geological risks for a tunnel excavation project in Singapore. The OHMM outperforms traditional methods, including the hidden Markov model, long short-term memory network, neural network, and support vector machine, in predicting geological risks ahead of the tunnel boring machine. Notably, the OHMM can accurately forecast geological risks in areas yet to be constructed, using limited observational and site investigation data. This research advances geological risk prediction models by offering an online updating capability for tunnel excavation and construction projects. It enables early-stage risk prediction and provides long-term forecasts with minimal historical data requirements, maximizing the use of site investigation data.
geological risk prediction / machine learning / online learning / hidden Markov model / borehole logging
[1] |
Arieli O, Denecker M, Bruynooghe M, (2007). Distance semantics for database repair. Annals of Mathematics and Artificial Intelligence, 50( 3): 389–415
CrossRef
Google scholar
|
[2] |
Bietti A, Bach F, Cont A, (2015). An online em algorithm in hidden (semi-)Markov models for audio segmentation and clustering. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1881–1885
|
[3] |
Blum A, (1998). On-line algorithms in machine learning. Online Algorithms: The State of the Art, 306–325
|
[4] |
CaoB TSaadallah AEgorovAFreitagSMeschkeG MorikK (2021, 2021). Online geological anomaly detection using machine learning in mechanized tunneling. Paper presented at the Challenges and Innovations in Geomechanics, Cham
|
[5] |
Chadza T, Kyriakopoulos K G, Lambotharan S, (2020). Analysis of hidden Markov model learning algorithms for the detection and prediction of multi-stage network attacks. Future Generation Computer Systems, 108: 636–649
CrossRef
Google scholar
|
[6] |
Chis T, Harrison P G, (2015). Adapting hidden Markov models for online learning. Electronic Notes in Theoretical Computer Science, 318: 109–127
CrossRef
Google scholar
|
[7] |
Cho S H, Kim J, Won J, Kim M K, (2017). Effects of jack force and construction steps on the change of lining stresses in a TBM tunnel. KSCE Journal of Civil Engineering, 21( 4): 1135–1146
CrossRef
Google scholar
|
[8] |
Dakir I, Benamara A, Aassoumi H, Ouallali A, Ait Bahammou Y, (2019). Application of induced polarization and resistivity to the determination of the location of metalliferous veins in the Taroucht and Tabesbaste areas. International Journal of Geophysics, 2019: 5849019
CrossRef
Google scholar
|
[9] |
Einstein H H, (2004). Decision aids for tunneling. Update., 1892( 1): 199–207
|
[10] |
EreminaOKozliakova IAnisimovaNKozhevnikovaI (2018). Assessment of exogenous geological hazards in Moscow, Russia. 55(1): 133–140
|
[11] |
Erharter G H, Marcher T, (2020). MSAC: Towards data driven system behavior classification for TBM tunneling. Tunnelling and Underground Space Technology, 103: 103466
CrossRef
Google scholar
|
[12] |
Erharter G H, Marcher T, Reinhold C, (2020). Artificial neural network based online rockmass behavior classification of TBM data. Information Technology in Geo-Engineering, 178–188
|
[13] |
Fu X, Zhang L, (2021). Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: A deep learning approach. Automation in Construction, 132: 103937
CrossRef
Google scholar
|
[14] |
Gandhi S M, Sarkar B C, (2016). Chapter 5 - Geophysical exploration. In: Essentials of Mineral Exploration and Evaluation, 97–123
|
[15] |
Golpasand M B, Do N A, Dias D, Nikudel M R, (2018). Effect of the lateral earth pressure coefficient on settlements during mechanized tunneling. Geomechanics and Engineering, 16( 6): 643–654
|
[16] |
Hoi S C H, Sahoo D, Lu J, Zhao P, (2021). Online learning: A comprehensive survey. Neurocomputing, 459: 249–289
