2510.0013 A Review of Intelligent Rock Mechanics: From Methods to Applications v5

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📋 Summary

This paper provides a comprehensive review of the integration of artificial intelligence (AI) and machine learning (ML) within the field of rock mechanics, tracing the evolution of these technologies from traditional methods to modern deep learning frameworks. The authors meticulously examine the application of AI across various aspects of rock engineering, including microstructure reconstruction, mechanical parameter estimation, constitutive modeling, and real-time hazard prediction. The paper highlights the transformative potential of AI in enhancing our understanding and prediction of complex geological behaviors, moving beyond empirical approaches towards more data-driven and physics-informed models. The authors emphasize the growing role of advanced techniques such as physics-informed neural networks (PINNs) and graph-based learning in bridging the gap between data-driven inference and physical interpretability. The paper also acknowledges the challenges that remain, particularly in areas such as data quality, model generalization, and interpretability. The authors conclude with a forward-looking perspective, envisioning the development of intelligent frameworks that integrate physical knowledge, spatial reasoning, and adaptive learning to advance rock mechanics towards fully autonomous systems. The paper's significance lies in its ability to synthesize a wide array of research, providing a valuable resource for researchers and practitioners seeking to understand the current state and future directions of AI in rock mechanics. It effectively highlights the paradigm shift occurring in the field, where AI is increasingly becoming an essential tool for addressing complex challenges and improving the efficiency and safety of rock engineering projects. The paper's strength is in its breadth, covering a wide range of applications and methodologies, and its ability to contextualize the current state of AI in rock mechanics within the broader landscape of both AI and rock engineering. However, the paper's focus on review rather than novel contributions, and its lack of in-depth analysis of specific AI methodologies, limits its overall impact. While it provides a valuable overview, it does not delve deeply into the nuances of applying these techniques to the specific challenges of rock mechanics, nor does it offer concrete solutions to the identified limitations. Despite these limitations, the paper serves as a useful starting point for researchers interested in the field, providing a solid foundation for further investigation and development.

✅ Strengths

This paper's primary strength lies in its comprehensive review of the application of AI and ML in rock mechanics. The authors have successfully synthesized a wide range of research, covering topics from fundamental methodologies to practical engineering applications. This breadth of coverage is particularly valuable for researchers and practitioners new to the field, providing a solid foundation for understanding the current state of AI in rock mechanics. The paper effectively traces the evolution of AI methodologies, from traditional approaches like backpropagation and support vector machines to modern deep learning frameworks such as convolutional and transformer architectures. This historical perspective is crucial for understanding the progress made and the challenges that remain. The authors also highlight the growing role of emerging techniques, such as physics-informed neural networks (PINNs) and graph-based learning, in bridging the gap between data-driven inference and physical interpretability. This emphasis on the integration of physics and data-driven approaches is a key strength, as it addresses a critical need in the field of rock mechanics, where physical principles are paramount. Furthermore, the paper's organization is logical and clear, making it easy for readers to follow the progression of ideas and understand the various applications of AI in rock mechanics. The authors effectively use examples to illustrate the potential of AI in different areas, such as microstructure reconstruction, mechanical parameter estimation, and real-time hazard prediction. The paper also acknowledges the challenges that remain, such as data quality, model generalization, and interpretability, demonstrating a balanced and realistic perspective on the current state of the field. The authors' emphasis on the need for interdisciplinary collaboration is also a strength, as it highlights the importance of integrating expertise from AI, rock mechanics, and other related disciplines to address the complex challenges in the field. Overall, the paper's strength lies in its ability to provide a comprehensive and well-organized overview of the current state of AI in rock mechanics, highlighting both the progress made and the challenges that remain.

