Реклама, 0+. ООО «ИнжПроектСтрой» ИНН 5902163884
erid: 2vfnxvifrnd

Реклама, 0+. АО «Мостдоргеотрест» ИНН 7716750744
erid: 2vfnxwa1cem

Литературное обозрение

Artificial intelligence transformations in geotechnics: progress, challenges and future enablers

Sheil B.

Инженерный факультет Кембриджского университета, г. Кембридж, Великобритания

bbs24@cam.ac.uk

Anagnostopoulos C.

Факультет (школа) компьютерных наук Университета Глазго, г. Глазго, Великобритания

Buckley R.

Факультет (школа) инженерных наук имени Джеймса Ватта Университета Глазго, г. Глазго, Великобритания

Ciantia M.O.

Факультет (школа) естественных и инженерных наук Университета Данди, г. Данди, Великобритания; факультет наук о Земле и окружающей среде Университета Милана-Бикокка, г. Милан, Италия

Febrianto E.

Факультет (школа) инженерных наук имени Джеймса Ватта Университета Глазго, г. Глазго, Великобритания

Fu J.

Факультет (школа) инженерных наук и материаловедения Лондонского университета имени Королевы Марии, г. Лондон, Великобритания

Gao Z.

Факультет (школа) инженерных наук имени Джеймса Ватта Университета Глазго, г. Глазго, Великобритания

Geng X.

Инженерный факультет Университета Уорика, г. Ковентри, Великобритания

Gong B.

Колледж инженерных, дизайнерских и естественных наук при Лондонском университете имени Брунеля, г. Лондон, Великобритания

Hanley K.

Бакалавриат по химическим технологиям Эдинбургского университета, г. Эдинбург, Великобритания

He P.

Факультет (школа) естественных и инженерных наук Университета Данди, г. Данди, Великобритания

Kolomvatsos K.

Факультет инженерных и компьютерных наук Университета Фессалии, г. Волоc, Греция

Lopes B.C.F.L.

Факультет гражданского и экологического строительства Университета Стратклайда, г. Глазго, Великобритания

Ninic J.

Инженерный факультет (школа) Бирмингемского университета, г. Бирмингем, Великобритания

Previtali M.

Факультет (школа) естественных и инженерных наук Университета Данди, г. Данди, Великобритания

Rezania M.

Инженерный факультет (школа) Университета Уорика, г. Ковентри, Великобритания

Ruiz-Lopez A.

Компания Seequent («Сиквент») – дочерняя компания корпорации Bentley Systems по подземным технологиям, г. Крайстчерч, Новая Зеландия; инженерный факультет Лондонского Имперского колледжа, г. Лондон, Великобритания

Sun J.

Факультет (школа) инженерных наук имени Джеймса Ватта Университета Глазго, г. Глазго, Великобритания

Suryasentana S.

Факультет гражданского и экологического строительства Университета Стратклайда, г. Глазго, Великобритания

Taborda D.

Инженерный факультет Лондонского имперского колледжа, г. Лондон, Великобритания

Utili S.

Инженерный факультет (школа) Университета Ньюкасла, г. Ньюкасл-апон-Тайн, Великобритания

Whyte S.

Компания Geowynd («Геовинд»), г. Лондон, Великобритания

Zhang P.

Факультет гражданского и экологического строительства Сингапурского национального университета, Сингапур

Скачать препринт

We present to our readers an adapted translation of the extensive review paper "Artificial intelligence transformations in geotechnics: progress, challenges and future enablers", authored by an international group of researchers (predominantly from the United Kingdom). This work is based on the authors' report at the 1st Workshop on Al in Geotechnics, held in May 2023 in Glasgow, Scotland, UK. After that workshop, the paper had been revised for almost two years, and it was submitted to the Computers and Geotechnics journal of the Elsevier publishing company in January 2025. The review will be published in that journal in January 2026. The paper is currently available in open access under the CC BY 4.0 license, which allows users to copy, distribute, adapt, modify it, and build upon it, provided that the license type, changes made are indicated and the original source is referenced. In our case, the full reference to the original source is provided at the end of the translation. Our reliance on the underground space to deliver critical civil engineering infrastructure is growing: to accommodate utility and transport infrastructure in urban environments, to provide innovative housing and commercial solutions, and to support proliferating renewable energy infrastructure, particularly offshore. Artificial intelligence (AI) is arguably the most promising enabler to transform geotechnical engineering by extracting knowledge from data to achieve step-change increases in efficiency, sustainability, reliability and safety. This paper seeks to develop a shared understanding of the state of the art of AI in geotechnics and to explore future developments. By way of example, specific popular use cases in geotechnics are considered to highlight current progress in AI applications including intelligent site investigation, predictive modelling for soil behaviour, and optimisation of design and construction processes. The paper then addresses key research challenges, such as data scarcity and interpretability, and discusses the opportunities that lie ahead in the integration of AI with geotechnical engineering. Finally, priority technological enablers are identified for future transformations.

DOI: 10.58339/2949-0677-2025-7-4-52-80

УДК: 004; 624.131

Финансирование: Нет информации

Ссылка для цитирования: Sheil B., Anagnostopoulos C., Buckley R., Ciantia M.O., Febrianto E., Fu J., Gao Z., Geng X., Gong B., Hanley K., He P., Kolomvatsos K., Lopes B.C.F.L., Ninic J., Previtali M., Rezania M., Ruiz-Lopez A., Sun J., Suryasentana S., Taborda D., Utili S., Whyte S., Zhang P. Transformatsii v geotehnike s pomoshch'yu iskusstvennogo intellekta: dostizheniya, problemy i perspektivy (adapt. per. s angl.) [Artificial intelligence transformations in geotechnics: progress, challenges and future enablers (adapted translation from English into Russian)] // Geoinfo. 2025. T. 7. № 4. S. 52–80. DOI:10.58339/2949-0677-2025-7-4-52-80 (in Rus.)

Статья в РИНЦ: Нет информации

Ключевые слова: geotechnics; geotechnical investigations; artificial intelligence; intelligent site investigations; soil behavior modeling; geotechnical design optimization; machine learning; human-machine interaction; interdisciplinary approach; ethical aspects; legal aspects.

