
DOI: 10.25296/1993-5056-2021-16-1-10-23
О ЗАДАЧАХ ЦИФРОВИЗАЦИИ И ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ИНЖЕНЕРНОЙ ГЕОЛОГИИ

Королев В.А.
Приведен обзор современного состояния цифровизации и применения искусственного интеллекта (ИИ) в инженерной геологии. Показано, что мировой тренд на цифровизацию экономики и всех сторон развития человеческой цивилизации не оставляет в стороне от ее внедрения в жизнь и инженерную геологию, в рамках которой использование цифровых технологий постоянно расширяется как в производственной (изыскательской), так и в научной областях. Существенной предпосылкой цифровизации на уровне отдельной инженерно-геологической компании (как отдельной производственно-хозяйственной единицы) является электронное ведение хозяйственной деятельности. Охарактеризованы области инженерной геологии, в которых информация уже сейчас представляется в цифровой форме, указаны перспективные направления дальнейшей цифровизации. Рассмотрены направления применения технологий искусственного интеллекта в инженерной геологии. Показано, что внедрение цифровизации и технологий искусственного интеллекта представляют собой важнейшую научно-организационную проблему. Для целей инженерной геологии наиболее перспективны и востребованы следующие направления: разработка интеллектуальных систем, основанных на инженерно-геологических и иных знаниях; интеллектуальные работы инженерно-геологической направленности (анализ и интерпретация результатов изысканий, прогнозирование и т.п.); распознавание образов; разработка программного обеспечения ИИ. Дальнейшее развитие инженерной геологии будет основываться на разработке и применении гибридных систем, сочетающих различные методы искусственного интеллекта, а также традиционные программирование и инженерно-геологические исследования. В сфере высшего образования подготовке инженер-геологов — специалистов в области цифровизации — должно уделяться повышенное внимание, обусловленное возрастающими потребностями в таких специалистах. Пока же из высших учебных заведений страны выпускаются лишь кадры низшего уровня — инженер-геологи аналитики данных, тогда как кадры среднего и высшего уровня целенаправленно практически не готовятся. Дальнейшее внедрение возможностей цифровизации и ИИ во все направления инженерной геологии — грунтоведение, инженерную геодинамику и региональную инженерную геологию — позволит эффективнее решать как производственные (изыскательские), так и научно-исследовательские задачи.
1. Болдырев Г.Г., Барвашов В.А., Шейнин В.И., Каширский В.И., Идрисов И.Х., Дивеев А.А., 2019. Информационные системы в геотехнике — 3D-геотехника. Геотехника, Том XI, № 2, с. 6–27, https://doi.org/10.25296/2221-5514-2019-11-2-6-27.
2. Гаврилов А.В., 2002. Гибридные интеллектуальные системы. Изд-во НГТУ, Новосибирск.
3. Королев В.А., 1999. Информационно-методические аспекты создания грунтоведческих ГИС. Вопросы инженерно-геологических, инженерно-экологических и инженерно-геодезических изысканий в Уральском регионе, Материалы научно-технического семинара, г. Екатеринбург, 1999, с. 66–67.
4. Королев В.А., 2020. Методология научных исследований в инженерной геологии. ООО «СамПолиграфист», Москва.
5. Королев В.А., 2021. Цифровизация и искусственный интеллект в инженерной геологии. Новые идеи и теоретические аспекты
инженерной геологии, Труды Международной научной конференции, под ред. В.Т. Трофимова и В.А. Королева, Москва, 2021,
с. 207–214.
6. Королев В.А., Соколов В.Н., Шлыков В.Г., 1999. Исследование фундаментальной взаимосвязи состава, структуры и свойств глинистых грунтов на основе геоинформационных технологий. Известия секции наук о Земле Российской академии естественных наук, № 2, с. 47–61.
7. Крылов А., 2018. Искусственный интеллект, основанный на логике.
URL: https://zakon.ru/blog/2018/8/27/iskusstvennyj_intellekt_osnovannyj_na_logike (дата обращения: 05.02.2021).
8. Математическая логика и искусственный интеллект, 2019. Математический форум «Math Help Planet».
URL: http://mathhelpplanet.com/static.php?p=matematicheskaya-logika-i-iskusstvenniy-intellekt (дата обращения: 05.02.2021).
9. Новые идеи и теоретические аспекты инженерной геологии, 2021. Труды Международной научной конференции,
под ред. В.Т. Трофимова и В.А. Королева, Москва, 2021.
