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This paper reviews the emergence of intelligent rock mechanics at the intersection of AI and classical rock mechanics. It surveys methods and applications from early neural networks and SVMs to modern deep learning, generative models, and especially physics-informed neural networks (PINNs) and related hybrids. Section 2 covers data-driven estimation of mechanical properties (UCS, elastic moduli, velocities) using ANNs, ANFIS, SVMs, ensembles, Bayesian models, and physics-aware approaches. Section 3 reviews image-based 3D microstructure reconstruction (from simulated annealing to VAEs/GANs) and fracture detection across 2D images and 3D point clouds, including transfer learning and hybrid deterministic–stochastic methods (DSIM). Section 4 surveys AI-assisted constitutive modeling (BP/LSTM/TCN hybrids, ensemble/probabilistic regressors) and physics-informed PDE solvers (PINN variants, PIRBN, PI-TCN, poromechanics PINNs, and MFGNNs) with integration into FEM/DEM settings. Section 6 synthesizes applications in rock mass classification, rockburst/geohazard prediction, tunneling/boring operations, slope stability, and other emerging use cases, highlighting ensembles, time-series models, and physics-aware learners. Section 7 outlines challenges (data scarcity, generalization, interpretability, computational efficiency, workflow integration, and maintenance/reproducibility) and future directions (hybrid physics-data models, digital twins, spatial reasoning, and potential LLM utilities).
Cross‑Modal Consistency: 32/50
Textual Logical Soundness: 18/30
Visual Aesthetics & Clarity: 10/20
Overall Score: 60/100
Detailed Evaluation (≤500 words):
1. Cross‑Modal Consistency
• Visual ground truth (image‑first):
– Figure 1: Single-pane framework diagram (blue/yellow boxes, arrows). Text boxes: geology, in‑situ stress, behavior/modeling, monitoring, design; very small fonts.
– Figure 2: (a) Left schematic: “domain subdivision using subgraphs,” “message passing.” (b) Right: convergence plots (error vs NDOF), example FEA vs MFGNN fields; tiny legends/axes.
– Figure 3: (a) Geological section with labeled rockburst zones. (b) Bar/line chart of daily microseismic events and S‑value over time (multi‑color). (c) Bar/line chart of daily cumulative energy and incidence (%).
• Major 1: Unreferenced/uncaptioned Figure 3 (a–c) appears; no figure number or mention in Sec. 6.2. Evidence: Three panes labeled “(a)(b)(c)” with no accompanying caption in text.
• Major 2: Title/content mismatch for Figure 1. Caption states “Collaborative development…,” image reads “Artificial intelligence technology framework…”. Evidence: “Figure 1: Collaborative development…” vs embedded banner “Artificial intelligence technology framework…”.
• Minor 1: Figure 2 appears to have two sub‑panes but the caption and text do not identify (a)/(b). Evidence: “Figure 2: Machine learning methods for solving FEM problems(Black & Najafi, 2022)”.
• Minor 2: Figure 1 is first cited in Intro but placed far from first mention. Evidence: Figure is separated by multiple paragraphs/sections.
2. Text Logic
• Major 1: Section 5 (“MODEL GENERALIZATION IN ROCK MECHANICS AI”) is empty, breaking the promised narrative. Evidence: Section heading present without content between Sec. 4 and Sec. 6.
• Minor 1: Broken line/citation in Intro around “Li et al., [newline] 2025”. Evidence: “(Li et al., [line break] 2025)”.
• Minor 2: Occasional typographic inconsistencies in captions (missing space before parenthesis). Evidence: “problems(Black & Najafi, 2022)”.
3. Figure Quality
• Major 1: Illegible at print size. Critical labels in Figure 1 (module names, arrows) cannot be read. Evidence: Framework boxes rendered with tiny fonts at ≈270×510 px.
• Major 2: Figure 3 (a–c) axes, legends, and values are too small to read, blocking comprehension. Evidence: Low‑resolution panels with dense annotations.
• Minor 1: Figure 2’s axes/legends are barely legible; sub‑pane labels absent. Evidence: Small tick labels in convergence plot and field maps.
