Collection
AI in Geomechanics and Geotechnical Engineering
Geomechanics or geotechnical engineering, which primarily deals with soil, rock, and their interactions with engineering structures such as foundations, retaining walls, and tunnels, has traditionally relied on model-based approaches (e.g., analytical, numerical, physical and empirical modelling) for design and analysis. However, artificial intelligence (AI) and data-based approaches are rapidly transforming the field of geomechanics and geotechnical engineering, offering innovative solutions to complex challenges associated with the behaviour of earth materials. The integration of AI technologies, particularly machine learning (ML) and data analytics is revolutionizing this field by enhancing predictive capabilities, optimizing designs, and improving safety margins. AI's role in geomechanics and geotechnical engineering is not just augmentative but also transformative, offering a paradigm shift from conventional deterministic models to a more robust, data-driven approach that enhances understanding, increases safety, and drives innovation. As this field continues to evolve, the synergy between AI and geotechnical engineering promises to unlock new possibilities in the construction and monitoring of safe, sustainable, and efficient infrastructure.
The guest editors are calling for paper submissions to this Topical Collection to be published in the international journal ‘AI in Civil Engineering’. The objective of the Topical Collection will be to present recent advances and to discuss current and emerging multi-disciplinary approaches in the field of geomechanics and geotechnical Engineering. Consequently, this special issue seeks to present the latest advancements covering a broad range of topics, including but not restricted to: -Machine learning applications in geotechnical site characterization and modelling; -AI-based characterization and modelling of geomaterials; -Intelligent monitoring and assessment of geotechnical infrastructure; -AI-based approaches for risk analysis and mitigation in geotechnical projects; -Optimization and decision support systems utilizing AI techniques in geotechnical design; -Integration of remote sensing data and AI for geohazard detection and prediction; -Sustainable geotechnical engineering solutions empowered by AI-driven methodologies; -Case studies and real-world applications demonstrating the efficacy of AI in geotechnical practice.
Editors
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Xiong (Bill) Yu
Chair, Department of Civil Engineering, Case School of Engineering
Professor, Department of Electrical Engineering and Computer Science, Case School of EngineeringProfessor, Department of Mechanical and Aerospace Engineering, Case School of Engineering
His research focuses on addressing emerging engineering and societal issues associated with civil infrastructure system. The activities are pursued with a strong interdisciplinary perspective that integrate engineering principles with the socioeconomic context.
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Zhen (Leo) Liu
Associate Professor, University of Virginia, USA. His research interests include soil mechanics, foundation engineering, Artificial Intelligence (AI), numerical simulations, and other topics in classical mechanics. His research interests are integrated for being more collaborative (multiphysics), more focused (multiscale), and more intelligent (AI).
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Xingyue Li
Professor Xingyue Li is a distinguished research fellow in the Department of Geotechnical Engineering at Tongji University. She obtained her Ph.D. in Civil Engineering from the Hong Kong University of Science and Technology (HKUST), and was a postdoc at the Swiss Federal Institute of Technology, Lausanne (EPFL). Her research expertise is in the computational modeling of gravitational mass movements and their impacts on mitigation measures, with both continuum- and discrete-based methods including CFD-DEM and MPM.
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Haihua Zhang
Research professor, Tongji University, China. Prof. Zhang's research on On-Site Visualization (OSV) technology represents a significant advancement in ensuring real-time visual early warning during construction activities.