❌ Weaknesses

While this paper offers a valuable synthesis of AI applications in rock mechanics, several weaknesses limit its overall impact. Firstly, the paper lacks novel contributions, as it primarily serves as a review of existing research rather than presenting new insights or innovative approaches. The authors themselves state that the paper 'synthesizes recent progress,' confirming its role as a review. While this is valuable, it does not advance the field in a significant way. The paper primarily focuses on describing existing methodologies and their applications, rather than proposing new research directions or solutions to open problems. For instance, the discussion on physics-informed neural networks (PINNs) and graph-based learning, while acknowledging some limitations, lacks a critical analysis of their specific challenges in the context of rock mechanics. The paper mentions the sensitivity of PINNs to network initialization and collocation sampling, but does not delve into the difficulties of defining appropriate loss functions for complex geological systems. Similarly, the discussion of graph-based learning does not address the computational cost of training graph neural networks on large-scale geological datasets. This lack of critical analysis limits the paper's ability to provide a nuanced understanding of the strengths and weaknesses of these methods. My confidence in this assessment is high, as the paper's stated purpose and content clearly indicate its nature as a review, and the lack of critical analysis is evident upon close reading. Secondly, the paper suffers from a lack of depth in its methodological analysis. While the paper reviews various AI methodologies, it does not provide a detailed and in-depth analysis of the specific techniques and algorithms used in rock mechanics. The descriptions of AI methods are often high-level and do not delve into the nuances of their implementation or adaptation for rock mechanics problems. For example, when discussing the use of convolutional neural networks (CNNs) for image-based fracture detection, the paper mentions architectures like FraSegNet and DeepLab V3+ but does not discuss the specific architectural choices, such as the number of layers, filter sizes, or activation functions, and how these choices impact the performance of the model. Similarly, the paper does not provide a detailed comparison of different machine learning algorithms, such as support vector machines (SVMs) or random forests, and their suitability for different types of rock mechanics problems. This lack of methodological depth makes it difficult for readers to understand the specific strengths and limitations of different approaches. My confidence in this assessment is high, as the paper's descriptions of AI methods are consistently high-level, and the lack of specific architectural or algorithmic details is evident. Thirdly, the paper does not adequately address the practical implications and challenges of implementing these technologies in real-world engineering projects. While the paper discusses various applications of AI in rock mechanics, it often focuses on the technical aspects rather than the practical challenges of implementation. For example, the paper mentions the use of multi-layer backpropagation networks for mapping geometry-soil/rock-method descriptors to settlement metrics in tunneling operations, but it does not provide a concrete case study illustrating the practical challenges of data acquisition, model training, and deployment in a real tunneling project. This lack of practical context limits the paper's ability to provide a grounded perspective on the utility of the reviewed techniques. My confidence in this assessment is high, as the paper's focus is consistently on the technical aspects of AI applications, and the lack of detailed case studies with practical implementation details is evident. Finally, the paper does not fully explore the ethical considerations and potential societal impacts of using AI in rock mechanics. The paper does not explicitly discuss ethical considerations or societal impacts, and it does not address potential risks like bias in models, the impact of automation on jobs, or the implications of relying on AI for critical infrastructure decisions. This omission is a significant weakness, as it fails to acknowledge the broader implications of AI adoption in the field. My confidence in this assessment is high, as the paper's content clearly lacks any discussion of ethical considerations or societal impacts.

