References
- Abbaszadeh Shahri, A. (2016), "An optimized artificial neural network structure to predict clay sensitivity in a high landslide prone area using piezocone penetration test (CPTu) data: A case study in Southwest of Sweden", Geotech. Geol. Eng., 34(2), 745-758. https://doi.org/10.1007/s10706-016-9976-y.
- Adadi, A. and Berrada, M. (2018), "Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)", IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052.
- Adeli, H. (2001), "Neural networks in civil engineering: 1989-2000", Comput. Aid. Civil Infrastr. Eng., 16, 126-142. https://doi.org/10.1111/0885-9507.00219.
- Adoko, A.C., Jiao, Y.Y., Wu, L., Wang, H. and Wang, Z.H. (2013), "Predicting tunnel convergence using multivariate adaptive regression spline and artificial neural network", Tunnell. Undergr. Space Technol., 38, 368-376. https://doi.org/10.1016/j.tust.2013.07.023.
- Alam, M.S., Sultana, N. and Hossain, S.M.Z. (2021), "Bayesian optimization algorithm-based support vector regression analysis for estimation of shear capacity of FRP reinforced concrete members", Appl. Soft Comput., 105, 107281. https://doi.org/10.1016/j.asoc.2021.107281.
- Alavi, A. and Gandomi, A. (2011), "A robust data mining approach for formulation of geotechnical engineering systems", Eng. Comput., 28, 242-274. https://doi.org/10.1108/02644401111118132.
- Alembagheri, M. and Ghaemian, M. (2013), "Seismic assessment of concrete gravity dams using capacity estimation and damage indexes", Earthq. Eng. Struct. Dyn., 42(1), 123-144. https://doi.org/https://doi.org/10.1002/eqe.2196.
- Alkroosh, I. and Nikraz, H. (2011), "Correlation of pile axial capacity and CPT data using gene expression programming", Geotech. Geol. Eng., 29, 725-748. https://doi.org/10.1007/s10706-011-9413-1.
- Alkroosh, I. and Nikraz, H. (2014), "Predicting pile dynamic capacity via application of an evolutionary algorithm", Soils Found., 54(2), 233-242. https://doi.org/10.1016/j.sandf.2014.02.013.
- Amjad, M., Ahmad, I., Ahmad, M., Wróblewski, P., Kamiński, P. and Amjad, U. (2022), "Prediction of pile bearing capacity using XGBoost algorithm: Modeling and performance evaluation", Appl. Sci., 12(4), 2126. https://doi.org/10.3390/app12042126.
- Anand, V. and Satish Kumar, S.R. (2018), "Seismic soil-structure interaction: A state-of-the-art review", Struct., 16, 317-326. https://doi.org/10.1016/J.ISTRUC.2018.10.009.
- Armaghani, D.J., Asteris, P.G., Fatemi, S.A., Hasanipanah, M., Tarinejad, R., Rashid, A.S.A. and Huynh, V.V. (2020), "On the use of neuro-swarm system to forecast the pile settlement", Appl. Sci., 10(6), 1904. https://doi.org/10.3390/app10061904.
- Balasubramaniam, N., Kauppinen, M., Rannisto, A., Hiekkanen, K. and Kujala, S. (2023), "Transparency and explainability of AI systems: From ethical guidelines to requirements", Inform. Softw. Technol., 159, 107197. https://doi.org/10.1016/j.infsof.2023.107197.
- Bejarbaneh, B.Y., Bejarbaneh, E.Y., Amin, M.F.M., Fahimifar, A., Jahed Armaghani, D. and Majid, M.Z.A. (2018), "Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems", Bull. Eng. Geol. Environ., 77(1), 345-61. https://doi.org/10.1007/s10064-016-0983-2.
- Bhattacharya, B. and Solomatine, D. (2006), "Machine learning in soil classification", Neural Netw., 19, 186-195. https://doi.org/10.1109/IJCNN.2005.1556350.
- Bradley, B.A. (2017, November), "On-going challenges in physics-based ground motion prediction and insights from the 2010-2011 Canterbury and 2016 Kaikoura, New Zealand earthquakes", Soil Dyn. Earthq. Eng., 124, 354-364. https://doi.org/10.1016/j.soildyn.2018.04.042.