CrossRef
Google scholar
|
[17] |
Huang X, Li J, Liang Y, Wang Z, Guo J, Jiao P, (2017). Spatial hidden Markov chain models for estimation of petroleum reservoir categorical variables. Journal of Petroleum Exploration and Production Technology, 7( 1): 11–22
CrossRef
Google scholar
|
[18] |
Langfield-Smith K, Wirth A, (1992). Measuring differences between cognitive maps. Journal of the Operational Research Society, 43( 12): 1135–1150
CrossRef
Google scholar
|
[19] |
Li K, Qiu C, Zhou X, Chen M, Lin Y, Jia X, Li B, (2022). Modeling and tagging of time sequence signals in the milling process based on an improved hidden semi-Markov model. Expert Systems with Applications, 205: 117758
CrossRef
Google scholar
|
[20] |
Li S, Liu B, Xu X, Nie L, Liu Z, Song J, Sun H, Chen L, Fan K, (2017). An overview of ahead geological prospecting in tunneling. Tunnelling and Underground Space Technology, 63: 69–94
CrossRef
Google scholar
|
[21] |
Lin B, Zhou L, Lv G, Zhu A X, (2017). 3D geological modelling based on 2D geological map. Annals of GIS, 23( 2): 117–129
CrossRef
Google scholar
|
[22] |
Liu Z, Li L, Fang X, Qi W, Shen J, Zhou H, Zhang Y, (2021). Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network. Automation in Construction, 125: 103647
CrossRef
Google scholar
|
[23] |
Lu C, Liu J, Liu Y, Liu Y, (2019). Intelligent construction technology of railway engineering in China. Frontiers of Engineering Management, 6( 4): 503–516
CrossRef
Google scholar
|
[24] |
Mahmoodzadeh A, Mohammadi M, Daraei A, Farid Hama Ali H, Ismail Abdullah A, Kameran Al-Salihi N, (2021). Forecasting tunnel geology, construction time and costs using machine learning methods. Neural Computing & Applications, 33( 1): 321–348
CrossRef
Google scholar
|
[25] |
Mishra S, Rao K S, Gupta N K, Kumar A, (2017). Damage to shallow tunnels under static and dynamic loading. Procedia Engineering, 173: 1322–1329
CrossRef
Google scholar
|
[26] |
Mongillo G, Deneve S, (2008). Online learning with hidden Markov models. Neural Computation, 20( 7): 1706–1716
CrossRef
Google scholar
|
[27] |
Peng S, Li Q, (2021). Research on 3D geological modeling method of tunnel engineering computer based on geological cross-section. Journal of Physics: Conference Series, 1992( 2): 022112
CrossRef
Google scholar
|
[28] |
ReddiL NJain A KYunH B (2012). 6 - Soil materials for earth construction: properties, classification and suitability testing. In Hall M, Lindsay R, Krayenhoff M. eds. Modern Earth Buildings, Woodhead Publishing Series in Energy: 155–171
|
[29] |
RekatsinasTChu XIlyasI FRéC (2017). HoloClean: Holistic data repairs with probabilistic inference. arXiv:1702.00820
|
[30] |
Savenok O V, Povarova L V, Kusov G V, (2020). Application of superdeep drilling technology for study of the earth crust. IOP Conference Series. Earth and Environmental Science, 459( 5): 052066
CrossRef
Google scholar
|
[31] |
Sheil B B, Suryasentana S K, Mooney M A, Zhu H, (2020). Machine learning to inform tunnelling operations: Recent advances and future trends. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 173( 4): 74–95
CrossRef
Google scholar
|
[32] |
SickingJPintz MAkilaMWirtzT (2020). DenseHMM: Learning hidden Markov models by learning dense representations. arXiv:2012.09783
|
[33] |
Stoner O, Economou T, (2020). An advanced hidden Markov model for hourly rainfall time series. Computational Statistics & Data Analysis, 152: 107045
CrossRef
Google scholar
|
[34] |
Sundell J, Haaf E, Norberg T, Alén C, Karlsson M, Rosén L, (2019). Risk mapping of groundwater-drawdown-induced land subsidence in heterogeneous soils on large areas. Risk Analysis, 39( 1): 105–124
CrossRef
Google scholar
|
[35] |