💡 Suggestions

To enhance the paper's contribution, I recommend several concrete improvements. Firstly, the authors should move beyond a mere summary of existing work and delve into a more critical and analytical discussion of the current state of AI in rock mechanics. This could involve identifying key challenges and limitations of existing AI methodologies when applied to rock mechanics problems. For example, the authors could discuss the difficulties in obtaining high-quality, labeled datasets for training AI models in rock mechanics, or the challenges in interpreting the results of complex AI models in a physically meaningful way. Furthermore, the authors should explore potential solutions to these challenges, such as the development of data augmentation techniques or the use of explainable AI methods. This would require a more in-depth engagement with the existing literature, going beyond a simple description of methods and delving into their specific limitations and potential solutions. Secondly, the paper should include a more detailed discussion of the specific AI techniques and algorithms used in rock mechanics, including their strengths and weaknesses, and provide guidance on their appropriate application. This could involve a comparative analysis of different machine learning algorithms, such as SVMs, random forests, and neural networks, and their suitability for different types of rock mechanics problems, such as image analysis, time-series prediction, or constitutive modeling. For example, when discussing CNNs for image-based fracture detection, the paper should discuss the specific architectural choices, such as the number of layers, filter sizes, and activation functions, and how these choices impact the performance of the model. This would require a more detailed analysis of the specific algorithms and their implementation, going beyond a high-level overview. Thirdly, the paper should address the issue of model generalization and robustness, which is particularly important in rock mechanics where data is often limited and noisy. The authors could discuss techniques for improving model generalization, such as regularization, cross-validation, and ensemble methods. They could also explore the use of transfer learning, where models trained on one dataset are adapted to another dataset, to overcome the limitations of small datasets. Furthermore, the paper should discuss the importance of uncertainty quantification in rock mechanics, where predictions often have significant consequences. The authors could explore methods for quantifying the uncertainty of AI model predictions, such as Bayesian neural networks or Monte Carlo dropout, and discuss how these uncertainties can be incorporated into engineering decision-making. This would require a more detailed discussion of the statistical aspects of AI model development and validation. Fourthly, the paper should include case studies that demonstrate the successful application of AI in rock mechanics. These case studies should not only showcase the benefits of AI but also highlight the challenges and limitations encountered in real-world applications. For example, a case study could focus on the use of AI for predicting rockfalls or landslides, demonstrating how AI can be used to improve hazard assessment and mitigation. Another case study could focus on the use of AI for optimizing the design of underground excavations, demonstrating how AI can be used to improve the efficiency and safety of mining and tunneling operations. These case studies should be accompanied by a detailed discussion of the data requirements, computational resources, and expertise needed to implement AI solutions in rock mechanics. This would provide a more grounded perspective on the practical utility of the reviewed techniques. Finally, the paper should address the ethical considerations and potential societal impacts of using AI in rock mechanics. This should include a discussion of the potential risks and benefits of automating rock mechanics analysis and decision-making processes. For example, the paper could explore the potential for bias in AI models trained on limited or skewed datasets, and the implications of relying on AI-driven decisions in critical infrastructure projects. The discussion should also address the potential for job displacement and the need for retraining programs for engineers and geologists. By addressing these ethical considerations, the paper would provide a more comprehensive and balanced perspective on the future of intelligent rock mechanics, ensuring that the technology is developed and deployed in a responsible and beneficial manner. These suggestions are all actionable and within the scope of a review paper, and they would significantly enhance the paper's contribution to the field.

❓ Questions

Several key uncertainties and methodological choices warrant further clarification. Firstly, given the paper's focus on reviewing existing literature, how do the authors plan to address the lack of novel contributions in their work? Are there any plans to include original research or innovative approaches in future work that would advance the field beyond a synthesis of current knowledge? This question is crucial for understanding the paper's overall impact and its potential to drive future research. Secondly, can the authors provide a more detailed analysis and comparison of the specific AI methodologies used in rock mechanics? This would help readers better understand the strengths and limitations of different approaches and their suitability for various rock mechanics problems. For example, what are the specific advantages and disadvantages of using CNNs versus graph neural networks for modeling fracture networks, and how do these choices impact the accuracy and interpretability of the results? Thirdly, how do the authors envision the integration of intelligent rock mechanics into real-world engineering projects, and what are the potential challenges and opportunities associated with this integration? This question is important for understanding the practical implications of the reviewed research and the steps needed to translate theoretical advancements into real-world applications. For example, what are the key barriers to adopting AI in rock engineering, and how can these barriers be overcome? Fourthly, what are the specific assumptions and limitations of the various AI models when applied to different rock types and geological settings? This question is crucial for understanding the generalizability of the reviewed research and the need for site-specific adaptations. For example, how do the performance of CNNs vary when applied to sedimentary rocks versus metamorphic rocks, and how does the accuracy of PINNs change with the complexity of the geological structure? Finally, what are the ethical considerations and potential societal impacts of using AI in rock mechanics, and how can these be addressed to ensure responsible and beneficial development of the technology? This question is essential for ensuring that the development and deployment of AI in rock mechanics are conducted in a responsible and ethical manner. For example, how can we mitigate the potential for bias in AI models trained on limited or skewed datasets, and what are the implications of relying on AI-driven decisions in critical infrastructure projects? These questions are designed to probe the core methodological choices and assumptions of the paper, and to seek clarification on the practical and ethical implications of the reviewed research.