Список литературы

Jiang F., Jiang Y., Zhi H., Dong Y., Li H., Ma S., Wang Y., Dong Q., Shen H., Wang Y. Artificial intelligence in healthcare: past, present and future // Stroke Vascular Neurol. 2017. Vol. 2. № 4.
Goodell J.W., Kumar S., Lim W.M., Pattnaik D. Artificial intelligence and machine learning in finance: identifying foundations, themes, and research clusters from bibliometric analysis // J. Behav. Exp. Financ. 2021. Vol. 32. Article 100577.
Febrianto E., Butler L., Girolami M., Cirak F. Digital twinning of self-sensing structures using the statistical finite element method // Data-Centric Eng. 2022. Vol. 3. № e31.
Sun F., Febrianto E., Fernando H., Butler L., Cirak F., Hoult N. Data-informed statistical finite element analysis of rail buckling // Comput. Struct. 2023. Vol. 289. Article 107163.
Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser Ј., Polosukhin I. Attention is all you need // Adv. Neural Informat. Process. Syst. 2017. Vol. 30.
Sheil B.B., Suryasentana S.K., Cheng W.C. Assessment of anomaly detection methods applied to microtunneling // J. Geotech. Geoenviron. Eng. 2020. Vol. 146. № 9. Article 04020094.
Sheil B.B., Suryasentana S.K., Mooney M.A., Zhu H. Machine learning to inform tunnelling operations: recent advances and future trends // Proc. Inst. Civil Eng. – Smart Infrastruct. Constr. 2020. Vol. 173. № 4. P. 74–95.
Stuyts B., Suryasentana S.K. Applications of data science in offshore geotechnical engineering: state of practice and future perspectives // 9th International SUT OSIG Conference. 2023.
Suryasentana S.K., Sheil B.B. Demystifying the connections between Gaussian process regression and kriging // 9th International SUT OSIG Conference. 2023. P. 1–8.
Russell S.J., Norvig P. Artificial intelligence: a modern approach. London, 2010.
MacKay D.J.C. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003.
Kennedy M.C., O’Hagan A. Bayesian calibration of computer models // J. R. Stat. Soc. Ser. B (Stat Methodol.). 2001. Vol. 63. P. 425–464.
Girolami M., Febrianto E., Yin G., Cirak F. The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions // Comput. Methods Appl. Mech. Eng. 2021. Vol. 375. Article 113533.
Hu J.Z., Zhang J., Huang H.W., Zheng J.G. Value of information analysis of site investigation program for slope design // Comput. Geotech. 2021. Vol. 131. Article 103938.
Zhao T., Wang Y. Determination of efficient sampling locations in geotechnical site characterization using information entropy and Bayesian compressive sampling // Can. Geotech. J. 2019. Vol. 56. № 11. P. 1622–1637.
Yoshida I., Tasaki Y., Tomizawa Y. Optimal placement of sampling locations for identification of a two-dimensional space // Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. 2022. Vol. 16. № 1. P. 98–113.
Li J., Cassidy M.J., Huang J., Zhang L., Kelly R. Probabilistic identification of soil stratification // Geotechnique. 2016. Vol. 66. № 1. P. 16–26.
Wang Y., Huang K., Cao Z. Probabilistic identification of underground soil stratification using cone penetration tests // Can. Geotech. J. 2013. Vol. 50. № 7. P. 766–776.
Houlsby N.M.T., Houlsby G.T. Statistical fitting of undrained strength data // Geotechnique. 2013. Vol. 63. № 14. P. 1253–1263.
Ching J., Wang J.-S., Juang C.H., Ku C.-S. Cone penetration test (CPT)-based stratigraphic profiling using the wavelet transform modulus maxima method // Canadian Geotechnical J. 2015. Vol. 52. № 12. P. 1993–2007.
Suryasentana S.K., Lawler M., Sheil B.B., Lehane B.M. Probabilistic soil strata delineation using DPT data and Bayesian changepoint detection // J. Geotech. Geoenviron. Eng. 2023. Vol. 149 . № 4. Article 06023001.
Wang Y., Hu Y., Zhao T. Cone Penetration test (CPT)-based subsurface soil classification and zonation in two-dimensional vertical cross section using Bayesian compressive sampling // Can. Geotech. J. 2020. Vol. 57. № 7. P. 947–958.
Cao Z.J., Zheng S., Li, D.Q., Phoon K.K. Bayesian identification of soil stratigraphy based on soil behavior type index // Can. Geotech. J. 2019. Vol. 56. № 4. P. 570–586.
Gong W., Zhao C., Juang C.H., Tang H., Wang H., Hu X. Stratigraphic uncertainty modelling with random field approach // Comput. Geotech. 2020. Vol. 125. Article 103681.
Shuku T., Phoon K.K., Yoshida I. Trend estimation and layer boundary detection in depth-dependent soil data using sparse Bayesian lasso // Comput. Geotech. 2020. Vol. 128. Article 103845.
Hegazy Y.A., Mayne P.W. Objective site characterization using clustering of piezocone data // J. Geotech. Geoenviron. Eng. 2002. Vol. 128. № 12. P. 986–996.
Zhao T., Wang Y. Interpolation and stratification of multilayer soil property profile from sparse measurements using machine learning methods // Eng. Geol. 2020. Vol. 265. Article 105430.
Zhou X., Shi P., Sheil B., Suryasentana S. Knowledge-based U-Net and transfer learning for automatic boundary segmentation // Adv. Eng. Inf. 2024. Vol. 59. Article 102243.
Suryasentana S.K., Sheil B.B., Lawler M. Assessment of Bayesian changepoint detection methods for soil layering identification using cone penetration test data // Geotechnics. 2024. Vol. 4. № 2 . P. 382–398.
Firouzianbandpey S., Ibsen L.B., Griffiths D.V., Vahdatirad M.J., Andersen L.V., Sшrensen J.D. Effect of spatial correlation length on the interpretation of normalized CPT data using a kriging approach // J. Geotech. Geoenviron. Eng. 2015. Vol. 141. № 12. Article 04015052.
