2510.0013 A Review of Intelligent Rock Mechanics: From Methods to Applications v2

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📋 Summary

This paper provides a comprehensive review of the integration of artificial intelligence (AI) and machine learning (ML) techniques into the field of rock mechanics. The authors meticulously trace the historical development of AI applications in this domain, starting from early methods like backpropagation and support vector machines to modern deep learning architectures such as convolutional neural networks (CNNs) and transformers. The review highlights the roles of these AI methodologies in various aspects of rock mechanics, including microstructure reconstruction, mechanical parameter estimation, constitutive modeling, and real-time hazard prediction. The paper also discusses emerging techniques like physics-informed neural networks (PINNs) and graph-based learning, which aim to bridge data-driven inference with physical interpretability. Furthermore, the authors explore the potential of large language models (LLMs) in automating code generation and decision support for geotechnical analysis. The review identifies key challenges, such as data quality, model generalization, and interpretability, and emphasizes the need for standardized datasets, interdisciplinary collaboration, and transparent AI workflows. The paper concludes with a forward-looking perspective on developing intelligent frameworks that couple physical knowledge, spatial reasoning, and adaptive learning, advancing rock mechanics towards fully autonomous systems. The paper's significance lies in its ability to synthesize the current state of AI in rock mechanics, highlighting both the progress made and the challenges that remain. It serves as a valuable resource for researchers and practitioners in the field, providing a clear overview of the existing literature and pointing towards future directions for research and development. The authors effectively demonstrate how AI and ML are transforming the field, offering new tools for analysis, prediction, and decision-making. However, the paper also acknowledges the limitations of current approaches, particularly in terms of model interpretability and the need for more robust and generalizable models. The paper's emphasis on the need for interdisciplinary collaboration and standardized datasets underscores the importance of a holistic approach to the development and application of AI in rock mechanics. Ultimately, this review provides a well-structured and informative overview of a rapidly evolving field, highlighting the potential of AI to revolutionize rock mechanics while also acknowledging the challenges that must be addressed to realize this potential fully. The paper's focus on both the technical and practical aspects of AI in rock mechanics makes it a valuable contribution to the field, offering insights for both researchers and practitioners. The authors' ability to synthesize a large body of literature into a coherent and accessible narrative is commendable, making this review a useful starting point for anyone interested in the intersection of AI and rock mechanics.

✅ Strengths

This paper offers a comprehensive and well-structured review of the integration of AI and ML in rock mechanics, covering a wide range of applications and methodologies. The authors effectively trace the historical development of AI in this field, providing valuable context for understanding the current state of the art. The paper's thoroughness makes it an excellent resource for researchers and practitioners alike, offering a clear overview of the existing literature and highlighting key advancements. The authors successfully highlight the roles of various AI methodologies in different aspects of rock mechanics, including microstructure reconstruction, mechanical parameter estimation, constitutive modeling, and real-time hazard prediction. This detailed breakdown of applications demonstrates the versatility and potential of AI in this field. Furthermore, the paper's discussion of emerging techniques like PINNs and graph-based learning is particularly insightful, showcasing the cutting-edge research being conducted in this area. The authors also effectively discuss the potential of large language models (LLMs) in automating code generation and decision support, which is a promising area for future development. The paper's identification of key challenges, such as data quality, model generalization, and interpretability, is also a significant strength. By acknowledging these limitations, the authors provide a balanced perspective on the current state of AI in rock mechanics and highlight the areas that require further research and development. The emphasis on the need for standardized datasets and interdisciplinary collaboration underscores the importance of a holistic approach to the field. The paper's forward-looking perspective on developing intelligent frameworks that couple physical knowledge, spatial reasoning, and adaptive learning is also commendable, providing a clear vision for the future of AI in rock mechanics. The paper's ability to synthesize a large body of literature into a coherent and accessible narrative is a significant strength, making it a valuable resource for both experts and newcomers to the field. The authors' clear and concise writing style, combined with their thorough analysis of the literature, makes this review a pleasure to read and a valuable contribution to the field. The paper's focus on both the technical and practical aspects of AI in rock mechanics is also a strength, making it relevant to both researchers and practitioners. The authors effectively demonstrate how AI and ML are transforming the field, offering new tools for analysis, prediction, and decision-making. The paper's ability to highlight both the potential and the challenges of AI in rock mechanics makes it a valuable contribution to the field, providing a balanced and nuanced perspective on this rapidly evolving area.