- Bui, X.N., Nguyen, H., Le, H.A., Bui, H.B. and Do, N.H. (2020), "Prediction of blast-induced air over-pressure in open-pit mine: Assessment of different artificial intelligence techniques", Nat. Resour. Res., 29(2), 571-591. https://doi.org/10.1007/s11053-019-09461-0.
- Cabalar, A.F., Cevik, A. and Gokceoglu, C. (2012), "Some applications of adaptive neuro-fuzzy Inference system (ANFIS) in geotechnical engineering", Comput. Geotech., 40, 14-33. https://doi.org/10.1016/j.compgeo.2011.09.008.
- Chen, R., Zhang, P., Wu, H., Wang, Z. and Zhong, Z. (2019), "Prediction of shield tunneling-induced ground settlement using machine learning techniques", Front. Struct. Civil Eng., 13(6), 1363-1378. https://doi.org/10.1007/s11709-019-0561-3.
- Cheng, K. and Ziotopoulou, K. (2023), "Machine learning applications in geotechnical earthquake engineering: Progress, gaps, and opportunities", Geo-Congress 2023, Los Angeles, CA, USA, March.
- Cheng, W.C., Ni, J.C., Arulrajah, A. and Huang, H.W. (2018), "A simple approach for characterising tunnel bore conditions based upon pipe-jacking data", Tunnell. Undergr. Sp. Technol., 71, 494-504. https://doi.org/10.1016/j.tust.2017.10.002.
- Ching, J. and Phoon, K.K. (2012), "Corrigendum: Modeling parameters of structured clays as a multivariate normal distribution", Can. Geotech. J., 49, 522-545. https://doi.org/10.1139/t2012-015.
- Ching, J. and Phoon, K.K. (2014a), "Correlations among some clay parameters - The multivariate distribution", Can. Geotech. J., 51(6), 686-704. https://doi.org/10.1139/cgj-2013-0353.
- Ching, J. and Phoon, K.K. (2014b), "Transformations and correlations among some clay parameters - The global database", Can. Geotech. J., 51(6), 663-685. https://doi.org/10.1139/cgj-2013-0262.
- Ching, J., Phoon, K.K. and Chen, C.H. (2014), "Modeling piezocone cone penetration (CPTU) parameters of clays as a multivariate normal distribution", Can. Geotech. J., 51(1), 77-91. https://doi.org/10.1139/cgj-2012-0259.
- Das, S. and Suman, S. (2015), "Prediction of lateral load capacity of pile in clay using multivariate adaptive regression spline and functional network", Arab. J. Sci. Eng., 40(6), 1565-1578. https://doi.org/10.1007/s13369-015-1624-y.
- Di Donna, A. and Laloui, L. (2013), Energy Geostructures: Innovation in Underground Engineering, John Wiley & Sons Inc., Hoboken, NJ, USA.
- Dignum, V. (2019), Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way, Springer International Publishing, Cham, Switzerland.
- Dunphy, K., Fekri, M.N., Grolinger, K. and Sadhu, A. (2022), "Data augmentation for deep-learning-based multiclass structural damage detection using limited information", Sens., 22(16), 6193. https://doi.org/10.3390/s22166193.
- Elbaz, K., Shen, S.L., Zhou, A., Yuan, D.J. and Xu, Y. (2019), "Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm", Appl. Sci., 9, 780. https://doi.org/10.3390/app9040780.
- Elbisy, M.S. (2015), "Support vector machine and regression analysis to predict the field hydraulic conductivity of sandy soil", KSCE J. Civil Eng., 19(7), 2307-2316. https://doi.org/10.1007/s12205-015-0210-x.
- Gandomi, A.H. and Alavi, A.H. (2012), "A new multi-gene genetic programming approach to nonlinear system modeling. Part I: Materials and structural engineering problems", Neural Comput. Appl., 21(1), 171-187. https://doi.org/10.1007/s00521-011-0734-z.
- Ghorbani, A., Hasanzadehshooiili, H., Ghamari, E. and Medzvieckas, J. (2014), "Comprehensive three-dimensional finite element analysis, parametric study and sensitivity analysis on the seismic performance of soil-micropile-superstructure interaction", Soil Dyn. Earthq. Eng., 58, 21-36. https://doi.org/10.1016/j.soildyn.2013.12.001.
