Intelligent Empowerment of Geotechnical Engineering: Big Data, AI and Optimization

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geomechanics".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1670

Special Issue Editors

School of Highway, Chang'an University, Xi’an 710064, China
Interests: intelligent monitoring and real-time early warning; geotechnical analysis driven by artificial intelligence and big data; BIM and digital twin technology; intelligent reconnaissance and remote sensing technology; machine learning; intelligent algorithm optimization
Special Issues, Collections and Topics in MDPI journals
School of Civil Engineering and Transportation, Guangzhou University, Guangzhou 510006, China
Interests: underground structural engineering; soil dynamics; machine learning; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China
Interests: energy underground engineering

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Guest Editor
Department of Civil Engineering, Shanghai University, Shanghai 200444, China
Interests: rock mechanics; rock engineering; tunnel engineering; geotechnical engineering; practical application of AI technology

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the application of big data analysis technology for processing large-scale geotechnical engineering datasets, establishing intelligent monitoring and early warning systems to facilitate real-time safety management and control in engineering projects. It also investigates the utilization of artificial intelligence, deep learning, and intelligent optimization algorithms in geotechnical parameter inversion, disaster prediction, and engineering design. Additionally, this Special Issue explores the innovative application of intelligent reconnaissance and remote sensing technologies, such as drones, LiDAR, and InSAR, to enhance the precision and efficiency of geological exploration. By integrating these cutting-edge technologies, the Special Issue aims to provide theoretical frameworks, technical case studies, and future directions for both academic and engineering communities, addressing the gaps in systematic, practical, and interdisciplinary integration within existing research, thereby advancing the collaborative development of informatization and intelligence in geotechnical engineering. In particular, the topics of interest include, but are not limited to, the following aspects:

  • Big data analysis and mining technology of geotechnical engineering;
  • Research on intelligent monitoring systems and real-time early warning methods;
  • Application of artificial intelligence and machine learning in geotechnical engineering;
  • Application of deep learning algorithms in the identification and prediction of geotechnical parameters;
  • Application of intelligent optimization algorithms in geotechnical engineering design;
  • Intelligent reconnaissance technology and new methods of remote sensing monitoring;
  • Multi-source data fusion and 3D geological modeling technology;
  • Research on intelligent standard systems and the specification of geotechnical engineering.

Dr. Jiangbo Xu
Dr. Yi Shan
Prof. Dr. Yu Cong
Dr. Shuaifeng Wang
Guest Editors

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Publisher’s Notice

The Special Issue has been shifted from Section Geoheritage, Geoparks and Geotourism to Section Geomechanics on 23 October 2025. At the time of the move, there were no publications in this Special Issue.

Keywords

  • geotechnical information and intelligence
  • big data analysis
  • intelligent monitoring and early warning
  • artificial intelligence
  • machine learning
  • deep learning
  • intelligent algorithms
  • intelligent reconnaissance
  • remote sensing technology
  • digital twin
  • three-dimensional geological modeling
  • data fusion
  • parameter inversion
  • disaster prediction

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Published Papers (2 papers)

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Research

30 pages, 10149 KB  
Article
Integrating Multidimensional 3D Spatial Analysis for Quantitative Geological Environment Evaluation in Urban Underground Space Planning
by Fanfan Dou, Yan Zou, Huaixue Xing, Hongjie Ma, Chaojie Zhen, Shiying Yang, Yong Hu and Haijie Yang
Geosciences 2026, 16(4), 157; https://doi.org/10.3390/geosciences16040157 - 13 Apr 2026
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Abstract
Geological environment evaluation for urban underground space (UGEE) is a critical foundation for optimizing the utilization of urban underground space (UUS) and mitigating exploitation risks. With recent advancements in 3D geological modeling technology, 3D UGEE has emerged as a transformative approach, offering innovative [...] Read more.
Geological environment evaluation for urban underground space (UGEE) is a critical foundation for optimizing the utilization of urban underground space (UUS) and mitigating exploitation risks. With recent advancements in 3D geological modeling technology, 3D UGEE has emerged as a transformative approach, offering innovative perspectives and technical solutions for rational 3D spatial development and geological risk reduction in subsurface engineering. A core component of the 3D UGEE workflow is the integration of diverse 3D spatial analysis methods, which enable comprehensive extraction of evaluation indices from multidimensional datasets—forming the essential basis for scientifically informed development planning. Focusing on quantitative 3D UGEE, this study systematically investigates the implementation of 3D spatial analysis methods across four key stages: (1) geological condition analysis, (2) evaluation information extraction, (3) 3D comprehensive evaluation, and (4) result analysis. Specifically, five core methodologies are highlighted: (1) 3D spatial statistical analysis, (2) 3D mathematical morphological analysis, (3) 3D surface morphology analysis, (4) 3D spatial distance field analysis, and (5) 3D spatial interpolation analysis. To improve the reliability and objectivity of 3D comprehensive evaluation results, we integrate game theory-based combination weighting with an improved TOPSIS model, which balances the subjectivity of expert judgment and the objectivity of data characteristics while adapting to the 3D block unit data structure, effectively avoiding the bias of single weighting or evaluation models. To validate these techniques, a case study in Hangzhou, Zhejiang Province, is conducted, demonstrating their practical effectiveness in evaluating UUS resources. The findings underscore that advanced 3D spatial analysis methods significantly enhance decision-making precision in UUS planning and risk management, providing a replicable framework for sustainable subsurface development. Full article
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22 pages, 9731 KB  
Article
Effects of Deviatoric Stress on Macro- and Meso-Mechanical Behavior of Granite for Water-Sealed Caverns Under True Triaxial Loading
by Liliang Han, Yu Cong, Xiaoshan Wang, Wenyang Du, Lixia Zhang, Jian Gao, Yuming Wang and Zhanchao Zhang
Geosciences 2026, 16(2), 66; https://doi.org/10.3390/geosciences16020066 - 3 Feb 2026
Viewed by 566
Abstract
Based on true triaxial loading experiments and particle flow numerical simulations (PFC3D), this study systematically analyzes the mechanical behavior and failure mechanisms of granite under the influence of stress difference (deviatoric stress). The experimental results indicate that increasing deviatoric stress reduces peak strength, [...] Read more.
Based on true triaxial loading experiments and particle flow numerical simulations (PFC3D), this study systematically analyzes the mechanical behavior and failure mechanisms of granite under the influence of stress difference (deviatoric stress). The experimental results indicate that increasing deviatoric stress reduces peak strength, axial strain, and lateral strain, promoting rock failure with less deformation and dilatancy. An energy analysis reveals that higher deviatoric stress suppresses peak energy accumulation, with a greater proportion of energy being dissipated through crack initiation and propagation. Macroscopic observations show that failure surfaces develop combined tensile-shear cracks, evolving into distinct “V” shapes as deviatoric stresses increase. Numerical simulations demonstrate that intermediate principal stress plays a dual role, initially facilitating, then inhibiting, and finally promoting rock failure with its continuous increase. Microscopically, tensile cracks dominate during pre-peak stages, while rapid crack coalescence in the post-peak stage leads to the formation of throughgoing V-shaped failure zones. Particle displacement analysis reveals that deformation concentrates along the minimum principal stress direction, with the displacement vectors ultimately forming a V-shaped boundary that delineates the failure zone. The research provides comprehensive insights into the macro-meso failure characteristics of hard rock under true triaxial conditions, offering valuable guidance for stability prediction and control in underground rock engineering projects such as water-sealed storage caverns. Full article
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