❌ Weaknesses

While this paper provides a comprehensive overview of AI applications in rock mechanics, I have identified several weaknesses that warrant further discussion. Firstly, the paper, while mentioning limitations of specific AI/ML methods, lacks a deep, critical analysis of these limitations. For instance, the paper acknowledges that backpropagation (BP) networks are sensitive to data completeness and noise, and that convolutional neural networks (CNNs) suffer from limited robustness when faced with low contrast, irregular morphologies, or imbalanced datasets. However, the analysis remains superficial, and the paper does not delve into the underlying reasons for these limitations or explore potential mitigation strategies in detail. This lack of in-depth critical analysis is a significant weakness, as it prevents the reader from gaining a nuanced understanding of the challenges associated with applying these methods in rock mechanics. The paper also does not cite works that critically analyze the limitations of specific AI/ML methods in rock mechanics, further highlighting this gap. My confidence in this assessment is high, as the paper's brief mentions of limitations contrast sharply with the depth of its other discussions. Secondly, while the paper includes a case study on deep learning for rockburst prediction and an example of LLM-generated code, it lacks a detailed discussion on the practical implementation and real-world application of the reviewed AI/ML methods. The paper does not provide sufficient information on the challenges faced during deployment, the computational requirements, or the impact of these implementations. For example, when discussing the use of machine learning for rock property estimation, the paper could have included specific examples of how these methods have been applied in different geological settings, such as in the characterization of coalbed methane reservoirs or in the assessment of rock stability in tunneling projects. The absence of such detailed discussions limits the paper's practical relevance and makes it difficult for practitioners to understand the feasibility of these techniques. My confidence in this assessment is medium, as the paper does provide some examples, but lacks the depth required for a thorough understanding of practical implementation. Finally, the paper completely omits any discussion of the ethical considerations and potential societal impacts of integrating AI and ML into rock mechanics. This is a significant oversight, as the use of AI in critical infrastructure projects raises important ethical questions regarding bias, transparency, and accountability. The paper does not address the potential for bias in AI models, the need for transparency and explainability, or the implications of using AI for decision-making in critical infrastructure projects. This lack of discussion is a major weakness, as it fails to acknowledge the broader societal implications of the research being reviewed. My confidence in this assessment is high, as the paper's complete absence of any discussion on ethical considerations is evident. These weaknesses, taken together, limit the paper's overall impact and highlight the need for further research and discussion in these areas.