- Ghorbani, B., Sadrossadat, E., Bolouri Bazaz, J. and Rahimzadeh Oskooei, P. (2018), "Numerical ANFIS-based formulation for prediction of the ultimate axial load bearing capacity of piles through CPT data", Geotech. Geol. Eng., 36(4), 2057-2076. https://doi.org/10.1007/s10706-018-0445-7.
- Goh, A., Zhang, Y., Zhang, R., Zhang, W. and Xiao, Y. (2017), "Evaluating stability of underground entry-type excavations using multivariate adaptive regression splines and logistic regression", Tunnell. Undergr. Sp. Technol., 70, 148-154. https://doi.org/10.1016/j.tust.2017.07.013.
- Goh, A.T.C. and Zhang, W.G. (2012), "Reliability assessment of stability of underground rock caverns", Int. J. Rock Mech. Min. Sci., 55, 157-163. https://doi.org/10.1016/j.ijrmms.2012.07.012.
- Goh, A.T.C. and Zhang, W.G. (2014), "An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines", Eng. Geol., 170, 1-10. https://doi.org/10.1016/j.enggeo.2013.12.003.
- Goh, A.T.C., Zhang, W., Zhang, Y., Xiao, Y. and Xiang, Y. (2018), "Determination of earth pressure balance tunnel-related maximum surface settlement: A multivariate adaptive regression splines approach", Bull. Eng. Geol. Environ., 77(2), 489-500. https://doi.org/10.1007/s10064-016-0937-8.
- Gravett, D.Z., Mourlas, C., Taljaard, V.L., Bakas, N., Markou, G. and Papadrakakis, M. (2021), "New fundamental period formulae for soil-reinforced concrete structures interaction using machine learning algorithms and ANNs", Soil Dyn. Earthq. Eng., 144, 106656. https://doi.org/10.1016/j.soildyn.2021.106656.
- Gupta, T., Ramana, G.V. and Elgamal, A. (2023), "A hybrid numerical-probabilistic approach for machine learning-based prediction of liquefaction-induced settlement using CPT data", Arab. J. Geosci., 16(6), 394. https://doi.org/10.1007/s12517-023-11500-3.
- Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M. and Hussain, A. (2024), "Interpreting black-box models: A review on explainable artificial intelligence", Cognit. Comput., 16(1), 45-74. https://doi.org/10.1007/s12559-023-10179-8.
- Hu, H., Jiang, S., Chen, D., Huang, J. and Zhou, C. (2024), "Probabilistic back analysis of slope parameters and reliability evaluation using improved Bayesian updating method", Rock Soil Mech., 45(3), 835-845. https://doi.org/10.26599/RSM.2024.9435485.
- Hu, J.L., Tang, X.W. and Qiu, J.N. (2016), "Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data", Soil Dyn. Earthq. Eng., 89, 49-60. https://doi.org/10.1016/j.soildyn.2016.07.007.
- Javadi, A.A. and Rezania, M. (2009), "Applications of artificial intelligence and data mining techniques in soil modelling", Geomech. Eng., 1(1), 53-74. https://doi.org/10.12989/gae.2009.1.1.053.
- Javadi, A.A., Faramarzi, A. and Ahangar-Asr, A. (2012), "Analysis of behaviour of soils under cyclic loading using EPR-based finite element method", Finite Elem. Anal. Des., 58, 53-65. https://doi.org/10.1016/j.finel.2012.04.005.
- Jin, Y., Biscontin, G. and Gardoni, P. (2018), "A Bayesian definition of 'most probable' parameters", Geotech. Res., 5, 1-52. https://doi.org/10.1680/jgere.18.00027.
- Jin, Y.F., Yin, Z.Y. and Zhou, W.H. (2019), "Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method", Acta Geotech., 14(6), 1925-1947. https://doi.org/10.1007/s11440-019-00847-1.
- Jong, S.C., Ong, D.E.L. and Oh, E. (2021), "State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction", Tunnell. Undergr. Sp. Technol., 113, 103946. https://doi.org/10.1016/j.tust.2021.103946.
- Kalantari, A., Johari, A., Zandpour, M. and Kalantari, M. (2023), "Effect of spatial variability of soil properties and geostatistical conditional simulation on reliability characteristics and critical slip surfaces of soil slopes", Transp. Geotech., 39, 100933. https://doi.org/10.1016/j.trgeo.2023.100933.
