Special Collection Information

The study of geomechanics and geotechnical engineering holds paramount significance across diverse domains, including construction, resource development, and geological disaster management. It plays a pivotal role in advancing societal well-being and sustainable economic growth. Geotechnical materials, characterized by their frequent heterogeneity and multiphase composition, present formidable challenges due to the intricate interplay of uncertain environmental conditions, nonlinear material behavior, and complex geological structures. Human activities can perturb these materials, leading to engineering disasters that pose formidable challenges for stability assessment and disaster prevention. Despite more than a century of research in geomechanics and geotechnical engineering, traditional empirical and retrospective analysis methods confront considerable difficulties when addressing the intricate "black box" conundrum inherent in engineering geomechanics.

In recent years, the rapid evolution of next-generation digital technologies, encompassing the IoT (Internet of Things), big data analytics, cloud computing, and artificial intelligence, has ushered in a transformative paradigm known as digital twinning. This paradigm integrates innovative numerical simulation techniques to address these challenges. Digital twinning harnesses the potential of IoT-based monitoring devices to procure precise data concerning in-situ conditions, mechanical responses, and geological structures within engineering geomechanics. Subsequently, it employs advanced data analytics and artificial intelligence for real-time inverse analysis and feature extraction, facilitating the continuous spatiotemporal evolution of geomechanical parameters. By amalgamating cloud computing and multi-field continuous-discontinuous modeling, alongside cutting-edge technologies such as deep learning, digital twinning facilitates stability analysis, risk assessment, and the potential elucidation of engineering geomechanical enigmatic facets. Recent strides in monitoring technologies, including InSAR (Interferometric Satellite Aperture Radar), MS (Micro Seismic) monitoring, fiber-optic grating-based deformation tracking, laser scanning, and CT (Computed Tomography) scanning, alongside novel simulation techniques like FDEM (coupled Finite and Discrete Element Method), SPH (Smoothed Particle Hydrodynamics), CA (Cellular Automata), MPM (Material Point Method), PFEM (Particle Finite Element Method), PD (Peri Dynamics), PFM (Phase Filed Model), NMM (Numerical Manifold Method), have paved the way for the emergence of this paradigm. The rapid proliferation of machine learning, data-driven computing, and cloud computing offers fresh avenues for prognosticating rock or soil failures.

We are delighted to announce a special issue in the Journal of Rock Mechanics and Geotechnical Engineering, entitled "Advanced Numerical Approaches and Digital Technologies in Geomechanics and Geotechnical Engineering." This special issue aims to spotlight recent advancements and future prospects in this field. It encompasses research areas such as novel observational methods, applications of machine learning, advanced numerical techniques, understanding failure mechanisms and processes, conducting stability analyses, and assessing risks associated with geotechnical failures. Additionally, we encourage contributions that explore emerging facets of geomechanics and geotechnical engineering, extending to fields such as energy, materials, environmental studies, and even geophysics or planetary science research where it intersects with geotechnical engineering.

Instructions for Authors

Manuscripts can be submitted via the journal website, and please do not forget to select the special issue ANADTGGE when you select the article type as follows:

Guest Editors

Pengzhi Pan

State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China

Email: pzpan@whrsm.ac.cn

Zhen-Yu Yin

The Hong Kong Polytechnic University, Hong Kong, China

Email: zhenyu.yin@polyu.edu.hk

Brain Sheil

University of Cambridge, UK

Email: bbs24@cam.ac.uk

Zhaofeng Wang

Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, China

Email: zfwang@whrsm.ac.cn

Special Collection Timeline

Deadline for manuscript submission: August 31, 2024

Publication of the Special Collection: March 25, 2025