💡 Suggestions

To address the identified weaknesses, I recommend several concrete improvements. Firstly, the authors should include a more in-depth critical analysis of the limitations of specific AI/ML methods in the context of rock mechanics. This analysis should go beyond simply stating that a method has limitations and should delve into the underlying reasons for these limitations. For example, when discussing the limitations of CNNs, the authors should explore the challenges of applying CNNs to complex geological structures with varying scales and orientations. They should also discuss the limitations of CNNs in capturing long-range dependencies and their sensitivity to data augmentation techniques. Similarly, the authors should explore the challenges of using physics-informed neural networks (PINNs) in complex rock mechanics problems, such as the difficulty in defining appropriate loss functions and the potential for numerical instability. Furthermore, a more detailed analysis of the interpretability of graph neural networks (GNNs) in the context of rock mechanics would be beneficial, addressing how the learned node and edge features relate to physical properties and processes. This deeper critical analysis would provide a more balanced and nuanced perspective on the current state of AI in rock mechanics. Secondly, the authors should include more detailed discussions of real-world applications and case studies. This should include specific examples of how these methods have been applied in different geological settings, such as in the characterization of coalbed methane reservoirs or in the assessment of rock stability in tunneling projects. The authors should also discuss the challenges of deploying these models in real-world scenarios, such as the need for robust data acquisition and the handling of uncertainty in model predictions. Furthermore, the authors should explore the practical considerations of using AI for real-time hazard prediction, such as the computational requirements and the need for reliable sensor data. By providing concrete examples and discussing the practical challenges, the review would be more useful for practitioners in the field. This would also help to bridge the gap between the theoretical aspects of AI and their practical implementation in rock mechanics. Finally, the authors should address the ethical considerations and potential societal impacts of integrating AI and ML into rock mechanics. This should include a discussion of the potential for bias in AI models, the need for transparency and explainability, and the implications of using AI for decision-making in critical infrastructure projects. For example, the authors could discuss the ethical implications of using AI to predict rockfalls or landslides, and the potential consequences of inaccurate predictions. Furthermore, the authors could explore the impact of AI on the workforce in the geotechnical engineering field, and the need for training and education to ensure that practitioners are equipped with the necessary skills to work with AI. By addressing these ethical considerations, the review would provide a more comprehensive and responsible perspective on the future of AI in rock mechanics. These changes would significantly enhance the paper's value and make it a more complete and insightful resource for the field.

❓ Questions

Based on my analysis, I have several questions that I believe would be valuable for the authors to address. Firstly, could you provide more details on the specific challenges and limitations of applying different AI and ML methods in rock mechanics, such as the interpretability of deep learning models and the generalization capabilities of physics-informed neural networks? I am particularly interested in understanding the underlying reasons for these limitations and the potential strategies for mitigating them. Secondly, how do you envision the integration of AI and ML methods into the daily practice of rock mechanics engineers, and what are the potential barriers to adoption in the industry? I am curious to know what steps need to be taken to ensure that these methods are accessible and usable for practitioners in the field. Thirdly, can you elaborate on the role of large language models (LLMs) in automating code generation and decision support for geotechnical analysis, and provide examples of how these models can be effectively utilized in the field? I am interested in understanding the practical implications of using LLMs in this context and the potential benefits and challenges associated with their adoption. Finally, given the increasing reliance on data-driven models, how can we ensure the quality and reliability of the data used for training these models, and what steps can be taken to address the issue of data scarcity in rock mechanics? I believe that addressing these questions would provide a more complete and nuanced understanding of the current state and future prospects of AI in rock mechanics.

📊 Scores

Soundness:3.0
Presentation:3.0
Contribution:3.0
Rating: 7.5

AI Review from ZGCA

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📋 Summary

This paper presents a narrative review of 'intelligent rock mechanics', synthesizing how AI/ML methods have been applied across rock mechanics from foundational algorithms (BP, SVMs, CNNs, LSTMs, GANs, transformers) to contemporary physics-aware techniques such as PINNs and graph-based models. The review is organized by capability and application: Section 2 covers data-driven estimation of mechanical properties (UCS, stiffness, wave velocities); Section 3 surveys image-based modeling and fracture detection (deep 3D reconstruction, VAEs/GANs, fracture segmentation in 2D/3D, DSIM for DFN modeling); Section 4 addresses AI-assisted constitutive modeling and physics-informed solvers (neural constitutive laws, FEM integration, PINN variants, multiscale and poromechanical applications); Section 5 discusses applications in rock engineering (rock mass classification, rockburst/geohazard prediction, tunneling and boring, slope stability, and other emerging areas); Section 6 outlines challenges (data quality/standardization, interpretability/trust, computational efficiency, workflow integration, and maintenance), and Section 7 concludes with an outlook toward hybrid, physics-aware, and potentially autonomous systems.