10. Озмидов О.Р., 2018. Цифровое грунтоведение — новая учебная дисциплина. Геоинфо, URL: https://www.geoinfo.ru/product/ozmidovoleg-rostislavovich/cifrovoe-gruntovedenie-novaya-uchebnaya-disciplina-39486.shtml (дата обращения: 08.02.2021).
11. Переверзева С.А., Кочнева М.Н., 2011. Специализированная база данных — как инструмент анализа и управления данными инженерно-геологических изысканий (на примере ЛАЭС). Атомное строительство, № 5, с. 15–17.
12. Розина И.Н., 2019. Цифровизация образования. URL: http://ito.lgb.ru/tezises/1027.doc (дата обращения: 20.03.2019).
13. Соколов В.Н., Королев В.А., Шлыков В.Г., Юрковец Д.И., Разгулина О.В., Чернов М.С., 2006. Экспертная система для получения обобщенных инженерно-геологических показателей глинистых грунтов Московского региона. Сергеевские чтения, Материалы годичной сессии Научного совета РАН по проблемам геоэкологии, инженерной геологии и гидрогеологии, Вып. 8, Москва, 2006, с. 341–343.
14. Халин В.Г. (ред.), 2019. Российские университеты в условиях цифровизации: математические и инструментальные методы оценки качества управления. Проспект, Москва.
15. Adams T.M., Bosscher P.J., 1995. Integration of GIS and knowledge-based systems for subsurface characterization. In M.L. Maher,
I. Tommelein (eds), Expert systems for civil engineers: integrated and distributed systems. Publishing house of the ASCE, New York, NY, USA.
16. Basheer I.A., Reddi L.N., Najjar Y.M., 1996. Site characterization by neuronets — an application to the landfill siting problem. Ground Water, Vol. 34, No. 4, pp. 610–617.
17. Butler A.G., Franklin J.A., 1990. Classex — an expert system for rock mass classification. Static and dynamic considerations in rock engineering, Proceedings of the International Symposium society for rock mechanics, Swaziland, 1990, pp. 73–80.
18. Cai Y.D., 1995. Soil classification by neural-network. Advances in Engineering Software, Vol. 22, No. 2, pp. 95–97.
19. Chen L., Wang L., Miao J., Gao H., Zhang Y., Yao Y., Bai M., Mei L., He J., 2020. Review of the application of Big Data and artificial intelligence in geology. Journal of Physics: Conference Series, Vol. 1684, Paper 012007, https://doi:10.1088/1742-6596/1684/1/012007.
20. Chen R.P., Lin X.T., Kang X., Zhong Z., Liu Y., Zhang P., Wu H., 2018. Deformation and stress characteristics of existing twin tunnels induced by close-distance EPBS under-crossing. Tunnelling and Underground Space Technology, Vol. 82, pp. 468–481,
https://doi.org/10.1016/j.tust.2018.08.059.
21. Davey-Wilson I.E.G., 1991. Geotechnical laboratory test simulation using AI techniques. In B.H.V. Topping (ed.), Artificial Intelligence and Civil Engineering. Civil-Comp Press, Edinburgh, UK, pp. 119–124.
22. Davey-Wilson I.E.G., Mistry K., 1995. An intelligent database to predict geotechnical parameters. In B.H.V. Topping (ed.), Developments in artificial intelligence for civil and structural engineering. Civil-Comp Press, Edinburgh, UK, pp. 105–111.
23. Ellis G.W., Yao C., Zhao R., Penumadu D., 1995. Stress-strain modeling of sands using artificial neural networks. Journal of Geotechnical Engineering, Vol. 121, No. 5, pp. 429–435.
24. Erzin Y., Cetin T., 2012. The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces. Scientia Iranica. Transactions A: Civil Engineering, Vol. 19, No. 2, pp. 188–194.
25. Gao M.Y., Zhang N., Shen S.L., Zhou A., 2020. Real-time dynamic earth-pressure regulation model for shield tunneling by integrating GRU deep learning method with GA optimization. IEEE Access, Vol. 8, pp. 64310–64323, https://doi.org/10.1109/ACCESS.2020.2984515.
26. Gillette D.R., 1991. An expert system for estimating soil strength parameters. Proceedings of the Geotechnical engineering Congress, geotechnical special publication, No. 27, Boulder, Colorado, CO, USA, 1991, pp. 276–287.