- Karpatne, A., Atluri, G., Faghmous, J.H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N. and Kumar, V. (2017), "Theory-guided data science: A new paradigm for scientific discovery from data", IEEE Transac. Knowled. Data Eng., 29(10), 2318-2331. https://doi.org/10.1109/TKDE.2017.2720168.
- Khandelwal, M., Marto, A., Fatemi, S.A., Ghoroqi, M., Armaghani, D.J., Singh, T.N. and Tabrizi, O. (2018), "Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples", Eng. Comput., 34(2), 307-317. https://doi.org/10.1007/s00366-017-0541-y.
- Khatti, J., Grover, K.S., Kim, H.J., Mawuntu, K.B.A. and Park, T.W. (2024), "Prediction of ultimate bearing capacity of shallow foundations on cohesionless soil using hybrid LSTM and RVM approaches: An extended investigation of multicollinearity", Comput. Geotech., 165, 105912. https://doi.org/10.1016/j.compgeo.2023.105912.
- Kong, D., Chen, Y. and Li, N. (2018), "Gaussian process regression for tool wear prediction", Mech. Syst. Signal Pr., 104, 556-574. https://doi.org/10.1016/j.ymssp.2017.11.021.
- Koza, J.R. (1994), "Genetic programming as a means for programming computers by natural selection", Stat. Comput., 4(2), 87-112. https://doi.org/10.1007/BF00175355.
- Kramer, S.L. (2016), Geotechnical Earthquake Engineering, CRC Press, Taylor & Francis Group, Boca Raton, FL, USA.
- Kubo, H., Naoi, M. and Kano, M. (2024), "Recent advances in earthquake seismology using machine learning", Earth. Planet. Sp., 76(1), https://doi.org/10.1186/s40623-024-01982-0.
- Kuo, Y.L., Jaksa, M.B., Lyamin, A.V. and Kaggwa, W.S. (2009), "ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil", Comput. Geotech., 36(3), 503-516. https://doi.org/10.1016/j.compgeo.2008.07.002.
- Li, C., Jiang, S.H., Li, J. and Huang, J. (2020), "Bayesian approach for sequential probabilistic back analysis of uncertain geomechanical parameters and reliability updating of tunneling-induced ground settlements", Adv. Civil Eng., 2020(1), 8528304. https://doi.org/10.1155/2020/8528304.
- Li, D., Li, Y. and Liu, C. (2021), "Gaussian process regression model incorporated with tool wear mechanism", Chin. J. Aeronaut., 35(10), 393-400. https://doi.org/10.1016/j.cja.2021.08.009.
- Li, N. and Jimenez, R. (2018), "A logistic regression classifier for long-term probabilistic prediction of rock burst hazard", Nat. Hazards, 90(1), 197-215. https://doi.org/10.1007/s11069-017-3044-7.
- Li, S.Q. and Chen, Y.S. (2020), "Analysis of the probability matrix model for the seismic damage vulnerability of empirical structures", Nat. Hazards, 104(1), 705-730. https://doi.org/10.1007/s11069-020-04187-2.
- Liu, H., Su, H., Sun, L. and Dias-da-Costa, D. (2024), "State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering", Artif. Intell. Rev., 57(8), 196. https://doi.org/10.1007/s10462-024-10836-w.
- Liu, L., Song, Z. and Li, X. (2024), "Artificial intelligence in tunnel construction: A comprehensive review of hotspots and frontier topics", Geohazard Mech., 2(1), 1-12. https://doi.org/10.1016/j.ghm.2023.11.004.
- Liu, M., Liao, S., Yang, Y., Men, Y., He, J. and Huang, Y. (2021), "Tunnel boring machine vibration-based deep learning for the ground identification of working faces", J. Rock Mech. Geotech. Eng., 13(6), 1340-1357. https://doi.org/10.1016/j.jrmge.2021.09.004.
- Liu, X. and Liu, B. (2023), "A hybrid time series model for predicting the displacement of high slope in the loess plateau region", Sustainab., 15(6), 5423. https://doi.org/10.3390/su15065423.