📊 Scores

Soundness:2.75
Presentation:2.75
Contribution:2.5
Rating: 6.0

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📋 Summary

This paper surveys the trajectory of AI in rock mechanics, from early backpropagation and SVMs to contemporary deep learning, transformers, physics-informed methods, and graph-based learning. It is organized around: (i) data-driven estimation of rock properties (Section 2), covering UCS/moduli and wave velocity inference from indirect indicators and hybrid models; (ii) image-based modeling and fracture detection (Section 3), including deep 3D reconstruction, VAEs/GANs for microstructures, and fracture segmentation (e.g., FraSegNet) and DFN modeling (e.g., DSIM); (iii) AI-assisted constitutive modeling and simulation (Section 4), spanning neural failure criteria, history-dependent LSTM models, and physics-informed neural solvers with a critical discussion of PINN limitations and alternatives; and (iv) applications (Section 5) in rock mass classification, rockburst/geohazard prediction, tunneling/boring, slope stability, and other emerging use cases. Section 6 outlines challenges (data quality, interpretability/trust, computational efficiency, workflow integration, and model maintenance), while the paper indicates an Appendix compiling datasets, codes, and an LLM-assisted tooling example.

✅ Strengths

  • Structured, end-to-end coverage that links property inference (Section 2), geometry extraction and reconstruction (Section 3), constitutive/simulation advances (Section 4), and field applications (Section 5).
  • Useful synthesis of physics-aware and hybrid AI approaches, including candid discussion of PINN sensitivities to initialization, residual scaling, and boundary enforcement, and mention of alternatives like PIRBN and PI-TCN (Section 4).
  • Clear, domain-relevant examples across scales: e.g., VAEs/GANs for digital rock reconstruction (Section 3), FraSegNet and DeepLab variants for fracture segmentation (Section 3), LSTM/ensembles for rockburst forecasting (Section 5.2), PINNs for tunneling-induced deformation (Section 5.3), and CNN surrogates for slope reliability (Section 5.4).
  • Explicit articulation of practical challenges and adoption barriers, including data scarcity/standardization, interpretability and trust, computational cost, workflow integration, and lifecycle model maintenance (Section 6).
  • Promise of practical resources (Appendix with datasets/codes and an LLM-assisted rockburst case study) to support reproducibility and benchmarking (Sections 1 and 5).

❌ Weaknesses

  • Bibliometric/literature selection methodology lacks transparency and reproducibility: the review relies on 17 journals (Introduction) without PRISMA-like details (databases, complete venue list, inclusion/exclusion criteria, screening protocol, handling of conference papers and interdisciplinary venues). This raises selection-bias concerns and undermines claims of a comprehensive, evolution-focused synthesis.
  • Primarily descriptive synthesis with limited quantitative aggregation: no standardized benchmarking, cross-dataset comparisons, or meta-analytic summaries that tie tasks, datasets, and metrics into a unified taxonomy.
  • Coverage appears uneven across interdisciplinary advances (e.g., major ML venues where physics-ML, scientific machine learning, and surrogate modeling are frequently published) given the stated journal-only scope; this may miss important developments relevant to PINNs, operator learning, and graph-based solvers.
  • Forward-looking claims regarding LLMs and decision support (Abstract; Section 1; Section 6) are speculative without presented, evaluated case studies in core rock mechanics workflows beyond a brief mention of LLM-assisted tooling in the Appendix.
  • No explicit assessment of dataset biases, data governance, and domain shift that are specific to geologic settings (e.g., site specificity, sensor variability), despite emphasizing data limitations (Section 6).
  • Lack of a consolidated, visual taxonomy/table that maps problem classes to representative methods, datasets, and recommended evaluation metrics to guide practitioners.