27. Goh A.T.C., 1995. Modeling soil correlations using neural networks. Journal of Computing in Civil Engineering, Vol. 9, No. 4,
pp. 275–278.
28. Gomes Correia A., Cortez P., Tinoco J., Marques R., 2013. Artificial intelligence applications in transportation geotechnics. Geotechnical and Geological Engineering, Vol. 31, pp. 861–879, https://doi.org/10.1007/s10706-012-9585-3.
29. Javadi A., Rezania M., 2009. Applications of artificial intelligence and data mining techniques in soil modeling. Geomechanics and Engineering, Vol. 1, No. 1, pp. 53–74, https://doi.org/10.12989/gae.2009.1.1.053.
30. Jin Y.F., Yin Z.Y., Zhou W.H., Horpibulsuk S., 2019. Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method. Acta Geotechnica, Vol. 14, No. 6, pp. 1925–1947, https://doi.org/10.1007/s11440-019-00847-1.
31. Jin Y.F., Yin Z.Y., Zhou W.H., Huang H.W., 2019. Multi-objective optimization-based updating of predictions during excavation. Engineering Applications of Artificial Intelligence, Vol. 78, pp. 102–123, https://doi.org/10.1016/j.engappai.2018.11.002.
32. Jin Y.F., Yin Z.Y., Zhou W.H., Yin J.H., Shao J.F., 2019. A single-objective EPR based model for creep index of soft clays considering L2 regularization. Engineering Geology, Vol. 248, pp. 242–255, https://doi.org/10.1016/j.enggeo.2018.12.006.
33. Juang C.H., Lee D.H., 1989. Development of an expert system for rock mass classification. Civil Engineering Systems, Vol. 6, No. 4, pp. 147–156.
34. Lawal A.I., Kwon S., 2021. Application of artificial intelligence to rock mechanics: an overview. Journal of Rock Mechanics and
Geotechnical Engineering, https://doi.org/10.1016/j.jrmge.2020.05.010. (in press)
35. Maher M.H., Williams T.P., 1991. A hybrid expert system for design with geosynthetics. Proceedings of the Geotechnical engineering Congress, geotechnical special publication, No. 27, Boulder, Colorado, CO, USA, 1991, pp. 241–252.
36. Makantasis K., Protopapadakis E., Doulamis A., Doulamis N., Loupos C., 2015. Deep convolutional neural networks for efficient vision based tunnel inspection. Proceedings of the International Conference on intelligent computer communication and processing, Cluj-Napoca, Romania, 2015, pp. 335–342, https://doi.org/10.1109/ICCP.2015.7312681.
37. Man H., Furukawa T., 2011. Neural network constitutive modelling for non-linear characterization of anisotropic materials. International Journal for Numerical Methods in Engineering, Vol. 85, No. 8, pp. 939–957, https://doi.org/10.1002/nme.2999.
38. Mannsbart G., Resl S., 1993. An expert-system for the design of geotextiles. Geotextiles and Geomembranes, Vol. 12, No. 5,
pp. 441–450.
39. Millar D.L., Calderbank P.A., 1995. On the investigation of a multilayer feedforward neural-network model of rock deformability behavior. Proceedings of the 8th International Congress on rock mechanics, Tokyo, Japan, 1995, pp. 933–938.
40. Miranda T.F.S., 2007. Geomechanical parameters evaluation in underground structures: artificial intelligence, bayesian probabilities and inverse methods. PhD Thesis, University of Minho, Guimarães, Portugal.
41. Nawari N.O., Liang R., Nusairat J., 1999. Artificial intelligence techniques for the design and analysis of deep foundations. Electronic Journal of Geotechnical Engineering, No. 4, pp. 1–21.
42. Negroponte N., 1995. Being Digital. URL: http://inance.ru/2017/09/cifrovaya-ekonomika (дата обращения: 05.02.2021).
43. Parikh S.A., Kameswara Rao N.S.V., 1991. An expert system for civil engineering application. Proceedings of the Geotechnical engineering Congress, geotechnical special publication, No. 27, Boulder, Colorado, CO, USA, 1991, pp. 413–421.
44. Pirnia P., Duhaime F., Manashti J., 2018. Machine learning algorithms for applications in geotechnical engineering. Proceedings of the 71st Canadian Geotechnical Conference and the 13th joint CGS/IAH-CNC groundwater Conference, Edmonton, Canada, 2018, pp. 1–7.