Key strengths:
Key weaknesses:
Actionable recommendations:
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This paper provides a comprehensive review of the integration of artificial intelligence (AI) and machine learning (ML) techniques into the field of rock mechanics. It chronologically outlines the evolution of AI methodologies, starting from early methods like backpropagation and support vector machines to more recent advancements such as deep learning frameworks, including convolutional neural networks and transformer architectures. The authors highlight the growing integration of physics-informed neural networks (PINNs) and graph-based learning, which aim to bridge data-driven inference with physical interpretability. A significant portion of the paper is dedicated to showcasing the diverse applications of these AI techniques in rock engineering, including data-driven estimation of rock properties, image-based modeling and fracture detection, AI-assisted constitutive modeling, and simulation. The paper also touches upon the challenges and future directions in the field, emphasizing the need for standardized datasets, interdisciplinary collaboration, and the development of transparent and reproducible AI workflows. While the paper provides a valuable overview of the current state of AI in rock mechanics, it falls short in critically evaluating the limitations of these methods and their practical implementation in real-world scenarios. The paper's focus on listing applications without a deeper analysis of their impact and the challenges they address makes it read more like a catalog than a critical review. Additionally, the paper lacks a thorough discussion on the practical aspects of implementing AI in rock engineering, such as the specific steps involved in data collection, model training, and validation in real-world projects. The absence of a detailed comparison with traditional methods and the limited discussion on the limitations of AI in this field further weaken the paper's overall contribution. Despite these limitations, the paper serves as a useful starting point for researchers interested in the intersection of AI and rock mechanics, providing a broad overview of the field and highlighting the potential of AI in advancing rock engineering.
One of the paper's notable strengths is its comprehensive and chronological overview of the evolution of AI methodologies in rock mechanics. The authors effectively trace the development of AI techniques from early methods like backpropagation and support vector machines to more recent advancements such as deep learning frameworks, including convolutional neural networks and transformer architectures. This historical perspective is valuable for understanding the progression of AI applications in the field. The paper also provides a detailed account of the diverse applications of AI in rock engineering, covering areas such as data-driven estimation of rock properties, image-based modeling and fracture detection, AI-assisted constitutive modeling, and simulation. The authors present a wide range of studies, which demonstrates the breadth of AI's impact on rock mechanics. The inclusion of recent advancements like physics-informed neural networks (PINNs) and graph-based learning is particularly commendable, as it highlights the growing trend towards integrating physical interpretability with data-driven approaches. The paper's emphasis on the potential of AI to transform rock mechanics from an empirical discipline to a data-driven, intelligence-enabled science is a strong and forward-looking statement. The authors also acknowledge the importance of interdisciplinary collaboration and the need for standardized datasets, which are crucial for the advancement of the field. Overall, the paper's strengths lie in its broad coverage of AI applications in rock mechanics and its optimistic outlook on the future of the field.
Despite its comprehensive overview, the paper has several significant weaknesses that undermine its overall contribution. Firstly, the paper's structure and writing style are more akin to a catalog of applications and methods rather than a critical review. The 'INTRODUCTION' section, for instance, reads more like a chronological listing of AI advancements and their applications in rock mechanics, lacking a clear articulation of the paper's scope, objectives, and key contributions. This makes it difficult for readers to grasp the core message and the paper's unique value. The 'REVIEW' section follows a similar pattern, systematically listing various AI methods and their applications without providing a critical analysis of the limitations or challenges associated with each method. For example, while the paper mentions the use of convolutional neural networks (CNNs) for microseismic event localization, it does not delve into the specific challenges of applying CNNs to this task, such as the need for large, labeled datasets and the difficulty in interpreting the results in a physically meaningful way. This lack of critical evaluation is a recurring issue throughout the paper, making it less insightful and more of a descriptive overview. Secondly, the paper's claim of being a 'comprehensive review' is overstated. The paper does not provide a detailed discussion of the practical aspects of implementing AI in rock engineering, such as the specific steps involved in data collection, model training, and validation in real-world projects. The 'CHALLENGES AND FUTURE DISCUSSIONS' section mentions data limitations, model interpretability, computational efficiency, integration with domain workflows, and maintenance of AI systems, but it does not offer concrete solutions or best practices for addressing these challenges. This omission is particularly problematic, as practical implementation is a crucial aspect of the field's development. Thirdly, the paper's discussion of the limitations of AI in rock mechanics is insufficient. While the authors acknowledge issues like data scarcity and model interpretability, they do not provide a balanced perspective on the potential drawbacks and challenges of using AI in this field. For instance, the paper does not address the 'black box' nature of many AI models, the risk of overfitting, or the ethical considerations associated with AI-driven decision-making in rock engineering. The absence of a dedicated section or a more thorough discussion on these limitations weakens the paper's credibility and makes it less useful for researchers and practitioners. Lastly, the paper's contribution is limited by its lack of novel insights or a unique perspective. The paper primarily summarizes existing research without offering new methodologies, frameworks, or critical evaluations that advance the field. The 'CONCLUSION' section reiterates the potential of intelligent rock mechanics but does not provide a compelling argument for the paper's originality or its impact on the broader scientific community. These weaknesses collectively suggest that the paper, while informative, falls short of being a comprehensive and critical review of AI in rock mechanics. The lack of critical analysis, practical guidance, and a balanced discussion of limitations are particularly concerning and need to be addressed to enhance the paper's value.