Cai Y., Li J., Li X., Li D., Zhang L. Estimating soil resistance at unsampled locations based on limited CPT data // Bull. Eng. Geol. Environ. 2019. Vol. 78. P. 3637–3648.
He X., Xu H., Sabetamal H., Sheng D. Machine learning aided stochastic reliability analysis of spatially variable slopes // Comput. Geotech. 2020. Vol. 126. Article 103711.
Rahman M.H., Abu-Farsakh M.Y., Jafari N. Generation and evaluation of synthetic cone penetration test (CPT) data using various spatial interpolation techniques // Can. Geotech. J. 2021. Vol. 58. № 2. P. 224–237.
Wang Y., Zhao T. Statistical interpretation of soil property profiles from sparse data using Bayesian compressive sampling // Geotechnique. 2017. Vol. 67. № 6. P. 523–536.
Wang Y., Li P. Data-driven determination of sample number and efficient sampling locations for geotechnical site investigation of a cross-section using Voronoi diagram and Bayesian compressive sampling // Comput. Geotech. 2021. Vol. 130. Article 103898.
Zhao T., Xu L., Wang Y. Fast non-parametric simulation of 2D multi-layer cone penetration test (CPT) data without pre-stratification using markov chain Monte Carlo simulation // Eng. Geol. 2020. Vol. 273. Article 105670.
Shi C., Wang Y. Development of subsurface geological cross-section from limited site-specific boreholes and prior geological knowledge using iterative convolution XGBoost // J. Geotech. Geoenviron. Eng. 2021. Vol. 147. № 9. Article 04021082.
Shi C., Wang Y. Nonparametric and data-driven interpolation of subsurface soil stratigraphy from limited data using multiple point statistics // Can. Geotech. J. 2021. Vol. 58. № 2. P. 261–280.
Sauvin G., Vanneste M., Vardy M.E., Klinkvort R.T., Carl Fredrik F. Machine learning and quantitative ground models for improving offshore wind site characterization // Offshore Technol. Conf. (OTC 2019), Houston, Texas, USA. 2019 . Vol. 2. P. 1323–1339. DOI:10.4043/29351-MS.
Wu S., Zhang J.M., Wang R. Machine learning method for CPTu based 3D stratification of New Zealand geotechnical database sites // Adv. Eng. Inf. 2021. Vol. 50. Article 101397.
Xie J., Huang J., Lu J., Burton G.J., Zeng C., Wang Y. Development of two- dimensional ground models by combining geotechnical and geophysical data // Eng. Geol. 2022. Vol. 300. Article 106579.
Huang J., Zheng D., Li D.Q., Kelly R., Sloan S.W. Probabilistic characterization of two-dimensional soil profile by integrating cone penetration test (CPT) with multi-channel analysis of surface wave (MASW) data// Can. Geotech. J. 2018. Vol. 55. № 8. P. 1168–1181.
Ghose R., Goudswaard J. Integrating S-wave seismic-reflection data and cone-penetration-test data using a multiangle multiscale approach // Geophysics. 2004. Vol. 69. № 2. P. 440–459.
Wellmann J.F., De La Varga M., Murdie R.E., Gessner K., Jessell M. Uncertainty estimation for a geological model of the Sandstone greenstone belt, Western Australia – insights from integrated geological and geophysical inversion in a Bayesian inference framework // Geol. Soc. Lond. Spec. Publ. 2018. Vol. 453. № 1. P. 41–56.
Medina-Cetina Z., Son J., Moradi M. Bayesian stratigraphy integration of geophysical, geological, and geotechnical surveys data // Offshore Technol. Conf. (OTC 2019), Houston, Texas, USA. 2019. Vol. 5. P. 3431–3440.
Xu J., Wang Y., Zhang L. 2021. Interpolation of extremely sparse geo-data by data fusion and collaborative Bayesian compressive sampling // Comput. Geotech. Vol. 134. Article 104098.
Xu J., Wang Y., Zhang L. Fusion of geotechnical and geophysical data for 2D subsurface site characterization using multi-source Bayesian compressive sampling // Can. Geotech. J. 2022. Vol. 59. № 10. P. 1756–1773.
Christensen C.W., Harrison E.J., Pfaffhuber A.A., Lund A.K. A machine learning-based approach to regional-scale mapping of sensitive glaciomarine clay combining airborne electromagnetics and geotechnical data // Near Surf. Geophys. 2021. Vol. 19. № 5. P. 523–539.
Chen J., Vissinga M., Shen Y., Hu S., Beal E., Newlin J. Machine learning–based digital integration of geotechnical and ultrahigh-frequency geophysical data for offshore site characterizations // J. Geotech. Geoenviron. Eng. 2021. Vol. 147. № 12. Article 04021160.
Coelho B.Z., Karaoulis M. Data fusion of geotechnical and geophysical data for three-dimensional subsoil schematisations // Adv. Eng. Inf. 2022. Vol. 53. Article 101671.
Roscoe K.H., Schofield, A.N., Wroth C.P. On the yielding of soils // Geotechnique. 1958. Vol. 8. № 1. P. 22–53.
Schofield A.N., Wroth C.P. Critical state soil mechanics. London, UK: McGraw Hill, 1968.
Been K., Jefferies M.G. A state parameter for sands // Geotechnique. 1985. Vol. 35. № 2 . P. 99–112.
Reynolds O. LVII. On the dilatancy of media composed of rigid particles in contact. With experimental illustrations // The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. 1885. Vol. 20. № 127. P. 469-481. DOI:10.1080/14786448508627791.
Taylor D.W. Fundamentals of Soil Mechanics. New York: John Wiley & Sons, 1948.
Bolton M.D. The strength and dilatancy of sands // Geotechnique. 1986. Vol. 36. № 1. P. 65–78.
Bishop A.W. The strength of soils as engineering materials // Geotechnique. 1966. Vol. 16. P. 91–128.
Amarasinghe S.F., Parry R.H. Anisotropy in heavily overconsolidated kaolin // J. Geotech. Eng. Div. ASCE, 1975. Vol. 101. № GT12. P. 1277–1292.
Burland J.B. On the compressibility and shear strength of natural clays // Geotechnique .1990. Vol. 40. № 3. P. 329–378.