45. Rashidian V., Hassanlourad M., 2013. Application of an artificial neural network for modeling the mechanical behavior of carbonate soils. International Journal of Geomechanics, Vol. 14, No. 1, pp. 142–150, https://doi.org/10.1061/(ASCE)GM.1943-5622.0000299.
46. Roy D.H., Singh T.N., 2019. Predicting deformational properties of Indian coal: soft computing and regression analysis approach. Measurement, Vol. 149, Paper 106975, https://doi.org/10.1016/j.measurement.2019.106975.
47. Santamarina J.C., Chameau J.L., 1987. Expert systems for geotechnical engineers. Journal of Computing in Civil Engineering, Vol. 1, No. 4, pp. 241–252.
48. Shahin M.A., 2013. Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions. In X.S. Yang (ed.), Metaheuristics in water, geotechnical and transport engineering. Elsevier, the Netherland, pp. 169–204, https://doi.org/10.1016/B978-0-12-398296-4.00008-8.
49. Shahin M.A., 2016. State-of-the-art review of some artificial intelligence applications in pile foundations. Geoscience Frontiers, Vol. 7, No. 1, pp. 33–44, https://doi.org/10.1016/j.gsf.2014.10.002.
50. Siller J.T., 1987. Expert systems in geotechnical engineering. In M.L. Maher (ed.), Expert systems for civil engineers: technology and applications. Publishing house of the ASCE, New York, NY, USA, pp. 77–84.
51. Smith I.G.N., Oliphant J., 1991. The use of a knowledge-based system for civil engineering site investigations. In B.H.V. Topping (ed.), Artificial Intelligence and Civil Engineering. Civil-Comp Press, Edinburgh, UK, pp. 105–112.
52. Toll D., 1996. Artificial intelligence applications in geotechnical engineering. Electronic Journal of Geotechnical Engineering, No. 1,
рр. 1–3.
53. Turk G., Logar J., Majes B., 2001. Modelling soil behavior in uniaxial strain conditions by neural networks. Advances in Engineering Software, Vol. 32, No. 10–11, pp. 805–812, https://doi.org/10.1016/S0965-9978(01)00032-1.
54. Wang K., Sun W.C., 2018. A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning. Computer Methods in Applied Mechanics and Engineering, Vol. 334, pp. 337–380, https://doi.org/10.1016/j.cma.2018.01.036.
55. Wang K., Sun W.C., Du Q., 2019. A cooperative game for automated learning of elastoplasticity knowledge graphs and models with AI-guided experimentation. Computational Mechanics, Vol. 64, No. 2, pp. 467–499, https://doi.org/10.1007/s00466-019-01723-1.
56. Wang S., Xiao S., Jia Z., 1994. An engineering geology expert system of shearing zone. Proceedings of the 7th International Congress Association Engineering Geology, Lisboa, Portugal, 1994, pp. 4489–4494.
57. Wislocki A.P., Bentley S.P., 1991. An expert system for landslide hazard and risk assessment. Computers and Structures, Vol. 40,
No. 1, pp 169–172.
58. Yang B.B., Yin K.L., Lacasse S., Liu Z., 2019. Time series analysis and long short-term memory neural network to predict landslide displacement. Landslides, Vol. 16, No. 4, pp. 677–694, https://doi.org/10.1007/s10346-018-01127-x.
59. Yin Z.Y., Jin Y.F., 2020. Practice of artificial intelligence in geotechnical engineering. Journal of Zhejiang University-SCIENCE A (Applied Physics and Engineering), Vol. 21, Issue 6, pp. 407–411.
60. Yin Z.Y., Jin Y.F., Shen J.S., Hicher P.Y., 2018. Optimization techniques for identifying soil parameters in geotechnical engineering: comparative study and enhancement. International Journal for Numerical and Analytical Methods in Geomechanics, Vol. 42, No. 1,
pp. 70–94, https://doi.org/10.1002/nag.2714.
61. Zhang P., Yin Z.Y., Jin Y.F., Chan T.H.T., 2020. A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest. Engineering Geology, Vol. 265, Paper 105328, https://doi.org/10.1016/j.enggeo.2019.105328.
62. Zhang Q., Song J.R., Nie X.Y., 1991. Application of neural network models to rock mechanics and rock engineering. International Journal of Rock Mechanics and Mining Sciences, Vol. 28, No. 6, pp. 535-540.
КОРОЛЕВ В.А.
