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Geosciences
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New Trends in Numerical Methods in Rock Mechanics
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New Trends in Numerical Methods in Rock Mechanics

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

Deadline for manuscript submissions: 30 October 2026 | Viewed by 2814

Special Issue Editors

Prof. Dr. Xiangchao Shi

E-Mail Website
Guest Editor
State Key Lab of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610065, China
Interests: intelligent rock mechanics; smart drill string dynamics; AI for oil and gas; reservoir geomechanics
Dr. Zetian Zhang

E-Mail Website
Guest Editor
College of Water Resources and Hydropower Engineering, Sichuan University, Chengdu, China
Interests: deep rock mechanics and engineering; deep rock mass seepage mechanics; experimental rock mechanics; deep in situ observations and coupled information interpretation
Dr. Daobing Wang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Petroleum University of Chemical Engineering, Beijing, China
Interests: hydraulic fracturing mechanisms of geothermal reservoirs; shale gas; tight oil and gas reservoirs; rock mechanics; efficient finite element numerical computation methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The focus of this Special Issue, “New Trends in Numerical Methods in Rock Mechanics”, is to highlight the latest advancements in numerical approaches for rock mechanics applications and in computational techniques, including modeling, simulation, and optimization methods. It seeks to examine their application to various real-world problems in geotechnical engineering, petroleum reservoirs, and mining. We welcome contributions that discuss innovative methods for improving the accuracy, efficiency, and applicability of numerical simulations in rock mechanics. This Special Issue aims to promote both theoretical advancements and practical implementations of numerical methods, particularly emphasizing the integration of new technologies, such as machine learning and AI, in rock mechanics research. Researchers are invited to submit original papers that explore these evolving areas, advancing both the understanding of and practical tools available for rock mechanics professionals. By featuring innovative research, this Special Issue hopes to foster the development of new insights and methodologies in this field.

Prof. Dr. Xiangchao Shi
Dr. Zetian Zhang
Dr. Daobing Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • numerical methods
  • rock mechanics
  • computational techniques
  • geotechnical engineering
  • petroleum reservoirs
  • simulation and modeling
  • artificial intelligence

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Published Papers (3 papers)