Leroueil S., Vaughan P.R. The general and congruent effects of structure in natural soils and weak rocks // Geotechnique. 1990. Vol. 40. № 3. P. 467–488.
Liu W.Z., Shi M.L., Miao L.C., Xu L.R., Zhang D.W. Constitutive modeling of the destructuration and anisotropy of natural soft clay // Comput. Geotech. 2013. Vol. 51. P. 24–41.
Lade P.V., Duncan J.M. Stress-path dependent behavior of cohesionless soil // J. Geotech. Eng. Div. 1976. Vol. 102. № 1. P. 51–68.
Suklje L. The analysis of the consolidation process by the isotaches method // Proc. 4th Int. Conf. Soil Mech Found. Eng., London, 1957. Vol. 1. P. 200–206.
Bjerrum L. Engineering geology of Norwegian normally consolidated marine clays as related to the settlements of buildings // Geotechnique. 1967. Vol. 17. № 2. P. 83–119.
Roscoe K.H., Bassett R.H., Cole E.R.L. Principal axes observed during simple shear of a sand // Proc., Geotechnical Conf. on Shear Strength Properties of Natural Soils and Rocks, Oslo, Norwegian Geotechnical Society, 1967. P. 231–237.
Kim Y.T., Leroueil S. Modeling the viscoplastic behaviour of clays during consolidation: application to Berthierville clay in both laboratory and field conditions // Can. Geotech. J. 2001. Vol. 38. № 3. P. 484–497.
Yin Z.-Y., Karstunen M., Chang C.S., Koskinen M., Lojander M. Modeling time-dependent behavior of soft sensitive clay // J. Geotech. Geoenviron. Eng. 2011 . Vol. 137. № 11. P. 1103–1113.
Su D., Yang Z.X. Drained analyses of cylindrical cavity expansion in sand incorporating a bounding-surface model with state-dependent dilatancy // App. Math. Model. 2019. Vol. 68. P. 1–20.
Kang X., Xia Z., Chen R., Ge L., Liu X. The critical state and steady state of sand: a literature review // Mar. Georesour. Geotec. 2019. Vol. 37. № 9. P. 1105–1118.
Wan R.G., Guo P.J. A simple constitutive model for granular soils: modified stress-dilatancy approach // Comput. Geotech. 1988. Vol. 22. P. 109–133.
Su L.-J., Yin J.-H., Zhou W.-H. Influences of overburden pressure and soil dilation on soil nail pull-out resistance // Comput. Geotech. 2010. Vol. 37. № 4. P. 555–564.
Dafalias Y.F. An anisotropic critical state soil plasticity model // Mech. Res. Comm. 1986. Vol. 13 . № 6. P. 341–347.
Yin Z.Y., Chang C.S., Karstunen M., Hicher P.Y. An anisotropic elastic–viscoplastic model for soft clays // Int. J. Solids Struct. 2010. Vol. 47. № 5. P. 665–677.
Kang X., Xia Z., Chen R.P. Measurement and correlations of K0 and Vs anisotropy of granular soils // Proc. Inst. Civil Eng.-Geotech. Eng. 2020. Vol. 173. № 6. P. 546–561.
Hu X., Zhang Y., Guo L., Wang J., Cai Y., Fu H., Cai Y. Cyclic behavior of saturated soft clay under stress path with bidirectional shear stresses // Soil Dyn. Earthq. Eng. 2018. Vol. 104. P. 319–328.
Tian Y., Yao Y.P. Modelling the non-coaxiality of soils from the view of cross-anisotropy // Comput. Geotech. 2017. Vol. 86. P. 219–229.
Zhou M.M., Meschke G. A three-phase thermo-hydro-mechanical finite element model for freezing soils // Int. J. Numer. Anal. Meth. Geomech. 2013. Vol. 37. № 18. P. 3173–3193.
Dafalias Y.F., Manzari M.T. Simple plasticity sand model accounting for fabric change effects // Journal of Engineering Mechanics. 2004. Vol. 130. № 6. P. 622–634.
Ghaboussi J., Carret J., Wu X. Material modelling with neural networks // Proceedings of the international conference on numerical methods in engineering: theory and applications, Swansea, UK, 1990. P. 701–717.
Ghaboussi J., Carret J., Wu X. Knowledge-based modelling of material behaviour with neural networks // J. Eng. Mech. Div. 1991. Vol. 117. № 1. P. 132–153.
Ellis G.W., Yao C., Zhao R. Neural network modeling of the mechanical behavior of sand // Proc. of the 9th ASCE Conference on Engineering Mechanics, Texas, 1992. P. 421–424.
Ellis G.W., Yao C., Zhao R., Penumadu D. Stress-strain modeling of sands using artificial neural netwoks // ASCE J. Geotech. Eng. Div. 1995. Vol. 121. № 5. P. 429–435.
Ghaboussi J., Sidarta D.E. New nested adaptive neural networks (NANN) for constitutive modeling // Comput. Geotech. 1998. Vol. 22 . № 1. P. 29–52.
Penumadu D., Zhao R. Triaxial compression behavior of sand and gravel using artificial neural networks (ANN) // Comput. Geotech. 1999. Vol. 24. № 3. P. 207–230.
Javadi A.A., Rezania M., Applications of artificial intelligence and data mining techniques in soil modeling // Geomech. Eng. 2009. Vol. 1. № 1. P. 53–74.
Shin H.S., Pande G.N. On self-learning finite element code based on monitored response of structures // Comput. Geotech. 2000. Vol. 27. P. 161–178.
Lefik M., Schrefler B.A. Artificial neural network as an incremenal non-linear constitutive model for finite element code // Comput. Methods Appl. Mech. Eng. 2003. Vol. 192. P. 3265–3283.
Hashash Y.M.A., Jung S., Ghaboussi J. Numerical implementation of a neural network based material model in finite element analysis // Int. J. Numer. Meth. Eng. 2004. Vol. 59. P. 989–1005.
Javadi A.A., Rezania M. Intelligent finite element method: an evolutionary approach to constitutive modeling // Adv. Eng. Inf. 2009. Vol. 23. № 4. P. 442–451.
Raissi M., Perdikaris P., Karniadakis G.E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations // J. Comput. Phys. 2019. Vol. 378. P. 686–707.
Masi F., Stefanou I., Vannucci P., Maffi-Berthier V. Thermodynamics-based artificial neural networks for constitutive modeling // J. Mech. Phys. Solids. 2021. Vol. 147. Article 104277.