Московский государственный университет им. М.В. Ломоносова, г. Москва, Россия, va-korolev@bk.ru
Адрес: Ленинские горы, д. 1, г. Москва, 119991, Росси
ON THE PROBLEMS OF DIGITALIZATION AND ARTIFICIAL INTELLIGENCE IN ENGINEERING GEOLOGY

Korolev V.A.
The paper provides an overview of the current state of digitalization and the use of artificial intelligence (AI) in engineering geology. It is shown that the global trend towards digitalization of the economy and all aspects of the development of human civilization does not leave aside its implementation in life and engineering geology, within which the use of digital technologies is constantly expanding both in the production (exploration) and scientific fields. An essential prerequisite for digitalization at the level of a separate engineering and geological company (as a separate production and economic unit) is electronic business management. The areas of engineering geology, in which information are currently already presented in digital form, are characterized, and promising directions for further digitalization of engineering geology are indicated. The directions of application of artificial intelligence technologies in engineering geology are considered. It is shown that the introduction of digitalization and artificial intelligence technologies are the most important scientific and organizational problem. For the purposes of engineering geology, the following areas are most promising and in demand: development of intelligent systems based on engineering-geological and other knowledge; intellectual works of engineering-geological orientation (analysis and interpretation of survey results, forecasting, etc.); pattern recognition; AI software development. Further development of engineering geology will be based on the development and application of hybrid systems that combine various methods of artificial intelligence, as well as traditional programming and engineering-geological research. In the field of higher education, the training of geological engineers — specialists in the field of digitalization, should be given increased attention due to the growing need for such specialists. In the meantime, only lower-level personnel are graduating from the country’s higher educational institutions — geological engineers and data analysts, while middle and higher-level personnel are purposefully practically not trained. Further implementation of digitalization and AI capabilities in all areas of engineering geology — soil science, engineering geodynamics and regional engineering geology, will make it possible to more effectively solve both production (exploration) and research tasks.
1. Boldyrev G.G., Barvashov V.A., Sheynin V.I., Kashirsky V.I., Idrisov I.Kh., Diveev A.A., 2019. Information systems in geotechnical
engineering — 3D-geotechnics. Geotechnics, Vol. XI, No. 2, pp. 6–27, https://doi.org/10.25296/2221-5514-2019-11-2-6-27. (in Russian)
2. Gavrilov A.V., 2002. Hybrid intelligent systems. Publishing house of the Novosibirsk State Technical University, Novosibirsk. (in Russian)
3. Korolev V.A., 1999. Information and methodological aspects of the creation of ground-based GIS. Questions of engineering and geological, engineering and ecological, and engineering and geodetic surveys in the Ural region, Materials of the scientific and technical Seminar, Yekaterinburg, 1999, pp. 66–67. (in Russian)
4. Korolev V.A., 2020. The methodology of scientific research in engineering geology. Sampoligrafist LLC, Moscow. (in Russian)
5. Korolev V.A., 2021. Digitalization and artificial intelligence in engineering geology. New ideas and theoretical aspects of engineering geology, Proceedings of the International scientific Conference, in V.T. Trofimov and V.A. Korolev (eds), Moscow, 2021, pp. 207–214. (in Russian)
6. Korolev V.A., Sokolov V.N., Shlykov V.G., 1999. Investigation of the fundamental relationship of the composition, structure and
properties of clay soils based on geoinformation technologies. Izvestiya Sektsii Nauk o Zemle Rossiyskoy Akademii Yestestvennykh Nauk, No. 2, pp. 47–61. (in Russian)
7. Krylov A., 2018. Artificial intelligence based on logic.
URL: https://zakon.ru/blog/2018/8/27/iskusstvennyj_intellekt_osnovannyj_na_logike (accessed: 5 February 2021). (in Russian)