✅ Strengths

  • Breadth and structure: The paper provides a well-organized panorama across methods-to-applications, with logical progression from property estimation (Section 2) through imaging and reconstruction (Section 3), mechanics and solvers (Section 4), and field applications (Section 5).
  • Current coverage: It captures modern trends such as physics-informed approaches and hybrid models (Sections 1 and 4), image-driven fracture segmentation and 3D reconstruction (Section 3), and operational decision-making in tunneling/slope stability via ensembles and time-series models (Section 5).
  • Bridging AI and mechanics: The discussion emphasizes embedding physical laws (PINNs/PIRBN/PI-TCN) and multiscale integration (FEM-DEM/RVE surrogates) (Section 4), aligning with the field’s need for interpretability and mechanical consistency.
  • Challenges and outlook: Section 6 clearly enumerates practice-relevant issues (data scarcity, interpretability/trust, computational training cost, workflow integration, lifecycle maintenance) and advocates for standardized datasets and reproducible workflows.
  • Readable synthesis with many concrete references: The review cites a broad literature, often pointing to representative architectures and use cases (e.g., rockburst early warning with LSTM and ensembles in Section 5.2; DSIM and point-cloud methods in Section 3).

❌ Weaknesses

  • Lack of a documented review protocol despite claiming a 'systematic review': The paper does not specify search strategy, databases, time window, inclusion/exclusion criteria, screening steps, or a PRISMA-like flow. The mention of a 'bibliometric overview of 17 journals' (Section 1) is not methodologically described. This undermines completeness and reproducibility.
  • Limited critical appraisal: The review primarily summarizes reported gains. It rarely interrogates the fairness of baselines, appropriateness of metrics, or robustness of the underlying studies. For example, PINNs and hybrid models (Sections 1 and 4) are presented as promising without a balanced analysis of convergence pathologies, boundary-condition handling, or scale limits observed in practice.
  • No quantitative synthesis or structured comparison: There are no consolidated tables mapping tasks to methods/datasets/metrics with cross-study comparisons, nor meta-analytic or systematic qualitative comparisons (e.g., what works best for rockburst vs slope stability under which data regimes).
  • Resource gap: Although the paper emphasizes standardized datasets and reproducibility (Abstract; Section 6), the manuscript does not present a clearly accessible artifact (e.g., curated dataset index, benchmark tasks, code repository). The Appendix is referenced as compiling datasets/codes and a case study with LLM-assisted tooling, but concrete access and structure are not provided here.
  • Breadth over depth: Some claims and examples (e.g., LLMs for decision support/code generation in geotechnics mentioned in the Abstract and Introduction) are forward-looking but not backed by concrete, critically evaluated case studies. Similarly, several application sections (Section 5) highlight performance improvements without discussing failure modes or negative results.
  • Scope fit for an ML venue: The contribution is a domain-focused review without new methods, benchmarks, or a rigorous survey framework; impact for a general ML audience may be limited unless accompanied by a strong taxonomy, benchmarks, or reproducibility assets.

❓ Questions

  • Systematic review protocol: Please detail the search strategy (databases, queries), time window, inclusion/exclusion criteria, screening steps, and inter-rater procedures. If this is not a systematic review, consider reframing the claim.
  • Coverage and bias: How did you ensure coverage beyond the 17 journals mentioned? Were conference proceedings (e.g., top ML/AI venues) systematically included for methods like PINNs/GNNs in mechanics?
  • Comparative synthesis: Can you provide structured comparison tables by task (e.g., UCS, modulus, rockburst, slope stability, TBM operations) listing datasets, sample sizes, metrics, baselines, and best-performing methods, along with ablation/limitations notes?
  • Evidence quality: For representative studies in Sections 2–5, can you assess baseline fairness (training budgets, data sizes, leakage control), metric choices, uncertainty quantification, and external validation? Any notable negative or non-replicable results?
  • PINNs and hybrids: Please discuss common failure modes (convergence instability, boundary conditions, stiff equations, scaling to 3D multiphysics), and summarize empirical guidance on remedies (curriculum, domain decomposition, adaptive weighting).
  • Datasets and benchmarks: Can you release the Appendix resources as a curated index with standardized splits and recommended benchmarks? If possible, include an open repository with scripts to reproduce baseline results for at least 2–3 exemplar tasks.
  • Taxonomy and guidance: Could you formalize a decision framework mapping problem types (small data, physics constraints, time series, point clouds) to recommended model classes and evaluation protocols, including uncertainty quantification and interpretability methods?
  • LLMs in geomechanics: Beyond high-level claims, can you provide concrete, critically evaluated examples (e.g., code-generation productivity studies, report parsing pipelines) and discuss their reliability and failure cases?
  • Threats to validity: How do site-specific biases, sensor drift, and geological non-stationarities affect reported models? What cross-site generalization evidence exists, and what strategies (domain adaptation, physics regularization) work best?