- Liu, X., Hussein, S.H., Ghazali, K.H., Tung, T.M. and Yaseen, Z.M. (2021), "Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction", Complex., 2021(1), 6666699. https://doi.org/10.1155/2021/6666699.
- Lu, Q., Parlikad, A.K., Woodall, P., Xie, X., Liang, Z., Konstantinou, E., Heaton, J. and Schooling, J. (2019), "Developing a dynamic digital twin at building and city levels: A case study of the West Cambridge campus", J. Manag. Eng., 36(3), 05020004. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763.
- Lundberg, S. and Lee, S.I. (2017), "A unified approach to interpreting model predictions", Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA, December.
- Luo, Z., Atamturktur, S., Cai, Y. and Juang, C.H. (2012), «Reliability analysis of basal-heave in a braced excavation in a 2-D random field", Comput. Geotech., 39, 27-37. https://doi.org/10.1016/j.compgeo.2011.08.005.
- Luo, Z., Atamturktur, S., Juang, C.H., Huang, H. and Lin, P.S. (2011), "Probability of serviceability failure in a braced excavation in a spatially random field: Fuzzy finite element approach", Comput. Geotech., 38, 1031-1040. https://doi.org/10.1016/j.compgeo.2011.07.009.
- Mahdevari, S., Haghighat, H.S. and Torabi, S.R. (2013), "A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation", Tunnell. Undergr. Sp. Technol., 38, 59-68. https://doi.org/10.1016/j.tust.2013.05.002.
- Mahdevari, S., Shahriar, K., Yagiz, S. and Akbarpour Shirazi, M. (2014), "A support vector regression model for predicting tunnel boring machine penetration rates", Int. J. Rock Mech. Min. Sci., 72, 214-229. https://doi.org/10.1016/j.ijrmms.2014.09.012.
- Marr, B. (2018), The Key Definitions of Artificial Intelligence (AI) that Explain Its Importance; Forbes, Jersey City, NJ, USA. https://www.forbes.com/sites/bernardmarr/2018/02/14/the-key-definitions-of-artificial-intelligence-ai-that-explain-its-importance/
- Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. and Galstyan, A. (2021), "A Survey on Bias and Fairness in Machine Learning", ACM Comput. Surv., 54, 1-35. https://doi.org/10.1145/3457607.
- Mitelman, A. and Urlainis, A. (2023), "Investigation of transfer learning for tunnel support design", Math., 11(7), 1623. https://doi.org/10.3390/math11071623.
- Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S. and Floridi, L. (2016), "The ethics of algorithms: Mapping the debate", Big Data Soc., 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679.
- Moayedi, H. and Hayati, S. (2018), "Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods", Appl. Soft Comput., 66, 208-219. https://doi.org/10.1016/j.asoc.2018.02.027.
- Moayedi, H., Gor, M., Mosallanezhad, M., Ghareh, S. and Le, B.N. (2024), "Limit equilibrium and swarm intelligence solutions in analyzing shallow footing's bearing capacity located on two-layered cohesionless soils", Geomech. Eng., 38(4), 439-453. https://doi.org/10.12989/gae.2024.38.4.439.
- Mohanty, R., Suman, S. and Das, S.K. (2016), "Prediction of vertical pile capacity of driven pile in cohesionless soil using artificial intelligence techniques", Int. J. Geotech. Eng., 12(2), 209-216. https://doi.org/10.1080/19386362.2016.1269043.
- Molnar, C. (2020), Interpretable Machine Learning, Leanpub, Victoria, BC, Canada.
- Muduli, P., Das, M., Samui, P. and Das, S. (2013), "Uplift capacity of suction caisson in clay using artificial intelligence techniques", Marine Georesour. Geotech., 31(4), 375-390. https://doi.org/10.1080/1064119X.2012.690827.
- Nguyen, B.Q.V. and Kim, Y.T. (2024), "The effect of soil physical properties on predicting shear strength parameters based on comparing ensemble learning, deep learning, and support vector machine models", Geomech. Eng., 39(3), 241-256. https://doi.org/10.12989/gae.2024.39.2.241.
- Niu, Z., Zhong, G. and Yu, H. (2021), "A review on the attention mechanism of deep learning", Neurocomput., 452, 48-62. https://doi.org/10.1016/j.neucom.2021.03.091.