❓ Questions

  • Please provide full details of the literature search and screening protocol: specific databases or platforms queried; the exact list of 17 journals; search strings; time windows; inclusion and exclusion criteria; handling of conference proceedings and non-English literature; and whether grey literature was considered.
  • How did you mitigate selection bias introduced by restricting to 17 journals? What steps were taken to capture interdisciplinary advances from ML venues (e.g., physics-informed learning, operator learning, graph PDE solvers)?
  • Can you release the Appendix resource index (datasets/codes) with stable links, metadata (task, size, modalities, licensing), and suggested benchmark splits to enable reproducibility?
  • Could you add a consolidated taxonomy (e.g., a table/figure) that maps tasks (property inference, imaging/reconstruction, constitutive modeling, PDE surrogates, applications) to representative methods, datasets, and recommended metrics, highlighting common failure modes and best practices?
  • Do you have any quantitative synthesis (meta-analysis or standardized re-evaluation) to compare classes of methods across shared datasets (e.g., for UCS/moduli, fracture segmentation, or slope stability surrogates)? If not, can you outline a plan or minimal benchmark protocol?
  • Regarding PINNs and alternatives (Section 4): can you specify recommended practices (residual scaling, boundary enforcement strategies, curriculum/collocation sampling) that you found most reliable in rock-mechanics PDEs? Any guidance on when to prefer graph neural PDE surrogates or operator learners over PINNs?
  • On LLMs: beyond code scaffolding and summarization, do you have any concrete, evaluated examples of LLM integration into geotechnical workflows (e.g., data QA, feature engineering, report triage) and associated risk controls (prompting patterns, retrieval augmentation, verification)?
  • How will the community evaluate and maintain models over the lifecycle of long-running projects (e.g., tunnels, mines)? Can you propose versioning, monitoring, and recalibration protocols tied to data drift and concept drift in field deployments?

⚠️ Limitations

  • Risk of selection bias due to a restricted set of journals and absence of a transparent, replicable search protocol; potential omission of key interdisciplinary advances.
  • The survey does not provide quantitative benchmarking or meta-analysis, limiting evidence-based guidance on method selection.
  • Speculative discussion of LLMs and limited evaluation of graph-based and operator-learning methods in rock mechanics contexts.
  • Potential negative societal impacts: overreliance on black-box predictions in safety-critical decisions (rockburst/landslides), automation bias, and miscalibrated models under domain shift; data governance and privacy issues for field data; and environmental costs of training large models without clear efficiency gains.
  • Reproducibility risks unless the promised datasets, codes, and tooling are released with curation, licensing, and documentation.

🖼️ Image Evaluation

Cross-Modal Consistency: 30/50

Textual Logical Soundness: 23/30

Visual Aesthetics & Clarity: 10/20

Overall Score: 63/100

Detailed Evaluation (≤500 words):

Image-first understanding (visual ground truth)

  • Figure 1: Regional geologic profile and tunnel cross-section. Cues: lithology patterns, faults, boreholes, “rockburst” callouts with depths, red “high geo-stress hazards” zone. Trend: multiple rockbursts clustered within the high-stress section.
  • Figure 2: Bar/line plot vs Time categories (4BR, 3BR, 2BR, 1BR, R, 1AR). Left axis: “Number of daily microseismic events.” Right axis: “S Value.” Bars colored by dates (12.17*R, 12.29*R, 1.14*R). Trend: events peak near 1BR–R; S-value varies non-monotonically.
  • Figure 3: Bar/line plot vs Time categories (3BR–1AR). Left axis: “Daily cumulative energy (×10^7).” Right axis: “Incidence of high- and medium-energy events (%)”. Trend: cumulative energy and incidence peak near R, decline at 1AR.

1. Cross-Modal Consistency

• Major 1: Figures are not referenced or captioned in the manuscript, blocking claim verification and context. Evidence: No “Figure” callouts in Sec. 5.2 despite discussing microseismic/rockburst precursors.

• Major 2: Ambiguous mapping in Fig. 2 between legend and axes (line labeled “S Value” but no color/series mapping; categories undefined). Evidence: Fig. 2 legend shows “S Value” without a series key; X-axis uses 4BR…1AR with no definition.

• Major 3: Illegible elements at print size in Fig. 1 (lithology legend, borehole IDs, many labels), impeding interpretation. Evidence: Fig. 1 lithology patterns and city/borehole text are too small to read at ≈100%.

• Minor 1: Fig. 3 unit ambiguity; left axis shows “×10^7” without explicit unit (likely J); “incidence” definition unspecified.