8. Mathematical logic and artificial intelligence, 2019. Mathematical forum «Math Help Planet».
URL: http://mathhelpplanet.com/static.php?p=matematicheskaya-logika-i-iskusstvenniy-intellekt (accessed: 5 February 2021). (in Russian)
9. New ideas and theoretical aspects of engineering geology, 2021. Proceedings of the International scientific Conference, in V.T. Trofimov and V.A. Korolev (eds), Moscow, 2021. (in Russian)
10. Ozmidov O.R., 2018. Digital soil science — a new academic discipline. Geoinfo, URL: https://www.geoinfo.ru/product/ozmidov-olegrostislavovich/cifrovoe-gruntovedenie-novaya-uchebnaya-disciplina-39486.shtml (accessed: 8 February 2021). (in Russian)
11. Pereverzeva S.A., Kochneva M.N., 2011. Specialized database — as a tool for analysis and management of engineering and geological survey data (for example, LNPP). Atomnoye Stroitelstvo, No. 5, pp. 5–17. (in Russian)
12. Rozina I.N., 2019. Digitalization of education. URL: http://ito.lgb.ru/tezises/1027.doc (accessed: 20 March 2019). (in Russian)
13. Sokolov V.N., Korolev V.A., Shlykov V.G., Yurkovets D.I., Razgulina O.V., Chernov M.S., 2006. Expert system for obtaining generalized engineering-geological indicators of clayey soils of the Moscow region. Sergeevsky readings, Materials of the annual Session of the Russian Academy of Sciences Scientific Council on Geoecology, Engineering Geology and Hydrogeology, Issue 8, Moscow, 2006,
pp. 341–343. (in Russian)
14. Khalin V.G. (ed.), 2019. Russian universities in the context of digitalization: mathematical and instrumental methods for assessing the quality of management. Prospekt, Moscow. (in Russian)
15. Adams T.M., Bosscher P.J., 1995. Integration of GIS and knowledge-based systems for subsurface characterization. In M.L. Maher,
I. Tommelein (eds), Expert systems for civil engineers: integrated and distributed systems. Publishing house of the ASCE, New York, NY, USA.
16. Basheer I.A., Reddi L.N., Najjar Y.M., 1996. Site characterization by neuronets — an application to the landfill siting problem. Ground Water, Vol. 34, No. 4, pp. 610–617.
17. Butler A.G., Franklin J.A., 1990. Classex — an expert system for rock mass classification. Static and dynamic considerations in rock engineering, Proceedings of the International Symposium society for rock mechanics, Swaziland, 1990, pp. 73–80.
18. Cai Y.D., 1995. Soil classification by neural-network. Advances in Engineering Software, Vol. 22, No. 2, pp. 95–97.
19. Chen L., Wang L., Miao J., Gao H., Zhang Y., Yao Y., Bai M., Mei L., He J., 2020. Review of the application of Big Data and artificial intelligence in geology. Journal of Physics: Conference Series, Vol. 1684, Paper 012007, https://doi:10.1088/1742-6596/1684/1/012007.
20. Chen R.P., Lin X.T., Kang X., Zhong Z., Liu Y., Zhang P., Wu H., 2018. Deformation and stress characteristics of existing twin tunnels induced by close-distance EPBS under-crossing. Tunnelling and Underground Space Technology, Vol. 82, pp. 468–481,
https://doi.org/10.1016/j.tust.2018.08.059.
21. Davey-Wilson I.E.G., 1991. Geotechnical laboratory test simulation using AI techniques. In B.H.V. Topping (ed.), Artificial Intelligence and Civil Engineering. Civil-Comp Press, Edinburgh, UK, pp. 119–124.
22. Davey-Wilson I.E.G., Mistry K., 1995. An intelligent database to predict geotechnical parameters. In B.H.V. Topping (ed.),
Developments in artificial intelligence for civil and structural engineering. Civil-Comp Press, Edinburgh, UK, pp. 105–111.
23. Ellis G.W., Yao C., Zhao R., Penumadu D., 1995. Stress-strain modeling of sands using artificial neural networks. Journal of Geotechnical Engineering, Vol. 121, No. 5, pp. 429–435.
24. Erzin Y., Cetin T., 2012. The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces. Scientia Iranica. Transactions A: Civil Engineering, Vol. 19, No. 2, pp. 188–194.
25. Gao M.Y., Zhang N., Shen S.L., Zhou A., 2020. Real-time dynamic earth-pressure regulation model for shield tunneling by integrating GRU deep learning method with GA optimization. IEEE Access, Vol. 8, pp. 64310–64323, https://doi.org/10.1109/ACCESS.2020.2984515.
26. Gillette D.R., 1991. An expert system for estimating soil strength parameters. Proceedings of the Geotechnical engineering Congress, geotechnical special publication, No. 27, Boulder, Colorado, CO, USA, 1991, pp. 276–287.
27. Goh A.T.C., 1995. Modeling soil correlations using neural networks. Journal of Computing in Civil Engineering, Vol. 9, No. 4,
pp. 275–278.