- Ouyang, W., Li, G., Chen, L. and Liu, S.W. (2024), "Machine learning-based soil-structure interaction analysis of laterally loaded piles through physics-informed neural networks", Acta Geotech., 19(7), 4765-4790. https://doi.org/10.1007/s11440-023-02179-7.
- Padarian, J., Minasny, B. and Mcbratney, A. (2020), "Machine learning and soil sciences: A review aided by machine learning tools", Soil, 6, 35-52. https://doi.org/10.5194/soil-6-35-2020.
- Pal, M. (2006), "Support vector machines-based modelling of seismic liquefaction potential", Int. J. Numer. Anal. Method. Geomech., 30(10), 983-996. https://doi.org/https://doi.org/10.1002/nag.509.
- Pham, T.A., Ly, H.B., Tran, V.Q., Giap, L.V., Vu, H.L.T. and Duong, H.A.T. (2020), "Prediction of pile axial bearing capacity using artificial neural network and random forest", Appl. Sci., 10(5), 1871. https://doi.org/10.3390/app10051871.
- Phoon, K.K. and Kulhawy, F.H. (1999), "Characterization of geotechnical variability", Can. Geotech. J., 36(4), 612-624. https://doi.org/10.1139/t99-038.
- Qi, C. and Tang, X. (2018), "Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study", Comput. Industr. Eng., 118, 112-122. https://doi.org/10.1016/j.cie.2018.02.028.
- Qi, H., Paz-Kagan, T., Karnieli, A. and Li, S. (2017), "Linear multi-task learning for predicting soil properties using field spectroscopy", Remote Sens., 9(11), 1099. https://doi.org/10.3390/rs9111099.
- Qi, X.H. and Zhou, W.H. (2017), "An efficient probabilistic back-analysis method for braced excavations using wall deflection data at multiple points", Comput. Geotech., 85, 186-198. https://doi.org/10.1016/j.compgeo.2016.12.032.
- Rafiei, M.H. and Adeli, H. (2017), "A novel machine learning-based algorithm to detect damage in high-rise building structures", Struct. Des. Tall Spec. Build., 26, e1400. https://doi.org/10.1002/tal.1400.
- Raissi, M., Perdikaris, P. and Karniadakis, G.E. (2019), "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations", J. Comput. Phys., 378, 686-707. https://doi.org/10.1016/j.jcp.2018.10.045.
- Salimi, A., Rostami, J., Moormann, C. and Delisio, A. (2016), "Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs", Tunnell. Undergr. Sp. Technol., 58, 236-246. https://doi.org/10.1016/j.tust.2016.05.009.
- Samui, P. (2012), "Multivariate adaptive regression spline (Mars) for prediction of elastic modulus of jointed rock mass", Geotech. Geol. Eng., 31, 249-253. https://doi.org/10.1007/s10706-012-9584-4.
- Sarker, I.H. (2021), "Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions", SN Comput. Sci., 2(6), 1-20. https://doi.org/10.1007/s42979-021-00815-1.
- Shahin, M. (2014), "Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks", Int. J. Geomech., 14, 06014012. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000370.
- Shahin, M., Jaksa, M. and Maier, H. (2008), "State of the art of artificial neural networks in geotechnical engineering", Electron. J. Geotech. Eng., 8(1), 1-26. https://doi.org/10.5515/JKIEES.2008.8.1.001
- Shahin, M.A. (2016), "State-of-the-art review of some artificial intelligence applications in pile foundations", Geosci. Front., 7(1), 33-44. https://doi.org/10.1016/j.gsf.2014.10.002.
- Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2002), "Predicting settlement of shallow foundations using neural networks", J. Geotech. Geoenviron. Eng., 128(9), 785-793. https://doi.org/10.1061/(asce)1090-0241(2002)128:9(785).
- Shao, W., Yue, W., Zhang, Y., Zhou, T., Zhang, Y., Dang, Y., Wang, H., Feng, X. and Chao, Z. (2023), "The application of machine learning techniques in geotechnical engineering: A review and comparison", Math., 11(18), 3976. https://doi.org/10.3390/math11183976.
- Shen, S.L., Wu, H.N., Cui, Y.J. and Yin, Z.Y. (2014), "Long-term settlement behaviour of metro tunnels in the soft deposits of Shanghai", Tunnell. Undergr. Sp. Technol., 40, 309-323. https://doi.org/10.1016/j.tust.2013.10.013.