Masi F., Stefanou I. Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) // Comput. Methods Appl. Mech. Eng. 2022. Vol. 398. Article 115190.
Weinan E., Yu B. The deep ritz method: a deep learning-based numerical algorithm for solving variational problems // Commun. Math. Stat. 2018. Vol. 6. P. 1–12.
Liu D., Wang Y. Multi-fidelity physics-constrained neural network and its application in materials modeling // J. Mech. Des. 2019. Vol. 141. № 12. Article 121403.
Sun L., Gao H., Pan S., Wang J.-X. Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data // Comput. Meth. Appl. Mech. Eng. 2020 . Vol. 361. Article 112732.
Cuomo S., Di Cola V.S., Giampaolo F., Rozza G., Raissi M., Piccialli F. Scientific machine learning through physics-informed neural networks: where we are and what’s next // J. Sci. Comput. 2022. Vol. 92. Article 88.
Vlassis N.N., Sun W. Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening // Comput. Meth. Appl. Mech. Eng. 2021. Vol. 377 . Article 113695.
Flaschela M., Kumar S., De Lorenzis L. Unsupervised discovery of interpretable hyperelastic constitutive laws // Comput. Meth. Appl. Mech. Eng. 2021. Vol. 381. Article 113852.
Zhang P., Yin Z.Y., Sheil B. Interpretable data-driven constitutive modelling of soils with sparse data // Comput. Geotech. 2023. Vol. 160. Article 105511.
Zhang J., Wang Z., Hu J., Xiao S., Shang W. Bayesian machine learning-based method for prediction of slope failure time // J. Rock Mech. Geotech. Eng. 2022. Vol. 14. № 4. P. 1188–1199.
Das B.M. Principles of geotechnical engineering. Cengage Learning, 2021.
Provenzano P., Ferlisi S., Musso A. Interpretation of a model footing response through an adaptive neural fuzzy inference system // Comput. Geotech. 2004. Vol. 31. № 3. P. 251–266.
Shahnazari H., Tutunchian M.A. Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: an evolutionary approach // KSCE J. Civ. Eng. 2012. Vol. 16. P. 950–957.
Tsai H.C., Tyan Y.Y., Wu Y.W., Lin Y.H. Determining ultimate bearing capacity of shallow foundations using a genetic programming system // Neural Comput. & Appl. 2013. Vol. 23. P. 2073–2084.
Lawal A.I., Kwon S. Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations // J. Rock Mech. Geotech. Eng. 2023. Vol. 15. № 3. P. 747–759.
Shahin M.A., Maier H.R., Jaksa M.B. Predicting settlement of shallow foundations using neural networks // J. Geotech. Geoenviron. Eng. 2002. Vol. 128. № 9. P. 785–793.
Shahin M.A., Maier H.R., Jaksa M.B. Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models // Comput. Geotech. 2003. Vol. 30. № 8. P. 637–647.
Rezania M., Javadi A.A. A new genetic programming model for predicting settlement of shallow foundations // Can. Geotech. J. 2007. Vol. 44. № 12. P. 1462–1473.
Samui P., Sitharam T.G. Least-square support vector machine applied to settlement of shallow foundations on cohesionless soils // Int. J. Numer. Anal. Meth. Geomech. 2008. Vol. 32. № 17. P. 2033–2043.
Zhang J., Dias D., An L., Li, C. Applying a novel slime mould algorithm-based artificial neural network to predict the settlement of a single footing on a soft soil reinforced by rigid inclusions // Mech. Adv. Mater. Struct. 2022. Vol. 31. № 1.
Vergote T.A., Raymackers S. Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability // Ocean Eng. 2022. Vol. 266. Article 113181.
Buckley R., Chen Y.M., Sheil B., Suryasentana S., Xu D.D., James R.M. Bayesian optimization for CPT-based prediction of impact pile drivability // J. Geotech. Geoenviron. Eng. 2023. Vol. 149. № 11. Article 04023100.
Pal M., Deswa S. Modelling pile capacity using Gaussian process regression // Comput. Geotech. 2010. Vol. 37. № 7–8. P. 942–947.
Alkroosh I., Nikraz H. Correlation of pile axial capacity and CPT data using gene expression programming // Geotech. Geol. Eng. 2011. Vol. 29. P. 725–748.
Kordjazi A., Nejad F.P., Jaksa M.B. Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data // Comput. Geotech. 2014. Vol. 55. P. 91–102.
Kardani N., Zhou A., Nazem M., Shen S.L. Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches // Geotech. Geol. Eng. 2020. Vol. 38. P. 2271–2291.
Alexander J.S., Buckley R.M., Whyte S.A. Machine learning to expedite concept monopile design // Proceedings of the XVIII ECSMGE “Geotechnical Engineering Challenges to Meet Current and Emerging Needs of Society”, Lisbon, 2024. P. 2760–2763.
Suryasentana S.K., Burd H.J., Byrne B.W., Aghakouchak A., Sorensen T. Comparison of machine learning models in a data-driven approach for scalable and adaptive design of laterally-loaded monopile foundations // International Symposium on Frontiers in Offshore Geotechnics. Deep Foundations Institute (DFI), USA, 2020. ISBN9780976322948.
Muduli P.K., Das S.K., Das M.R. Prediction of lateral load capacity of piles using extreme learning machine // Int. J. Geotech. Eng. 2013. Vol. 7. № 4. P. 388–394.
Taherkhani A.H., Mei Q., Han F. Capacity prediction and design optimization for laterally loaded monopiles in sandy soil using hybrid neural network and sequential quadratic programming // Comput. Geotech. 2023. Vol. 163. Article 105745.
Nejad F.P., Jaksa M.B., Kakhi M., McCabe B.A. Prediction of pile settlement using artificial neural networks based on standard penetration test data // Comput. Geotech. 2009. Vol. 36. № 7. P. 1125–1133.
Jebur A.A., Atherton W., Al Khaddar R.M., Loffill E. Settlement prediction of model piles embedded in sandy soil using the Levenberg-Marquardt (LM) training algorithm // Geotech. Geol. Eng. 2018. Vol. 36. P. 2893–2906.