⚠️ Limitations

  • The review currently lacks a transparent methodology, making it hard to assess completeness and potential selection bias.
  • The synthesis emphasizes reported successes; limited discussion of negative results, overfitting risks with small site-specific datasets, and cross-site generalization failures.
  • Physics-aware models (e.g., PINNs) face nontrivial training instabilities and scaling limits that are not critically dissected here.
  • Potential societal impacts: Over-reliance on black-box predictions for safety-critical decisions (rockburst, slope stability) could cause harm if uncertainty and validation are inadequate. Dataset biases may lead to inequitable risk assessments across sites. Recommendations should emphasize uncertainty reporting, external validation, and human-in-the-loop decision-making.

🖼️ Image Evaluation

Cross‑Modal Consistency: 26/50

Textual Logical Soundness: 22/30

Visual Aesthetics & Clarity: 8/20

Overall Score: 56/100

Detailed Evaluation (≤500 words):

Image‑first understanding (visual ground truth)

• Figure 1 / (a) Schematic of message passing in a graph; arrows between nodes; no readable labels.

• Figure 1 / (b) “Multi‑fidelity GNN” block diagram; two DNN branches; no variable names.

• Figure 1 / (c) Bar chart (computation time) and line plot (error vs NDOF); axes/units illegible.

• Figure 1 / (d) Simplified FEA cases with color maps of |u|; qualitative comparison FEA vs MFGNN.

• Rockburst visuals / (a) Geological cross‑section showing lithologies/faults and rockburst depths; symbols legend tiny.

• Rockburst visuals / (b) Time series: daily microseismic events and S‑value; dual‑axis; labels unreadable.

• Rockburst visuals / (c) Time series: daily cumulative energy and incidence; dual‑axis; text too small.

Synopsis: Fig. 1 appears to illustrate GNN/MFGNN methodology and performance for FEM surrogates. The rockburst set seems to show site geology and monitoring trends; none are tied to captions in the text here.

1. Cross‑Modal Consistency

• Major 1: “see Figure 1” is used to support PINN/Shield‑tunneling discussion, but Fig. 1 shows MFGNN for FEM, not PINN. Evidence: Sec 5.3 “…PINN formulations… for shield tunneling… (Zhang et al., 2023b)(see Figure 1).”

• Major 2: Three rockburst figures appear without caption numbers or in‑text citation in Sec 5.2. Evidence: Uncaptioned rockburst cross‑section and two time‑series plots after Sec 5.5.

• Major 3: Fig. 1 sub‑panes lack labels (a/b/…), while prose never specifies which pane supports which claim. Evidence: Fig. 1 caption “Machine learning methods for solving FEM problems (Black & Najafi, 2022)” without sub‑labels.

• Minor 1: Mixed figure sourcing—caption credits Black & Najafi (2022), but nearby prose ties to Zhang et al. (2023b). Evidence: Sec 5.3 around Fig. 1.

• Minor 2: No axis titles/units visible in performance plots, hindering claim verification. Evidence: Fig. 1(c) small, unreadable axes.