• Minor 2: Acronyms BR/R/AR not defined in visuals or text near first reference.

2. Text Logic

• Major 1: Bibliometric claims lack shown evidence/plots, weakening a central thread of the Introduction. Evidence: “A bibliometric overview… The results highlight that AI has taken root…” (no accompanying figure/table).

• Minor 1: Typographical breaks/formatting errors disrupt flow (e.g., “compu tational”; truncated citation “Li et al., 2025)”). Evidence: Introduction contains split words and a dangling citation.

• Minor 2: Several strong generalizations (e.g., “AI has transcended empirical prediction”) lack quantitative support within the body.

3. Figure Quality

• Major 1: Fig. 1 contains dense symbology and small fonts; critical legend items unreadable at print size.

• Minor 1: Inconsistent legend design and missing series keys in Figs. 2–3.

• Minor 2: Axes labels and acronyms not self-explanatory; no captions to guide interpretation.

Key strengths:

  • Comprehensive, well-structured survey spanning properties, imaging, constitutive modeling, and applications.
  • Balanced discussion of PINNs’ limitations and hybrid/uncertainty-aware trends.
  • Strong, up-to-date bibliography across subdomains.

Key weaknesses:

  • Missing figure callouts/captions; ambiguous/illegible visuals block verification.
  • Bibliometric narrative lacks presented evidence.
  • Several notation/typo issues and undefined acronyms in figures.

Recommendations:

  • Add formal captions and in-text references for Figs. 1–3; define BR/R/AR and S-value; fix legend/axis mappings and units.
  • Provide a bibliometric figure/table supporting trend claims.
  • Increase font sizes and simplify Fig. 1 legend; add call-outs and scale bars.
  • Tighten prose and resolve formatting/citation glitches.

📊 Scores

Originality:2
Quality:2
Clarity:3
Significance:2
Soundness:2
Presentation:3
Contribution:2
Rating: 4

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📋 Summary

This paper offers a comprehensive review of the integration of artificial intelligence (AI) and machine learning (ML) techniques into the field of rock mechanics, aiming to synthesize recent advancements and highlight the potential of these methods to address long-standing challenges. The authors trace the historical development of AI in rock mechanics, from early methods like backpropagation and support vector machines to modern deep learning architectures, including convolutional neural networks (CNNs) and transformer models. The paper is structured around a logical progression, beginning with foundational AI methodologies and culminating in practical applications across various domains such as rock mass classification, rockburst prediction, tunneling, boring operations, and slope stability analysis. The core methodological approach involves a systematic review of existing literature, coupled with illustrative examples of how specific AI techniques have been applied to solve problems in rock engineering. For instance, the paper discusses the use of CNNs for microseismic event localization, long short-term memory (LSTM) networks for modeling rheological behavior, and physics-informed neural networks (PINNs) for solving partial differential equations. The paper also explores the application of AI in data-driven estimation of rock properties, image-based modeling and fracture detection, and AI-assisted constitutive modeling and simulation. The empirical findings presented in the paper are primarily derived from existing literature, with the authors synthesizing results from various studies to demonstrate the effectiveness of AI in rock mechanics. The paper highlights the potential of AI to improve the accuracy and efficiency of rock engineering tasks, such as predicting rockbursts, optimizing tunneling operations, and assessing slope stability. Despite its comprehensive nature, the paper acknowledges the limitations of current AI applications in rock mechanics, including challenges related to data quality, model generalization, and interpretability. The authors emphasize the need for further research to address these limitations and to fully realize the potential of AI in this field. The paper's significance lies in its ability to provide a clear and concise overview of the current state of AI in rock mechanics, highlighting both the achievements and the challenges that lie ahead. It serves as a valuable resource for researchers and practitioners interested in exploring the application of AI in this domain, offering insights into the various techniques and their potential impact on the field. The paper also underscores the importance of interdisciplinary collaboration between AI experts and rock mechanics specialists to advance the development and application of intelligent rock mechanics systems. By bridging the gap between these two fields, the paper contributes to the ongoing efforts to create more accurate, efficient, and reliable methods for analyzing and predicting the behavior of rock masses.