Ge Q., Li C., Yang F. Support vector machine to predict the pile settlement using novel optimization algorithm // Geotech. Geol. Eng. 2023. Vol. 41. № 7 . P. 1–15.
Khatti J., Samadi H., Grover K.S. Estimation of settlement of pile group in clay using soft computing techniques // Geotech. Geol. Eng. 2023. Vol. 42. № 3. P. 1–32.
Alm T., Hamre L. Soil model for pile driveability predictions based on CPT interpretations // Proc. of the 15th Int. Conf. on Soil Mechanics and Geotechnical Engineering. Boca Raton, FL, USA: CRC Press, 2001. P. 1297–1302.
Sheil B.B., Suryasentana S.K., Templeman J.O., Phillips B.M., Cheng W.C., Zhang L. Prediction of pipe-jacking forces using a Bayesian updating approach // J. Geotech. Geoenviron. Eng. 2022. Vol. 148. № 1. Article 04021173.
Deng L., Smith A., Dixon N., Yuan H. Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements // Eng. Geol. 2021. Vol. 293. Article 106315.
Xu H., He X., Shan F., Niu G., Sheng D. Machine learning in the stochastic analysis of slope stability: a state-of-the-art review // Modelling. 2023 . Vol. 4. № 4 . P. 426–453.
Luo Z., Bui X.N., Nguyen H., Moayedi H. A novel artificial intelligence technique for analyzing slope stability using PSO-CA model // Eng. Comput. 2021. Vol. 37. P. 533–544.
Mahmoodzadeh A., Mohammadi M., Farid H.A.H., Hashim I.H., Nariman A.S., Nejati H.R. Prediction of safety factors for slope stability: comparison of machine learning techniques // Natural Hazards. 2022. Vol. 111. P. 1771–1799.
Aminpour M., Alaie R., Khosravi S., Kardani N., Moridpour S., Nazem M., Slope stability machine learning predictions on spatially variable random fields with and without factor of safety calculations // Comput. Geotech. 2023. Vol. 153. Article 105094.
Xiao T., Zhang L.M., Cheung R.W.M., Lacasse S. Predicting spatio-temporal man-made slope failures induced by rainfall in Hong Kong using machine learning techniques // Geotechnique. 2023. Vol. 73. № 9. P. 749–765.
Guardiani C., Soranzo E., Wu W. Time-dependent reliability analysis of unsaturated slopes under rapid drawdown with intelligent surrogate models // Acta Geotech. 2022. Vol. 17. P. 1071–1096.
Lin Y., Zhou K., Li J. Prediction of slope stability using four supervised learning methods // IEEE Access. 2018. Vol. 6. P. 31169–31179.
Zeng P., Zhang T., Li T., Jimenez R., Zhang J., Sun X. Binary classification method for efficient and accurate system reliability analyses of layered soil slopes // Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. 2022. Vol. 16. № 3. P. 435–451.
Novellino A., Cesarano M., Cappelletti P., Di Martire D., Di Napoli M., Ramondini M., Sowter A., Calcaterra D. Slow-moving landslide risk assessment combining machine learning and InSAR techniques // Catena. 2021. Vol. 203. Article 105317.
Bayaraa M., Rossi C., Kalaitzis F., Sheil B. Entity embeddings in remote sensing: application to deformation monitoring for infrastructure // Remote Sens. 2023. Vol. 15. № 20. Article 4910.
Cao B.T., Obel M., Freitag S., Mark P., Meschke G. Artificial neural network surrogate modelling for real-time predictions and control of building damage during mechanised tunnelling // Adv. Eng. Softw. 2020 . Vol. 149. Article 102869.
Ninic J., Gamra A., Ghiassi B. Real-time assessment of tunnelling-induced damage to structures within the building information modelling framework // Underground Space. 2024. Vol. 14. P. 99–117.
Mokhtari S., Mooney M.A. Feasibility study of EPB shield automation using deep learning // Tunnels and Underground Cities: Engineering and Innovation Meet Archaeology, Architecture and Art. Boca Raton, FL, USA: CRC Press, 2019. P. 2691–2699.
Chen X., Li X., Zhu H. Condition evaluation of urban metro shield tunnels in Shanghai through multiple indicators multiple causes model combined with multiple regression method // Tunn. Undergr. Space Technol. 2019. Vol. 85. P. 170–181.
Li X., Lin X., Zhu H., Wang X., Liu Z. Condition assessment of shield tunnel using a new indicator: the tunnel serviceability index // Tunn. Undergr. Space Technol. 2017. Vol. 67. P. 98–106.
Zhu M., Zhu H., Guo F., Chen X., Ju J.W. Tunnel condition assessment via cloud model-based random forests and self-training approach // Comput. Aided Civ. Inf. Eng. 2021. Vol. 36. № 2. P. 164–179. DOI:10.1111/mice.12601.
Sheil B.B., Suryasentana S.K., Mooney M.A., Zhu, H., McCabe B.A., O’Dwyer K.G. Discussion: machine learning to inform tunnelling operations: recent advances and future trends // Proc. Inst. Civil Eng.-Smart Infrastruct. Constr. 2020 . Vol. 173. № 1. P. 180–181.
Yu Y., Workman A., Grasmick J.G., Mooney M.A., Hering A.S. Space-time outlier identification in a large ground deformation data set // J. Qual. Technol. 2018. Vol. 50. № 4. P. 431–445.
Xue Y.D., Zhang S. A fast metro tunnel profile measuring method based on close-range photogrammetry // Information Technology in Geo-engineering: Proceedings of the 3rd International Conference (ICITG), Guimaraes, Portugal. Cham, Switzerland: Springer, 2019. P. 57–69.
Khetwal S., Pei S., Gutierrez M. A data-driven approach for direct assessment and analysis of traffic tunnel resilience // Information Technology in Geo- engineering: Proceedings of the 3rd International Conference (ICITG), Guimaraes, Portugal. Cham, Switzerland: Springer, 2019. P. 168–177.