2. Text Logic

• Major 1: The Introduction mentions “A bibliometric overview of 17 journals,” but no figure/table/method is provided. Evidence: Sec 1 “A bibliometric overview of 17 journals…” (no supporting visual/data).

• Minor 1: Occasional forward‑looking claims (e.g., “co‑pilot” for operations) lack quantitative backing in Results sections.

• Minor 2: Some reference-year mix (2025) cited for methods without clarifying preprint/accepted status.

3. Figure Quality

• Major 1: Multiple panes are illegible at print size; tick labels and legends cannot be read. Evidence: Panel resolutions ≈136–175 px height; text unreadable.

• Major 2: Missing sub‑figure labels and legends impede independent interpretation. Evidence: Fig. 1 lacks (a–d) markers; rockburst figures lack captions.

• Minor 1: Color choices appear adequate, but no colorbars for displacement fields.

Key strengths:

• Broad, well‑structured survey bridging properties, imaging, constitutive ML, and applications.

• Good coverage of physics‑aware ML (PINNs, PIRBN, PI‑TCN, MFGNN) with relevant citations.

• Practical framing of challenges and workflow integration.

Key weaknesses:

• Core figure–text mismatches (PINN vs MFGNN) and uncited/uncaptioned rockburst visuals.

• Illegible plots block claim verification.

• Bibliometric claim lacks presented evidence.

Recommendations:

• Relabel Fig. 1 with sub‑panes, readable axes/units, and map each pane to specific sentences.

• Provide a dedicated figure/table for the “17‑journal” bibliometric overview.

• Add captions and in‑text references for rockburst figures or remove if not discussed.

📊 Scores

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

AI Review from SafeReviewer

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📋 Summary

This paper provides a comprehensive review of the integration of artificial intelligence (AI) and machine learning (ML) techniques into the field of rock mechanics. The authors meticulously trace the historical development of AI applications in this domain, starting from early methods like backpropagation and support vector machines to more recent advancements such as deep learning frameworks, including convolutional neural networks (CNNs) and transformer architectures. The paper highlights the growing use of physics-informed neural networks (PINNs) and graph-based learning, which aim to bridge the gap between data-driven models and the underlying physical principles of rock mechanics. The core contribution of this work lies in its synthesis of the current state of AI in rock engineering, demonstrating how these techniques are being used to address traditional challenges such as anisotropy, discontinuities, and multiphysics coupling. The authors discuss the application of AI in various aspects of rock mechanics, including data-driven estimation of rock properties, image-based modeling and fracture detection, AI-assisted constitutive modeling, and simulation. They also explore the use of AI in practical engineering applications such as rock mass classification, rockburst and geohazard prediction, tunneling, boring operations, and slope stability analysis. The paper emphasizes the potential of AI to transform rock mechanics from an empirical discipline to a data-driven, intelligence-enabled science. However, it also acknowledges the existing challenges, particularly in data quality, model generalization, and interpretability. The authors advocate for the development of standardized datasets and interdisciplinary collaboration to overcome these limitations. Overall, this review serves as a valuable resource for researchers and practitioners in the field, providing a broad overview of the current state of AI in rock mechanics and highlighting both its potential and its challenges.

✅ Strengths

I found several strengths in this paper. First, the paper provides a comprehensive and well-structured review of the literature, effectively synthesizing a wide range of research on the application of AI and ML in rock mechanics. The authors successfully trace the historical development of AI in this field, providing a clear understanding of how the field has evolved over time. The inclusion of a detailed table of contents and the clear organization of the paper into sections made it easy to follow and understand the various aspects of the topic. The paper also effectively highlights the growing use of physics-informed neural networks (PINNs) and graph-based learning, demonstrating how these techniques are being used to bridge the gap between data-driven models and the underlying physical principles of rock mechanics. Furthermore, the paper provides a balanced perspective, acknowledging both the potential and the challenges of applying AI in this domain. The authors effectively discuss the limitations of current AI models, particularly in terms of data quality, model generalization, and interpretability, which is crucial for a realistic assessment of the field. The paper also emphasizes the need for standardized datasets and interdisciplinary collaboration, which are essential for further progress in this area. Finally, the paper's focus on the practical applications of AI in rock mechanics, such as rock mass classification, rockburst prediction, and slope stability analysis, demonstrates the real-world relevance of this research.