✅ Strengths

The paper's primary strength lies in its comprehensive and well-structured review of the application of AI and ML in rock mechanics. I appreciate the authors' efforts to synthesize a wide range of literature, providing a valuable resource for researchers and practitioners in this interdisciplinary field. The paper effectively traces the historical development of AI in rock mechanics, offering a clear understanding of how the field has evolved over time. The inclusion of numerous examples of successful AI applications, such as the use of CNNs for microseismic event localization and PINNs for solving partial differential equations, demonstrates the practical potential of these techniques. I find the discussion of various AI methodologies, including backpropagation, support vector machines, CNNs, LSTMs, and transformers, to be informative and well-presented. The paper's organization into distinct sections, such as data-driven estimation of rock properties, image-based modeling, and AI-assisted constitutive modeling, facilitates a logical flow of information and makes it easier for readers to navigate the content. The authors' ability to connect theoretical concepts with practical applications is commendable, as it highlights the real-world relevance of AI in rock mechanics. The paper also acknowledges the limitations of current AI applications, demonstrating a balanced and critical perspective. The emphasis on the need for interdisciplinary collaboration between AI experts and rock mechanics specialists is a valuable contribution, as it underscores the importance of bridging the gap between these two fields. Overall, the paper provides a solid foundation for understanding the current state of AI in rock mechanics and identifies promising avenues for future research.

❌ Weaknesses

Despite its strengths, I have identified several weaknesses in the paper that warrant further discussion. Firstly, the paper's broad scope, while providing a comprehensive overview, leads to a lack of depth in certain areas. The rapid advancements in AI and ML mean that the field is constantly evolving, and the paper could benefit from a more focused discussion on the most recent and cutting-edge techniques. For instance, while the paper mentions transformers, it could delve deeper into their specific applications and advantages in rock mechanics, beyond visual classification tasks. Similarly, the discussion of graph neural networks (GNNs) is limited, and a more detailed exploration of their potential in modeling complex rock systems would be valuable. This lack of depth might leave readers with a superficial understanding of these advanced techniques and their specific relevance to rock mechanics. Secondly, the paper could provide a more in-depth critical analysis of the limitations and challenges associated with applying AI in rock mechanics. While the paper acknowledges issues like data quality, model generalization, and interpretability, it does not fully explore potential solutions or mitigation strategies. For example, the paper could discuss specific strategies for improving data quality in rock mechanics, such as data augmentation techniques or the use of synthetic data. Similarly, the discussion of model generalization could be enhanced by exploring techniques like domain adaptation or transfer learning, which are particularly relevant in the context of limited and site-specific data in rock mechanics. The absence of a thorough discussion on these challenges might lead readers to underestimate the difficulties involved in deploying AI models in real-world rock engineering applications. Thirdly, the paper's structure, while logical, could be refined to better highlight the connections between different AI methodologies and their applications. The current structure, which separates methodologies and applications into distinct sections, might make it difficult for readers to understand how specific AI techniques can be applied to solve particular problems in rock mechanics. A more integrated approach, where methodologies and applications are discussed in conjunction, could improve the paper's clarity and impact. For instance, when discussing CNNs, the paper could simultaneously present examples of their application in rock mechanics, such as image-based fracture detection. This would provide a more concrete understanding of how these techniques are used in practice. Fourthly, the paper could benefit from a more detailed discussion of the practical implications of using AI in rock mechanics. While the paper provides examples of successful applications, it could elaborate on the challenges of deploying AI models in real-world engineering practice. This could include discussions on the need for robust validation, the importance of uncertainty quantification, and the ethical considerations associated with using AI in high-stakes engineering decisions. The lack of such a discussion might lead to an overestimation of the current practical applicability of AI in rock mechanics. Lastly, the paper's reliance on existing literature, while necessary for a review paper, might limit its novelty. The paper primarily synthesizes existing knowledge rather than presenting new research findings or innovative methodologies. While this is understandable for a review, it means that the paper might not offer significant new insights to researchers who are already familiar with the field. The paper could have been strengthened by including a more forward-looking perspective, discussing potential future research directions and emerging trends in more detail. These weaknesses, while not undermining the overall value of the paper, suggest areas where further work is needed to fully realize the potential of AI in rock mechanics. I am confident that addressing these points would significantly enhance the paper's contribution to the field.