Ding H., Liu S., Cai S., Xia Y. Big data analysis of structural defects and traffic accidents in existing highway tunnels // Information Technology in Geoengineering: Proceedings of the 3rd International Conference (ICITG), Guimaraes, Portugal. Cham, Switzerland: Springer, 2019. P. 189–195.
Hayashi H., Miyanaka M., Gomi H., et al. Prediction of forward tunnel face score of rock mass classification for stability by applying machine learning to drilling data // Information Technology in Geoengineering: Proceedings of the 3rd International Conference (ICITG), Guimaraes, Portugal. Cham, Switzerland: Springer, 2019. P. 268–278.
Liu Y., Hou S. Rockburst prediction based on particle swarm optimization and machine learning algorithm // Information Technology in Geoengineering: Proceedings of the 3rd International Conference (ICITG), Guimaraes, Portugal. Cham, Switzerland: Springer, 2019. P. 292–303.
Zhao W., Wei Y., Liu B., Liu S., Xiao L. Design and application of automatic monitoring and BIM technology to the construction of shield-bored underneath building // Information Technology in Geoengineering: Proceedings of the 3rd International Conference (ICITG), Guimaraes, Portugal. Cham, Switzerland: Springer, 2019. P. 493–501.
Charles J.A., Gourvenec S., Vardy M.E. Recovering shear stiffness degradation curves from classification data with a neural network approach // Acta Geotech. 2023. Vol. 18. № 10. P. 1–15.
Lambert S., Toe D., Mentani A., Bourrier F., 2021. A meta-model-based procedure for quantifying the on-site efficiency of rockfall barriers // Rock Mech. Rock Eng. Vol. 54. P. 487–500.
Previtali M., Ciantia M.O., Spadea S., Castellanza R., Crosta G. Assessing rockfall barrier performance through block propagation codes and meta-models // Proceedings of the 16th International Conference of the International Association for Computer Methods and Advances in Geomechanics. Cham, Switzerland: Springer International Publishing, 2022. P. 291–298.
Lanfranconi C., Sala G., Frattini P., Crosta G.B., Valagussa A. Assessing the rockfall protection efficiency of forests at the regional scale // Landslides. 2020 . Vol. 17. P. 2703–2721.
Bao Y., Chen Z., Wei S., Xu Y., Tang Z., Li H. The state of the art of data science and engineering in structural health monitoring // Engineering. 2019. Vol. 5. № 2. P. 234–242.
Jeong S., Ko J., Kim J. The effectiveness of a wireless sensor network system for landslide monitoring // IEEE Access. 2019. Vol. 8. P. 8073–8086.
Soga K., Luo L. Distributed fiber optics sensors for civil engineering infrastructure sensing // J. Struct. Integrity Maint. 2018. Vol. 3. № 1. P. 1–21.
Bayaraa M., Sheil B., Rossi C. InSAR and numerical modelling for tailings dam monitoring – the Cadia failure case study // Geotechnique. 2024. Vol. 74. № 10. P. 985–1003.
Voyagaki E., Crispin J.J., Gilder C.E., Ntassiou K., O’Riordan N., Nowak P., Sadek T., Patel D., Mylonakis G., Vardanega P.J. The DINGO database of axial pile load tests for the UK: settlement prediction in fine-grained soils // Georisk: Assess. Manage. Risk Eng. Syst. Geohazards. 2022. Vol. 16. № 4. P. 640–661.
Vahab M., Shahbodagh B., Haghighat E., Khalili N. Application of physics- informed neural networks for forward and inverse analysis of pile-soil interaction // Int. J. Solids Struct. 2023. Vol. 277. Article 112319.
Zhang P., Yin Z.Y., Sheil B. Interpretable data-driven constitutive modelling of soils with sparse data // Comput. Geotech. 2023. Vol. 160. Article 105511.
Zhang P., Yin Z.Y., Jin Y.F., Sheil B. Physics-constrained hierarchical data-driven modelling framework for complex path-dependent behaviour of soils // Int. J. Numer. Anal. Meth. Geomech. 2023. Vol. 46. № 10. P. 1831–1850.
Ribeiro M.T., Singh S., Guestrin C. Why should i trust you? Explaining the predictions of any classifier // Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016. P. 1135–1144.
Guidotti R., Monreale A., Ruggieri S., Turini F., Giannotti F., Pedreschi D. A survey of methods for explaining black box models // ACM Comput. Surv. (CSUR). 2018. Vol. 51. № 5. P. 1–42.
Lundberg S.M., Lee S.I. A unified approach to interpreting model predictions // Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4-9 December 2017. P. 4768-4777.
Mitchell M., Wu S., Zaldivar A., Barnes P., Vasserman L., Hutchinson B., Spitzer E., Inioluwa D.R., Timnit G. Model cards for model reporting // Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019. P. 220–229.
Peng J., Liu X. Automated code compliance checking research based on BIM and knowledge graph // Sci. Rep. 2023. Vol. 13. № 1. Article 7065.
Shahin M.A., Jaksa M.B., Maier H.R. State of the art of artificial neural networks in geotechnical engineering // Electron. J. Geotech. Eng. 2008. Vol. 8. № 1. P. 1–26.
Baghbani A., Choudhury T., Costa S., Reiner J. Application of artificial intelligence in geotechnical engineering: a state-of-the-art review // Earth Sci. Rev. 2022. Vol. 228. Article 103991.
Latif K., Sharafat A., Seo J. Digital twin-driven framework for TBM performance prediction, visualization, and monitoring through machine learning // Appl. Sci. 2023. Vol. 13. № 20. Article 11435.
Kumar K. Geotechnical parrot tales (gpt): Harnessing large language models in geotechnical engineering // J. Geotech. Geoenviron. Eng. 2024. Vol. 150. № 1. Article 02523001.
Erichson N.B., Mathelin L., Yao Z., Brunton S.L., Mahoney M.W., Kutz J.N., Shallow neural networks for fluid flow reconstruction with limited sensors // Proc. Roy. Soc. 2020. Vol. A476. № 2238. Article 20200097.