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Research

22 pages, 5107 KB  
Article
Adaptive Filtering Method for Low-SNR Rock Mass Fracture Microseismic Signals in Deep-Buried Tunnels Considering Noise Intrusion Characteristics
by Tao Lin, Weiwei Tao, Yakang Xu and Wenjing Niu
Geosciences 2026, 16(4), 143; https://doi.org/10.3390/geosciences16040143 - 1 Apr 2026
Viewed by 411
Abstract
Aiming at the problems of microseismic signals from rock mass fracture in deep-buried tunnels with low signal-to-noise ratio (SNR) suffering from coupled interference of multi-source noise, and traditional filtering methods having fixed parameters and poor processing effects on spectral aliasing, this study proposes [...] Read more.
Aiming at the problems of microseismic signals from rock mass fracture in deep-buried tunnels with low signal-to-noise ratio (SNR) suffering from coupled interference of multi-source noise, and traditional filtering methods having fixed parameters and poor processing effects on spectral aliasing, this study proposes a ternary coupled adaptive filtering method integrating the Sparrow Search Algorithm, Variational Mode Decomposition and Wavelet Threshold Denoising (SSA-VMD-DWT). First, the noise intrusion characteristics of low-SNR microseismic signals in deep-buried tunnels were analyzed, and the filtering difficulties of white noise, low-frequency noise, high-frequency noise and non-stationary noise were clarified. Subsequently, a parameter optimization framework with the Sparrow Search Algorithm (SSA) as the core was constructed to optimize the key parameters, including the penalty factor α and modal number K of Variational Mode Decomposition (VMD), as well as the wavelet basis and decomposition layers of Wavelet Threshold Denoising (DWT), respectively. A dual-index threshold decision function based on kurtosis and correlation coefficient, and a wavelet packet entropy weighted reconstruction algorithm were designed to realize the collaborative adaptive adjustment of decomposition depth and threshold rules. Finally, the performance of the algorithm was verified through simulation signal experiments and an engineering case of a deep-buried tunnel in Southwest China. The results show that for the simulated signal with a low SNR of 2 dB, the SNR is increased to 12.43 dB, and the root mean square error is reduced to 2.36 × 10−7 after denoising by this algorithm, which is significantly superior to the Empirical Mode Decomposition (EMD) and traditional DWT methods. In the engineering case, the information entropy of the filtered signal is the lowest among all methods, which can effectively suppress multi-band noise and retain the core characteristics of microseismic signals from rock mass fracture, solving the problems of spectral aliasing, detail loss and empirical parameter setting of traditional methods. This method provides a new technical paradigm for the processing of low-quality microseismic signals in deep tunnel engineering and can improve the accuracy of monitoring and early warning for rock mass dynamic disasters. Full article
(This article belongs to the Special Issue New Trends in Numerical Methods in Rock Mechanics)
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25 pages, 5458 KB  
Article
Neural Network Inversion Algorithm for Geostress Field Based on Physics-Informed Constraints
by Fei Li, Lin Wang, Zhifeng Liang, Jinan Wang, Chuanqi Zhu and Ruiyang Yuan
Geosciences 2026, 16(3), 118; https://doi.org/10.3390/geosciences16030118 - 12 Mar 2026
Cited by 1 | Viewed by 936
Abstract
Traditional methods for geostressfield inversion face issues such as weak physical interpretability and insufficient generalization ability. This study pioneers the application of Physics-Informed Neural Network (PINN) to this problem, developing a data- and physics-driven inversion algorithm. The framework incorporates a constitutive-equation-based regularized loss [...] Read more.
Traditional methods for geostressfield inversion face issues such as weak physical interpretability and insufficient generalization ability. This study pioneers the application of Physics-Informed Neural Network (PINN) to this problem, developing a data- and physics-driven inversion algorithm. The framework incorporates a constitutive-equation-based regularized loss function as a hard constraint during training to ensure physical consistency. To address boundary load uncertainty, two quantification approaches—Bayesian linear regression and surrogate model optimization—are proposed to establish 95% confidence intervals for boundary coefficients. Verification based on simple three-dimensional models and actual geological models of mines shows that PINN inversion achieves a mean absolute relative error as low as 0.0772%, with an error of 15.67% under sparse sampling conditions—significantly lower than the 31.07% error of the traditional Back propagation neural network. This demonstrates excellent robustness and data efficiency. In the practical engineering application of complex geological bodies, the average error of principal stress inversion is 9.35% with a minimum error of 0.137%. All inversion results fall within the permissible accuracy range of engineering, and the stress distribution conforms to basic laws, with an average error of 0.453 in the constitutive relation. Compared with BP neural network and multiple linear regression methods, it shows obvious accuracy advantages. This method provides a new solution for intelligent ground stress prediction with high accuracy, high efficiency, and strong physical interpretability, and also lays the foundation for early identification of geological disasters. Full article
(This article belongs to the Special Issue New Trends in Numerical Methods in Rock Mechanics)
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23 pages, 8736 KB  
Article
Discrete Element Simulation on the Evolution Mechanism of Excavation Damage Zone in Deep-Buried Tunnels Under Confining Pressure and Comprehensive Structural Planes
by Zhina Liu, Yan Qiao, Yuanfeng Suo and Haoyu Diao
Geosciences 2025, 15(12), 443; https://doi.org/10.3390/geosciences15120443 - 21 Nov 2025
Cited by 2 | Viewed by 705
Abstract
The failure mechanism of fractured rock masses under high in situ stress is crucial to the stability of deep underground engineering. This study employs the discrete element method to investigate the evolution of the excavation damage zone (EDZ) in deep-buried tunnels. Numerical models [...] Read more.
The failure mechanism of fractured rock masses under high in situ stress is crucial to the stability of deep underground engineering. This study employs the discrete element method to investigate the evolution of the excavation damage zone (EDZ) in deep-buried tunnels. Numerical models of granite were developed to analyze how confining pressure influences single fractures with varying characteristics and to compare the behavior of filled versus unfilled fractures in double-fracture configurations. The results show the following: (1) confining pressure exerts a dual role, promoting crack initiation and EDZ expansion in intact rock and exposed fractures due to stress concentration while suppressing damage near hidden filled fractures through confinement; (2) EDZ geometry is governed by fracture orientation and filling condition, with filled fractures maintaining stress continuity and raising the crack initiation stress ratio to 0.3–0.4; (3) in multi-fracture setups, unfilled fractures facilitate stress release and crack coalescence, whereas filled fractures act as barriers, diverting cracks and promoting symmetric stress redistribution; and (4) models accurately reproduced failure patterns from real rockburst cases, validating the method for predicting fracture behavior, with filled fractures reducing EDZ area by up to 44%. These findings provide theoretical support for rockburst risk assessment and support design in complex geological conditions. Full article
(This article belongs to the Special Issue New Trends in Numerical Methods in Rock Mechanics)
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