To enhance the paper's contribution and address its limitations, several concrete and actionable improvements are recommended. Firstly, the paper should adopt a more critical and analytical approach, moving beyond a simple cataloging of methods and applications. Each section should not only describe the AI techniques used but also provide a detailed analysis of their limitations, challenges, and the specific contexts in which they are most effective. For example, when discussing the use of convolutional neural networks (CNNs) for microseismic event localization, the paper should delve into the specific challenges of applying CNNs to this task, such as the need for large, labeled datasets, the difficulty in interpreting the results in a physically meaningful way, and the potential for overfitting. Similarly, the discussion of physics-informed neural networks (PINNs) should include an analysis of the challenges in formulating the governing equations and the computational cost associated with training these models. This would provide a more nuanced and insightful perspective on the current state of AI in rock mechanics. Secondly, the paper should include a dedicated section on the practical aspects of implementing AI in rock engineering. This section should provide concrete guidance on data collection, model training, and validation in real-world projects. The authors should discuss the specific steps involved in preparing data for AI models, the computational resources required for training, and the methods for validating the models in practical scenarios. For instance, the paper could explore the use of transfer learning to address data scarcity, discuss the importance of feature engineering for improving model interpretability, and provide case studies that demonstrate the successful implementation of AI in rock engineering projects. This would make the paper more useful for practitioners and researchers looking to apply AI in their work. Thirdly, the paper should provide a more balanced discussion of the limitations of AI in rock mechanics. While the paper acknowledges issues like data scarcity and model interpretability, it should also address the 'black box' nature of many AI models, the risk of overfitting, and the ethical considerations associated with AI-driven decision-making in rock engineering. The authors should discuss the potential for AI models to perpetuate biases present in the training data and the need for transparency and accountability in AI-driven rock engineering solutions. This would provide a more comprehensive and realistic view of the current state of AI in rock mechanics and help to guide future research in this area. Finally, the paper should clearly articulate its unique contribution and avoid making overly broad claims. Instead of aiming to be a comprehensive review of all AI applications in rock mechanics, the paper could focus on a specific theme or challenge within the field, such as the use of AI for real-time monitoring and prediction, or the integration of AI with physics-based models. This would allow the paper to provide a more in-depth analysis of a specific area and make a more significant contribution to the field. The paper should also include a forward-looking perspective, discussing the potential impact of AI on the future of rock mechanics and identifying key areas for future research. This would help to position the paper as a valuable contribution to the field and provide a roadmap for future research in this area.
1. What specific criteria were used to select the studies and applications included in the review? How did the authors ensure that the selected studies represent the most significant and impactful work in the field of AI in rock mechanics?
2. Could the authors provide a more detailed discussion on the practical challenges of implementing AI models in real-world rock engineering projects? For example, what are the specific steps involved in data collection, model training, and validation, and how can these steps be optimized for practical use?
3. How do the AI models discussed in the paper handle uncertainty and variability in geological data? What methods are used to quantify and mitigate these issues, and what are the limitations of these methods?
4. What are the ethical considerations associated with the use of AI in rock engineering, particularly in high-consequence decision-making scenarios? How can these ethical concerns be addressed to ensure responsible and transparent AI-driven solutions?
5. Could the authors elaborate on the integration of AI models with existing rock mechanics software and workflows? What are the current limitations and challenges in this integration, and what steps can be taken to improve it?
6. How do the AI models discussed in the paper compare to traditional methods in terms of accuracy, efficiency, and interpretability? What are the specific scenarios where AI models outperform traditional methods, and where do they fall short?
7. What are the key areas for future research in the field of AI in rock mechanics? How can the limitations of current AI models be addressed, and what new methodologies or frameworks are needed to advance the field?