Yuan B., Heitor A., Wang H., Chen X. Physics-informed deep learning to solve three-dimensional Terzaghi consolidation equation: forward and inverse problems // Arabian Journal for Science and Engineering. 2025. 24 September. https://doi.org/10.1007/s13369-025-10602-2.
Ouyang W., Li G., Chen L., Liu S.-W. Machine learning-based prediction of drilled-shaft capacity // Soils and Foundations. 2024. Vol. 64. № 2. P. 262–274.
Tao R., Pan Y., Liu Z., et al. A physics-inspired machine learning approach for water-tightness estimation of defective cut-off walls with random construction errors // Acta Geotech. 2023. Vol. 18. P. 5959–5982. https://doi.org/10.1007/s11440-023-02030-z.
Perdikaris P., Raissi M., Damianou A., Lawrence N.D., Karniadakis G.E. Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling // Proc. Roy. Soc. A: Math. Phys. Eng. Sci. 2017. Vol. 473. 2198. № 2198. Article 20160751.
Le Gratiet L., Garnier J. Recursive co-kriging model for design of computer experiments with multiple levels of fidelity // Int. J. Uncertain. Quantif. 2014. Vol. 4. № 5. P. 365–386.
Xu C., Cao B.T., Yuan Y., Meschke G. A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: application to real-time settlement prediction during tunnel construction // Eng. Appl. Artif. Intel. 2024. Vol. 133. Article 108156.
Lam R., Allaire D.L., Willcox K.E. Multifidelity optimization using statistical surrogate modeling for non-hierarchical information sources // 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2015. Article 0143.
Zhou X., Shi P. Multi-scale generative adversarial network for 2D subsurface reconstruction using multi-fidelity geological exploration data // Adv. Eng. Inf. 2025. Vol. 66. Article 103482.
Zhang P., Yin Z.Y., Jin Y.F., Yang J., Sheil B. Physics-informed multifidelity residual neural networks for hydromechanical modeling of granular soils and foundation considering internal erosion // J. Eng. Mech. 2022. Vol. 148. № 4. Article 04022015.
He G.F., Zhang P., Yin Z.Y., Jin Y.F., Yang Y. Multi-fidelity data-driven modelling of rate-dependent behaviour of soft clays // Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards.2023. Vol. 17. № 1. P. 64–76.
Zhang P., Yin Z.Y., Sheil B. Multifidelity constitutive modeling of stress-induced anisotropic behavior of clay // J. Geotech. Geoenviron. Eng. 2024. Vol. 150. № 3. Article 04024003.
Brunton S.L., Proctor J.L., Kutz J.N. Discovering governing equations from data by sparse identification of nonlinear dynamical systems // Proc. Natl. Acad. Sci. 2016. Vol. 113. № 15. P. 3932–3937.
Zhang P., Yin Z.-Y., Sheil B. A physics-informed data-driven approach for consolidation analysis // Geotechnique. 2023 . Vol. 74. № 7. P. 620–631.
Apoji D., Sheil B., Soga K. Shaping the future of tunneling with data and emerging technologies // Data-Centric Eng. 2023. Vol. 4. Article e29.
Zhao Y., Liu Y., Mu E. A review of intelligent subway tunnels based on digital twin technology // Buildings. 2024. Vol. 14. № 8. Article 2452.
Chen L., Tophel A., Hettiyadura U., Kodikara J. An investigation into the utility of large language models in geotechnical education and problem solving // Geotechnics. 2024. Vol. 4. № 2. P. 470–498.
Xu H.R., Zhang N., Yin Z.Y., Njock P.G.A. GeoLLM: a specialized large language model framework for intelligent geotechnical design // Comput. Geotech. 2025. Vol. 177. Article 106849.
Qian Z., Shi C. Large language model-empowered paradigm for automated geotechnical site planning and geological characterization // Autom. Constr. 2025. Vol. 173. Article 106103.
Li H., Shi C. Few-shot learning of geological cross-sections from sparse data using large language model // Geodata and AI. 2025. Vol. 2. Article 100010.
Xu H.R., Zhang N., Yin Z.Y., Njock P.G.A. Multimodal framework integrating multiple large language model agents for intelligent geotechnical design // Autom. Constr. 2025. Vol. 176. Article 106257.
Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y. Generative adversarial nets // Adv. Neural Inform. Process. Syst. 2014. Vol. 3. № 11.
Song Y., Sohl-Dickstein J., Kingma D.P., Kumar A., Ermon S., Poole B. Score-based generative modeling through stochastic differential equations // ArXiv preprint. 2020. arXiv:2011.13456v1 [cs.LG] 26 Nov 2020.
Montero F.C., Coelho B.Z., Smyrniou E., Taormina R., Vardon P.J. SchemaGAN: a conditional generative adversarial network for geotechnical subsurface schematisation // Comput. Geotech. 2025. Vol. 183 . Article 107177.
Ge Q., Li J., Lacasse S., Sun H., Liu Z. Data-augmented landslide displacement prediction using generative adversarial network // J. Rock Mech. Geotech. Eng. 2024. Vol. 16. № 10. P. 4017–4033.
Tripura T., Chakraborty S. Wavelet Neural Operator for solving parametric partial differential equations in computational mechanics problems // Computer Methods in Applied Mechanics and Engineering. 2023. Vol. 404. Article 115783.
Zhong W., Meidani H. 2025. Physics-informed geometry-aware neural operator // Comput. Methods Appl. Mech. Eng. Vol. 434. Article 117540.
Jiang Y., Byrne E., Glassey J., Chen X. Integrating graph neural network-based surrogate modeling with inverse design for granular flows // Ind. Eng. Chem. Res. 2024. Vol. 63. № 20. P. 9225–9235.
Choi Y., Macedo J., Liu C. Differentiable graph neural network simulator for forward and inverse modeling of multi-layered slope system with multiple material properties // ArXiv preprint. 2025. arXiv:2504.15938.