❌ Weaknesses

While this paper offers a valuable synthesis of AI applications in rock mechanics, I have identified several weaknesses that warrant attention. First, the paper's discussion of physics-informed neural networks (PINNs) lacks sufficient depth. While the authors mention PINNs and cite a relevant paper, they do not provide a detailed explanation of how PINNs are specifically applied to solve partial differential equations (PDEs) in rock mechanics, such as those related to stress, strain, or fluid flow. The paper also fails to discuss the specific loss functions used in PINNs for rock mechanics, such as those incorporating stress equilibrium or constitutive relationships. Furthermore, the paper does not address the numerical challenges associated with training PINNs, such as convergence and stability issues, which are particularly relevant for complex 3D problems. This lack of detail limits the reader's understanding of the practical application of PINNs in this field. My verification confirms that while PINNs are mentioned, the paper does not delve into the specifics of their application in rock mechanics, such as the types of PDEs solved, specific loss functions, or numerical challenges. Second, the paper's discussion of graph-based learning is also limited. While the authors mention graph neural networks (GNNs) and their use in modeling discontinuities and fracture networks, they do not provide a detailed explanation of how GNNs are applied to model the complex geometry and connectivity of fracture networks. The paper also fails to discuss the specific types of graph neural networks used, such as graph convolutional networks (GCNs) or graph attention networks (GATs), and their suitability for different types of fracture network data. Additionally, the paper does not address the challenges of applying GNNs to large-scale fracture networks, such as computational cost and memory limitations. My verification confirms that while GNNs are mentioned, the paper lacks detail on their specific application in modeling fracture networks, including the types of GNNs used and the challenges of applying them to large-scale networks. Third, the paper lacks a detailed discussion of the practical challenges of implementing AI models in real-world rock mechanics projects. While the authors acknowledge data limitations, they do not delve into the specifics of data acquisition, preprocessing, and quality control in rock mechanics. The paper also fails to discuss the computational resources required for training and deploying AI models in this field, and the challenges of integrating AI models with existing engineering workflows and software. My verification confirms that while data limitations are mentioned, the paper lacks a detailed discussion on the practical challenges of data acquisition, preprocessing, computational resources, and integration with existing workflows. Fourth, the paper does not provide a detailed analysis of the limitations of current AI models in rock mechanics. While the authors acknowledge issues of data quality, model generalization, and interpretability, they do not delve into the specific challenges of applying AI to complex geological systems, such as the presence of noise and outliers in the data, the difficulty of capturing the underlying physics of rock behavior, and the need for more robust and reliable models. My verification confirms that while limitations are mentioned, a detailed analysis of the specific challenges of applying AI to complex geological systems is missing. Fifth, the paper lacks a dedicated section or detailed discussion on the ethical implications of using AI in rock mechanics. The paper does not address issues such as bias in data and models, the potential for misuse of AI, and the impact of AI on the workforce. My verification confirms the absence of a discussion on ethical implications. Finally, the paper does not provide a detailed discussion of the challenges of deploying AI models in real-world rock mechanics projects. While the authors mention the need for interdisciplinary collaboration, they do not delve into the practical aspects of integrating AI models into existing engineering workflows and software. My verification confirms the lack of a detailed discussion on the practical challenges of deploying AI models in real-world projects. These weaknesses, while not invalidating the paper's overall contribution, highlight areas where further research and discussion are needed.