28. Gomes Correia A., Cortez P., Tinoco J., Marques R., 2013. Artificial intelligence applications in transportation geotechnics. Geotechnical and Geological Engineering, Vol. 31, pp. 861–879, https://doi.org/10.1007/s10706-012-9585-3.
29. Javadi A., Rezania M., 2009. Applications of artificial intelligence and data mining techniques in soil modeling. Geomechanics and Engineering, Vol. 1, No. 1, pp. 53–74, https://doi.org/10.12989/gae.2009.1.1.053.
30. Jin Y.F., Yin Z.Y., Zhou W.H., Horpibulsuk S., 2019. Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method. Acta Geotechnica, Vol. 14, No. 6, pp. 1925–1947, https://doi.org/10.1007/s11440-019-00847-1.
31. Jin Y.F., Yin Z.Y., Zhou W.H., Huang H.W., 2019. Multi-objective optimization-based updating of predictions during excavation. Engineering Applications of Artificial Intelligence, Vol. 78, pp. 102–123, https://doi.org/10.1016/j.engappai.2018.11.002.
32. Jin Y.F., Yin Z.Y., Zhou W.H., Yin J.H., Shao J.F., 2019. A single-objective EPR based model for creep index of soft clays considering L2 regularization. Engineering Geology, Vol. 248, pp. 242–255, https://doi.org/10.1016/j.enggeo.2018.12.006.
33. Juang C.H., Lee D.H., 1989. Development of an expert system for rock mass classification. Civil Engineering Systems, Vol. 6, No. 4, pp. 147–156.
34. Lawal A.I., Kwon S., 2021. Application of artificial intelligence to rock mechanics: an overview. Journal of Rock Mechanics and
Geotechnical Engineering, https://doi.org/10.1016/j.jrmge.2020.05.010. (in press)
35. Maher M.H., Williams T.P., 1991. A hybrid expert system for design with geosynthetics. Proceedings of the Geotechnical engineering Congress, geotechnical special publication, No. 27, Boulder, Colorado, CO, USA, 1991, pp. 241–252.
36. Makantasis K., Protopapadakis E., Doulamis A., Doulamis N., Loupos C., 2015. Deep convolutional neural networks for efficient vision based tunnel inspection. Proceedings of the International Conference on intelligent computer communication and processing, Cluj-Napoca, Romania, 2015, pp. 335–342, https://doi.org/10.1109/ICCP.2015.7312681.
37. Man H., Furukawa T., 2011. Neural network constitutive modelling for non-linear characterization of anisotropic materials. International Journal for Numerical Methods in Engineering, Vol. 85, No. 8, pp. 939–957, https://doi.org/10.1002/nme.2999.
38. Mannsbart G., Resl S., 1993. An expert-system for the design of geotextiles. Geotextiles and Geomembranes, Vol. 12, No. 5,
pp. 441–450.
39. Millar D.L., Calderbank P.A., 1995. On the investigation of a multilayer feedforward neural-network model of rock deformability behavior. Proceedings of the 8th International Congress on rock mechanics, Tokyo, Japan, 1995, pp. 933–938.
40. Miranda T.F.S., 2007. Geomechanical parameters evaluation in underground structures: artificial intelligence, bayesian probabilities and inverse methods. PhD Thesis, University of Minho, Guimarães, Portugal.
41. Nawari N.O., Liang R., Nusairat J., 1999. Artificial intelligence techniques for the design and analysis of deep foundations. Electronic Journal of Geotechnical Engineering, No. 4, pp. 1–21.
42. Negroponte N., 1995. Being Digital. URL: http://inance.ru/2017/09/cifrovaya-ekonomika (accessed: 5 February 2021).
43. Parikh S.A., Kameswara Rao N.S.V., 1991. An expert system for civil engineering application. Proceedings of the Geotechnical
engineering Congress, geotechnical special publication, No. 27, Boulder, Colorado, CO, USA, 1991, pp. 413–421.
44. Pirnia P., Duhaime F., Manashti J., 2018. Machine learning algorithms for applications in geotechnical engineering. Proceedings of the 71st Canadian Geotechnical Conference and the 13th joint CGS/IAH-CNC groundwater Conference, Edmonton, Canada, 2018, pp. 1–7.
45. Rashidian V., Hassanlourad M., 2013. Application of an artificial neural network for modeling the mechanical behavior of carbonate soils. International Journal of Geomechanics, Vol. 14, No. 1, pp. 142–150, https://doi.org/10.1061/(ASCE)GM.1943-5622.0000299.