- Singhai, K. and Tiwari, N. (2024), "A state-of-the-art review on earthquake soil-structure interaction including dynamic cross interaction (DCI) and site city interactions (SCI)", Earthq. Struct., 27(5), 361-383. https://doi.org/10.12989/eas.2024.27.5.361.
- Soga, K. and Schooling, J. (2016), "Infrastructure sensing", Interf. Focus, 6, 20160023. https://doi.org/10.1098/rsfs.2016.0023.
- Talatahari, S., Hosseini, A., Mirghaderi, S.R. and Rezazadeh, F. (2014), "Optimum performance-based seismic design using a hybrid optimization algorithm", Math. Probl. Eng., 2014(1), 693128. https://doi.org/10.1155/2014/693128.
- Tartakovsky, A., Marrero, C., Perdikaris, P., Tartakovsky, G. and Barajas‐Solano, D. (2020), "Physics-informed deep neural networks for learning parameters and constitutive relationships in subsurface flow problems", Water Resour. Res., 56, e2019WR026731. https://doi.org/10.1029/2019WR026731.
- Uddin, S., Lu, H., Rahman, A. and Gao, J. (2024), "A novel approach for assessing fairness in deployed machine learning algorithms", Sci. Rep., 14(1), 17753. https://doi.org/10.1038/s41598-024-68651-w.
- Wang, J., Xie, Y., Guo, T. and Du, Z. (2023), "Predicting seismic interaction effect between soil and structure group using convolutional neural network", Struct., 50, 1011-1020. https://doi.org/10.1016/j.istruc.2023.02.087.
- Wang, Y. and Cao, Z. (2013), "Probabilistic characterization of Young's modulus of soil using equivalent samples", Eng. Geol., 159, 106-118. https://doi.org/10.1016/j.enggeo.2013.03.017.
- Wang, Y. and Zhao, T. (2016), "Interpretation of soil property profile from limited measurement data: A compressive sampling perspective", Can. Geotech. J., 53(9), 1547-1559. https://doi.org/10.1139/cgj-2015-0545.
- Wang, Y., Hu, Y. and Zhao, T. (2019), "CPT-based subsurface soil classification and zonation in a 2D vertical cross-section using Bayesian compressive sampling", Can. Geotech. J., 57, 1-42. https://doi.org/10.1139/cgj-2019-0131.
- Wang, Y., Huang, J., Tang, H. and Zeng, C. (2020), "Bayesian back analysis of landslides considering slip surface uncertainty", Landslides, 17(9), 2125-2136. https://doi.org/10.1007/s10346-020-01432-4.
- Wang, Y., Zhao, T. and Cao, Z. (2015), "Site-specific probability distribution of geotechnical properties", Comput. Geotech., 70, 159-168. https://doi.org/10.1016/j.compgeo.2015.08.002.
- Wang, Y., Zhao, T. and Phoon, K.K. (2017), "Direct simulation of random field samples from sparsely measured geotechnical data with consideration of uncertainty in interpretation", Can. Geotech. J., 55(6), 862-880. https://doi.org/10.1139/cgj-2017-0254.
- Wu, S., Otake, Y., Higo, Y. and Yoshida, I. (2024), "Pathway to a fully data-driven geotechnics: Lessons from materials informatics", Soil. Found., 64(3), 101471. https://doi.org/10.1016/j.sandf.2024.101471.
- Wu, X., Chen, Q., Skibniewski, M. and Hsu, S.C. (2014), "Towards a safety management approach for adjacent buildings in tunneling environments: Case study in China", Build. Environ., 75, 222-235. https://doi.org/10.1016/j.buildenv.2014.02.005.
- Xiong, J., Pei, T. and Qiu, T. (2023), "A machine learning-based method with integrated physics knowledge for predicting bearing capacity of pile foundations", Geo-Congress 2023, Los Angeles, CA, USA, March.
- Xu, H., He, X., Shan, F., Niu, G. and Sheng, D. (2023), "Machine learning in the stochastic analysis of slope stability: A state-of-the-art review", Modell., 4(4), 426-453. https://doi.org/10.3390/modelling4040025.