💡 Suggestions

To address the identified weaknesses, I recommend several concrete improvements. First, the paper should include a more detailed discussion of physics-informed neural networks (PINNs), specifically focusing on their application in rock mechanics. This should include a discussion of the specific types of PDEs that PINNs are used to solve in this field, such as those related to stress, strain, or fluid flow. The paper should also provide examples of how PINNs have been used to model specific rock mechanics problems, such as slope stability or tunneling. Furthermore, the paper should discuss the specific loss functions used in PINNs for rock mechanics, such as those incorporating stress equilibrium or constitutive relationships. The paper should also address the numerical challenges associated with training PINNs, such as convergence and stability issues, and discuss how these challenges can be mitigated. Second, the paper should expand its discussion of graph-based learning, providing more detail on how graph neural networks (GNNs) are applied to model fracture networks in rock mechanics. This should include a discussion of the specific types of graph neural networks used, such as graph convolutional networks (GCNs) or graph attention networks (GATs), and how they are adapted to handle the complex geometry and connectivity of fracture networks. The paper should also discuss the challenges of applying GNNs to large-scale fracture networks, such as computational cost and memory limitations, and how these challenges can be addressed. Furthermore, the paper should provide examples of how GNNs have been used to predict the mechanical behavior of fractured rock masses, such as deformation or failure. Third, the paper should include a more detailed discussion of the practical challenges of implementing AI models in real-world rock mechanics projects. This should include a discussion of the specific requirements for data acquisition, such as the types of sensors needed, the sampling rates, and the data storage and management. The paper should also discuss the challenges of data preprocessing, such as noise reduction, data cleaning, and feature extraction, and how these challenges can be addressed. Furthermore, the paper should discuss the computational resources required for training and deploying AI models in rock mechanics, such as the type of hardware, the software environment, and the training time. The paper should also address the challenges of integrating AI models with existing engineering workflows and software, and how these challenges can be overcome. Fourth, the paper should include a more detailed analysis of the limitations of current AI models in rock mechanics. This should include a discussion of the specific challenges of applying AI to complex geological systems, such as the presence of noise and outliers in the data, the difficulty of capturing the underlying physics of rock behavior, and the need for more robust and reliable models. Fifth, the paper should include a dedicated section or detailed discussion on the ethical implications of using AI in rock mechanics. This should address issues such as bias in data and models, the potential for misuse of AI, and the impact of AI on the workforce. Finally, the paper should provide a more detailed discussion of the challenges of deploying AI models in real-world rock mechanics projects. This should include a discussion of the practical aspects of integrating AI models into existing engineering workflows and software, and the need for interdisciplinary collaboration.

❓ Questions

Based on my analysis, I have several questions that could further clarify the paper's findings and implications. First, regarding the application of physics-informed neural networks (PINNs), what specific types of partial differential equations (PDEs) are most commonly solved using PINNs in rock mechanics, and what are the typical boundary and initial conditions used in these applications? Second, concerning graph-based learning, what are the most effective strategies for handling the heterogeneity and anisotropy of rock masses when using graph neural networks (GNNs) to model fracture networks, and how do these strategies impact the accuracy and reliability of the models? Third, in terms of data quality, what are the most common sources of noise and uncertainty in rock mechanics data, and what are the most effective techniques for mitigating these issues during data preprocessing? Fourth, regarding model interpretability, what are the most promising techniques for explaining the predictions of complex AI models in rock mechanics, and how can these techniques be used to build trust and confidence in AI-driven decision-making? Fifth, concerning the practical implementation of AI models, what are the most significant barriers to the adoption of AI in real-world rock mechanics projects, and what are the most effective strategies for overcoming these barriers? Finally, regarding the ethical implications of AI, what are the most pressing ethical concerns related to the use of AI in rock mechanics, and what steps can be taken to ensure that AI is used responsibly and ethically in this field?

📊 Scores

Soundness:3.0
Presentation:3.0
Contribution:2.75
Rating: 6.25

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