Thalund-Hansen R, Troldborg M, Levy L, Christiansen A V, Bording T S, Bjerg P L, (2023). Assessing contaminant mass discharge uncertainty with application of hydraulic conductivities derived from geoelectrical cross-borehole induced polarization and other methods. Water Resources Research, 59: e2022WR034360
|
[36] |
Vereecken H, Amelung W, Bauke S L, Bogena H, Brüggemann N, Montzka C, Vanderborght J, Bechtold M, Blöschl G, Carminati A, Javaux M, Konings A G, Kusche J, Neuweiler I, Or D, Steele-Dunne S, Verhoef A, Young M, Zhang Y, (2022). Soil hydrology in the Earth system. Nature Reviews. Earth & Environment, 3( 9): 573–587
CrossRef
Google scholar
|
[37] |
Wan F, Guo H, Li J, Gu M, Pan W, Ying Y, (2021). A scheduling and planning method for geological disasters. Applied Soft Computing, 111: 107712
CrossRef
Google scholar
|
[38] |
Wang B, Sun D, Chen Q, Lin W, Li A W, Cao H, (2020a). Stress-state differences between sedimentary cover and basement of the Songliao Basin, NE China: In-situ stress measurements at 6–7 km depth of an ICDP Scientific Drilling borehole (SK-II). Tectonophysics, 777: 228337
CrossRef
Google scholar
|
[39] |
Wang X, Lai J, Qiu J, Xu W, Wang L, Luo Y, (2020b). Geohazards, reflection and challenges in mountain tunnel construction of China: A data collection from 2002 to 2018. Geomatics, Natural Hazards & Risk, 11( 1): 766–785
CrossRef
Google scholar
|
[40] |
Wang Z, Zhang B, (2023). Key technical innovations in the construction of Baihetan Hydropower Station Project. Frontiers of Engineering Management, 10( 2): 367–372
CrossRef
Google scholar
|
[41] |
Wolfsberg A, (1997). Rock Fractures and fluid flow. Contemporary Understanding and Applications, 78( 49): 569–573
|
[42] |
Xiang Y, Zeng Z, Xiang Y, Abi E, Zheng Y, Yuan H, (2021). Tunnel failure mechanism during loading and unloading processes through physical model testing and DEM simulation. Scientific Reports, 11( 1): 16753
CrossRef
Google scholar
|
[43] |
Xiong Z, Guo J, Xia Y, Lu H, Wang M, Shi S, (2018). A 3D multi-scale geology modeling method for tunnel engineering risk assessment. Tunnelling and Underground Space Technology, 73: 71–81
CrossRef
Google scholar
|
[44] |
Xu Z, Liu F, Lin P, Shao R, Shi X, (2021a). Non-destructive, in-situ, fast identification of adverse geology in tunnels based on anomalies analysis of element content. Tunnelling and Underground Space Technology, 118: 104146
CrossRef
Google scholar
|
[45] |
Xu Z H, Wang W Y, Lin P, Nie L C, Wu J, Li Z M, (2021b). Hard-rock TBM jamming subject to adverse geological conditions: Influencing factor, hazard mode and a case study of Gaoligongshan Tunnel. Tunnelling and Underground Space Technology, 108: 103683
CrossRef
Google scholar
|
[46] |
Yazdani N, Garcia E C, Riad M, (2018). Field assessment of concrete structures rehabilitated with FRP. Eco-Efficient Repair and Rehabilitation of Concrete Infrastructures, 171–194
|
[47] |
ZhangASong SWangJYuP S (2017). Time series data cleaning: from anomaly detection to anomaly repairing. 10(10%J Proc. VLDB Endow.), 1046–1057
|
[48] |
Zhang L, Lin P, (2021). Multi-objective optimization for limiting tunnel-induced damages considering uncertainties. Reliability Engineering & System Safety, 216: 107945
CrossRef
Google scholar
|
[49] |
Zhang P, Lu D, Du X, Qi J, (2021). A division method for shallow tunnels and deep tunnels considering soil stress path dependency. Computers and Geotechnics, 135: 104012
CrossRef
Google scholar
|
[50] |
Zhou Y, Chen X, Wu M, Cao W, (2021). Modeling and coordinated optimization method featuring coupling relationship among subsystems for improving safety and efficiency of drilling process. Applied Soft Computing, 99: 106899
CrossRef
Google scholar
|
/
〈 | 〉 |