- Yaghoubi, E., Yaghoubi, E., Khamees, A. and Vakili, A.H. (2024), "A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering", Neural Comput. Appl., 36(21), 12655-12699. https://doi.org/10.1007/s00521-024-09893-7.
- Yang, P., Yong, W., Li, C., Peng, K., Wei, W., Qiu, Y. and Zhou, J. (2023), "Hybrid random forest-based models for earth pressure balance tunneling-induced ground settlement prediction", Appl. Sci., 13(4), 2574. https://doi.org/10.3390/app13042574.
- Yousheng, D., Keqin, Z., Zhongju, F., Wen, Z., Xinjun, Z. and Huiling, Z. (2024), "Machine learning based prediction model for the pile bearing capacity of saline soils in cold regions", Struct., 59, 105735 https://doi.org/10.1016/j.istruc.2023.105735.
- Zhai, Z., Chen, F., Yu, H., Hu, J., Zhou, X. and Xu, H. (2024), "PS-MTL-LUCAS: A partially shared multi-task learning model for simultaneously predicting multiple soil properties", Ecol. Inform., 82, 102784. https://doi.org/10.1016/j.ecoinf.2024.102784.
- Zhang, J., Yang, S., Zhang, L.L. and Zhou, M.L. (2022), "Bayesian estimation of soil-water characteristic curves", Can. Geotech. J., 59(4), 569-582. https://doi.org/10.1139/cgj-2021-0070.
- Zhang, L., Pan, Y., Wu, X. and Skibniewski, M. (2021), Artificial Intelligence in Construction Engineering and Management, Springer, Singapore.
- Zhang, W., Gu, X., Hong, L., Han, L. and Wang, L. (2023), "Comprehensive review of machine learning in geotechnical reliability analysis: Algorithms, applications and further challenges", Appl. Soft Comput., 136, 110066. https://doi.org/10.1016/j.asoc.2023.110066.
- Zhang, W., Wu, C., Zhong, H., Li, Y. and Wang, L. (2021), "Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization", Geosci. Front., 12(1), 469-477. https://doi.org/10.1016/j.gsf.2020.03.007.
- Zhang, W., Zhang, R. and Goh, A. (2018), "Multivariate adaptive regression splines approach to estimate lateral wall deflection profiles caused by braced excavations in clays", Geotech. Geol. Eng., 36(2), 1349-1363. https://doi.org/10.1007/s10706-017-0397-3.
- Zhang, W., Zhang, R., Wu, C., Goh, A.T.C. and Wang, L. (2022), "Assessment of basal heave stability for braced excavations in anisotropic clay using extreme gradient boosting and random forest regression", Undergr. Sp. (Chin.), 7(2), 233-241. https://doi.org/10.1016/j.undsp.2020.03.001.
- Zhang, W., Zhang, R., Wu, C., Goh, A.T.C., Lacasse, S., Liu, Z. and Liu, H. (2020), "State-of-the-art review of soft computing applications in underground excavations", Geosci. Front., 11(4), 1095-1106. https://doi.org/10.1016/J.GSF.2019.12.003.
- Zhao, L.S., Zhou, W.H., Su, L.J., Garg, A. and Yuen, K.V. (2019), "Selection of physical and chemical properties of natural fibers for predicting soil reinforcement", J. Mater. Civil Eng., 31(10), 04019212. https://doi.org/10.1061/(asce)mt.1943-5533.0002850.
- Zheng, D., Huang, J., Li, D., Kelly, R. and Sloan, S. (2017), "Embankment prediction using testing data and monitored behaviour: A Bayesian updating approach", Comput. Geotech., 93C, 150-162. https://doi.org/10.1016/j.compgeo.2017.05.003.
- Zhou, J., Li, E., Wei, H., Li, C., Qiao, Q. and Jahed Armaghani, D. (2019), "Random forests and cubist algorithms for predicting shear strengths of rockfill materials", Appl. Sci., 9, 1621. https://doi.org/10.3390/app9081621.
- Zhou, J., Shi, X., Du, K., Qiu, X., Li, X. and Mitri, H.S. (2016), "Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel", Int. J. Geomech., 17(6), 04016129. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000817.
- Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H. and He, Q. (2021), "A comprehensive survey on transfer learning", Proc. IEEE, 109(1), 43-76. https://doi.org/10.1109/JPROC.2020.300455.