💡 Suggestions

To address the identified weaknesses, I propose the following suggestions for improving the paper: Firstly, the authors should consider narrowing the scope of the paper to allow for a more in-depth discussion of specific AI techniques and their applications in rock mechanics. Instead of attempting to cover all aspects of AI in rock mechanics, the authors could focus on a few key areas where AI has shown the most promise, such as rock mass classification, rockburst prediction, or slope stability analysis. This would enable a more detailed exploration of the challenges and opportunities associated with applying AI in these specific domains. Secondly, the authors should dedicate a section to a critical analysis of the limitations and challenges of using AI in rock mechanics. This section should not only acknowledge the limitations but also propose potential solutions and mitigation strategies. For example, the authors could discuss specific data augmentation techniques that are suitable for rock mechanics data, or they could explore the use of domain adaptation and transfer learning to improve model generalization. The authors should also discuss the importance of uncertainty quantification in AI models and propose methods for assessing and communicating uncertainty in rock engineering applications. Thirdly, the authors should consider restructuring the paper to better integrate the discussion of AI methodologies and their applications. Instead of having separate sections for methodologies and applications, the authors could discuss specific AI techniques in the context of their applications. For example, when discussing CNNs, the authors could simultaneously present examples of their application in rock mechanics, such as image-based fracture detection. This would provide a more concrete understanding of how these techniques are used in practice and would make the paper more accessible to readers who are not experts in AI. Fourthly, the authors should include a more detailed discussion of the practical implications of using AI in rock mechanics. This discussion should go beyond the examples of successful applications and should address the challenges of deploying AI models in real-world engineering practice. The authors could discuss the need for robust validation, the importance of uncertainty quantification, and the ethical considerations associated with using AI in high-stakes engineering decisions. This would provide a more realistic perspective on the current state of AI in rock mechanics and would help to manage expectations regarding its practical applicability. Fifthly, the authors should include a more forward-looking perspective, discussing potential future research directions and emerging trends in more detail. This could include a discussion of the potential of emerging AI techniques, such as graph neural networks and transformers, in rock mechanics. The authors could also discuss the potential of using AI to address other challenges in rock mechanics, such as the management of large-scale geotechnical data and the development of real-time monitoring systems. By addressing these suggestions, the authors can significantly enhance the paper's contribution to the field and provide a more comprehensive and insightful review of intelligent rock mechanics.

❓ Questions

Based on my analysis, I have several key questions that, if addressed, could further clarify the paper's arguments and contributions. Firstly, regarding the discussion on transformers, could the authors elaborate on the specific advantages of using transformer architectures over other deep learning models in rock mechanics applications, particularly in tasks beyond visual classification? Are there any unique characteristics of rock mechanics data that make transformers particularly well-suited for these tasks? Secondly, concerning the application of graph neural networks (GNNs), could the authors provide more concrete examples of how GNNs can be used to model complex rock systems? What specific types of rock mechanics problems are most amenable to solution using GNNs, and what are the potential challenges associated with their application? Thirdly, in the context of data quality and model generalization, could the authors discuss specific strategies for improving the robustness of AI models in rock mechanics, particularly when dealing with limited and site-specific data? Are there any best practices for data collection and preprocessing that can help to mitigate these challenges? Fourthly, regarding the practical deployment of AI models in rock engineering, could the authors elaborate on the challenges of validating these models in real-world scenarios? What specific metrics or criteria should be used to assess the performance of AI models in rock mechanics, and how can uncertainty be effectively quantified and communicated? Lastly, considering the rapid advancements in AI, what are the authors' perspectives on the future role of AI in rock mechanics? Are there any emerging AI techniques or applications that the authors believe are particularly promising for this field? How do the authors envision the collaboration between AI experts and rock mechanics specialists evolving in the coming years to further advance the development and application of intelligent rock mechanics systems?

📊 Scores

Soundness:3.0
Presentation:3.0
Contribution:2.75
Rating: 6.5

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