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

Авторы
Шейл Б.Инженерный факультет Кембриджского университета, г. Кембридж, Великобритания
Анагностопулос К.Факультет (школа) компьютерных наук Университета Глазго, г. Глазго, Великобритания
Бакли Р.Факультет (школа) инженерных наук имени Джеймса Ватта Университета Глазго, г. Глазго, Великобритания
Чиантиа М.О.Факультет (школа) естественных и инженерных наук Университета Данди, г. Данди, Великобритания; факультет наук о Земле и окружающей среде Университета Милана-Бикокка, г. Милан, Италия
Фебрианто Э.Факультет (школа) инженерных наук имени Джеймса Ватта Университета Глазго, г. Глазго, Великобритания
Фу Ц.Факультет (школа) инженерных наук и материаловедения Лондонского университета имени Королевы Марии, г. Лондон, Великобритания
Гао Ч.Факультет (школа) инженерных наук имени Джеймса Ватта Университета Глазго, г. Глазго, Великобритания
Гэн С.Инженерный факультет Университета Уорика, г. Ковентри, Великобритания
Гун Б.Колледж инженерных, дизайнерских и естественных наук при Лондонском университете имени Брунеля, г. Лондон, Великобритания
Хэнли К.Бакалавриат по химическим технологиям Эдинбургского университета, г. Эдинбург, Великобритания
Хэ П.Факультет (школа) естественных и инженерных наук Университета Данди, г. Данди, Великобритания
Коломватсос К.Факультет инженерных и компьютерных наук Университета Фессалии, г. Волоc, Греция
Лопес Б.К.Ф.Л.Факультет гражданского и экологического строительства Университета Стратклайда, г. Глазго, Великобритания
Нинич Й.Инженерный факультет (школа) Бирмингемского университета, г. Бирмингем, Великобритания
Превитали М.Факультет (школа) естественных и инженерных наук Университета Данди, г. Данди, Великобритания
Резания М.Инженерный факультет (школа) Университета Уорика, г. Ковентри, Великобритания
Руис-Лопес А.Компания Seequent («Сиквент») – дочерняя компания корпорации Bentley Systems по подземным технологиям, г. Крайстчерч, Новая Зеландия; инженерный факультет Лондонского Имперского колледжа, г. Лондон, Великобритания
Сунь Ц.Факультет (школа) инженерных наук имени Джеймса Ватта Университета Глазго, г. Глазго, Великобритания
Сурьясентана С.Факультет гражданского и экологического строительства Университета Стратклайда, г. Глазго, Великобритания
Таборда Д.Инженерный факультет Лондонского имперского колледжа, г. Лондон, Великобритания
Утили С.Инженерный факультет (школа) Университета Ньюкасла, г. Ньюкасл-апон-Тайн, Великобритания
Вайт С.Компания Geowynd («Геовинд»), г. Лондон, Великобритания
Чжан П.Факультет гражданского и экологического строительства Сингапурского национального университета, Сингапур

Abstract: 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.
 

Keywords: 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.

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

UDC: 004; 624.131

 
For citation: 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.).
 

Funding: No information
 

Authors:
 

Sheil B.
Department of Engineering, University of Cambridge, Cambridge, UK
 

Anagnostopoulos C.
School of Computing Science, University of Glasgow, Glasgow, UK
 

Buckley R.
James Watt School of Engineering, University of Glasgow, Glasgow, UK
 

Ciantia M.O.
School of Science and Engineering, University of Dundee, Dundee, Scotland, UK; Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
 

Febrianto E.
James Watt School of Engineering, University of Glasgow, Glasgow, UK
 

Fu J.
School of Engineering and Materials Science, Queen Mary University of London, London, UK
 

Gao Z.
James Watt School of Engineering, University of Glasgow, Glasgow, UK
 

Geng X.
School of Engineering, University of Warwick, Coventry, UK
 

Gong B.
College of Engineering, Design and Physical Sciences, Brunel University of London, London, UK
 

Hanley K.
Chemical Engineering (BEng Hons) programme, University of Edinburgh, Edinburgh, UK
 

He P.
School of Science and Engineering, University of Dundee, Dundee, UK
 

Kolomvatsos K.
Engineering and Computer Science, University of Thessaly, Volos, Greece
 

Lopes B.C.F.L.
Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, UK
 

Ninic J.
School of Engineering, University of Birmingham, Birmingham, UK
 

Previtali M.
School of Science and Engineering, University of Dundee, Dundee, UK
 

Rezania M.
School of Engineering, University of Warwick, Coventry, UK
 

Ruiz-Lopez A.
Sequent (The Bentley Subsurface Company), Christchurch, New Zealand; Faculty of Engineering, Imperial College London, London, UK
 

Sun J.
James Watt School of Engineering, University of Glasgow, Glasgow, UK
 

Suryasentana S.
Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, UK
 

Taborda D.
Faculty of Engineering, Imperial College London, London, UK
 

Utili S.
School of Engineering, Newcastle University, Newcastle upon Tyne, UK
 

Whyte S.
Geowynd company, London, UK
 

Zhang P.
Department of Civil and Environmental Engineering, National University of Singapore, Singapore

 

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