46. Roy D.H., Singh T.N., 2019. Predicting deformational properties of Indian coal: soft computing and regression analysis approach. Measurement, Vol. 149, Paper 106975, https://doi.org/10.1016/j.measurement.2019.106975.
47. Santamarina J.C., Chameau J.L., 1987. Expert systems for geotechnical engineers. Journal of Computing in Civil Engineering, Vol. 1, No. 4, pp. 241–252.
48. Shahin M.A., 2013. Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions.
In X.S. Yang (ed.), Metaheuristics in water, geotechnical and transport engineering. Elsevier, the Netherland, pp. 169–204,
https://doi.org/10.1016/B978-0-12-398296-4.00008-8.
49. Shahin M.A., 2016. State-of-the-art review of some artificial intelligence applications in pile foundations. Geoscience Frontiers, Vol. 7, No. 1, pp. 33–44, https://doi.org/10.1016/j.gsf.2014.10.002.
50. Siller J.T., 1987. Expert systems in geotechnical engineering. In M.L. Maher (ed.), Expert systems for civil engineers: technology and applications. Publishing house of the ASCE, New York, NY, USA, pp. 77–84.
51. Smith I.G.N., Oliphant J., 1991. The use of a knowledge-based system for civil engineering site investigations. In B.H.V. Topping (ed.), Artificial Intelligence and Civil Engineering. Civil-Comp Press, Edinburgh, UK, pp. 105–112.
52. Toll D., 1996. Artificial intelligence applications in geotechnical engineering. Electronic Journal of Geotechnical Engineering, No. 1,
рр. 1–3.
53. Turk G., Logar J., Majes B., 2001. Modelling soil behavior in uniaxial strain conditions by neural networks. Advances in Engineering Software, Vol. 32, No. 10–11, pp. 805–812, https://doi.org/10.1016/S0965-9978(01)00032-1.
54. Wang K., Sun W.C., 2018. A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning. Computer Methods in Applied Mechanics and Engineering, Vol. 334, pp. 337–380, https://doi.org/10.1016/j.cma.2018.01.036.
55. Wang K., Sun W.C., Du Q., 2019. A cooperative game for automated learning of elastoplasticity knowledge graphs and models with AI-guided experimentation. Computational Mechanics, Vol. 64, No. 2, pp. 467–499, https://doi.org/10.1007/s00466-019-01723-1.
56. Wang S., Xiao S., Jia Z., 1994. An engineering geology expert system of shearing zone. Proceedings of the 7th International Congress Association Engineering Geology, Lisboa, Portugal, 1994, pp. 4489–4494.
57. Wislocki A.P., Bentley S.P., 1991. An expert system for landslide hazard and risk assessment. Computers and Structures, Vol. 40,
No. 1, pp. 169–172.
58. Yang B.B., Yin K.L., Lacasse S., Liu Z., 2019. Time series analysis and long short-term memory neural network to predict landslide displacement. Landslides, Vol. 16, No. 4, pp. 677–694, https://doi.org/10.1007/s10346-018-01127-x.
59. Yin Z.Y., Jin Y.F., 2020. Practice of artificial intelligence in geotechnical engineering. Journal of Zhejiang University-SCIENCE A
(Applied Physics and Engineering), Vol. 21, Issue 6, pp. 407–411.
60. Yin Z.Y., Jin Y.F., Shen J.S., Hicher P.Y., 2018. Optimization techniques for identifying soil parameters in geotechnical engineering: comparative study and enhancement. International Journal for Numerical and Analytical Methods in Geomechanics, Vol. 42, No. 1,
pp. 70–94, https://doi.org/10.1002/nag.2714.
61. Zhang P., Yin Z.Y., Jin Y.F., Chan T.H.T., 2020. A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest. Engineering Geology, Vol. 265, Paper 105328, https://doi.org/10.1016/j.enggeo.2019.105328.
62. Zhang Q., Song J.R., Nie X.Y., 1991. Application of neural network models to rock mechanics and rock engineering. International Journal of Rock Mechanics and Mining Sciences, Vol. 28, No. 6, pp. 535–540.
VLADIMIR A. KOROLEV
Lomonosov Moscow State University; Moscow, Russia; va-korolev@bk.ru
Address: Bld. 1, Leninskie Gory, 119991, Moscow, Russia
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