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This paper reviews the integration of AI methods into rock mechanics, tracing developments from early neural networks and SVMs to CNNs/transformers, generative models, and physics-informed approaches. It organizes the field into four pillars: (i) data-driven estimation of rock properties (strength, stiffness, velocities) (Section 2); (ii) image-based modeling and fracture detection, including 3D reconstruction and segmentation from images and point clouds (Section 3); (iii) AI-assisted constitutive modeling and physics-aware solvers (PINNs, hybrids) (Section 4); and (iv) applications, including rock mass classification, rockburst/geohazard prediction, tunneling/boring operations, slope stability, and emerging use cases (Section 5). Section 6 synthesizes challenges—data scarcity and standardization, interpretability/trust, computational efficiency, workflow integration, and lifecycle maintenance—and Section 7 offers an outlook toward hybrid physics–data models and digital-twin workflows. The paper claims a bibliometric survey across 17 journals (Section 1) and references an appendix with datasets and tooling (Appendix A.1, A.3).
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)
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:
Key weaknesses:
Recommendations:
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This paper presents a comprehensive review of the application of artificial intelligence (AI) and machine learning (ML) techniques in the field of rock mechanics. The authors meticulously trace the evolution of AI methodologies, from early neural networks to modern deep learning architectures, and their integration into various aspects of rock engineering. The paper is structured around the flow of information in rock engineering projects, beginning with data acquisition and progressing through property estimation, fracture detection, constitutive modeling, and finally, to practical applications. The authors highlight the increasing adoption of AI/ML methods for tasks such as rock mass classification, rockburst prediction, and tunneling operations, demonstrating the potential of these techniques to enhance efficiency and accuracy in geotechnical analysis. The paper also acknowledges the limitations of current approaches, particularly concerning data quality, model interpretability, and generalization. The authors advocate for the development of standardized datasets and interdisciplinary collaboration to address these challenges. The paper's significance lies in its attempt to synthesize a large body of literature into a coherent narrative, providing a valuable resource for researchers and practitioners in the field. While the paper does not present novel research findings, it offers a well-organized and insightful overview of the current state of AI/ML in rock mechanics, highlighting both the achievements and the areas that require further investigation. The authors' emphasis on the need for standardized datasets and interdisciplinary collaboration underscores the importance of addressing the practical challenges that hinder the widespread adoption of AI/ML in this domain. The paper's focus on the flow of information in rock engineering projects provides a useful framework for understanding how AI/ML can be integrated into the various stages of analysis and design. Overall, the paper serves as a useful starting point for researchers and practitioners interested in exploring the application of AI/ML in rock mechanics, providing a broad overview of the field and highlighting the key challenges and opportunities.
The paper's primary strength lies in its comprehensive and well-structured review of AI and ML applications in rock mechanics. The authors have successfully synthesized a large body of literature into a coherent and accessible narrative. I found the organization of the paper particularly effective, as it follows the natural flow of information in rock engineering projects, making it easy to understand how AI/ML can be applied at different stages. The paper's attempt to bridge the gap between theoretical AI/ML concepts and practical rock mechanics problems is commendable. The authors provide concrete examples of how different AI/ML techniques, such as CNNs for image-based modeling and LSTMs for time-series prediction, are used in various rock engineering tasks. The inclusion of a forward-looking perspective is another strength, as it highlights the potential of emerging techniques like physics-informed neural networks (PINNs) and graph-based learning to address current limitations. The paper also acknowledges the challenges associated with data quality, model interpretability, and generalization, demonstrating a balanced and critical approach. The authors' call for standardized datasets and interdisciplinary collaboration is a crucial contribution, as it highlights the practical steps needed to advance the field. The paper's emphasis on the need for transparent and reproducible AI workflows is also important, as it promotes best practices in research and development. The inclusion of a case study in Appendix A.2, while brief, provides a concrete example of how deep learning can be applied to rockburst prediction, further enhancing the paper's practical relevance. Finally, the paper's attempt to provide a comprehensive list of references, although not exhaustive, demonstrates the authors' commitment to providing a thorough overview of the field. Overall, the paper's strengths lie in its comprehensive coverage, clear organization, and insightful discussion of both the achievements and the challenges in applying AI/ML to rock mechanics.
Despite its strengths, the paper exhibits several weaknesses that warrant careful consideration. A recurring theme across multiple reviews is the lack of a comprehensive literature search. While the paper includes a substantial number of references, several reviewers have pointed out specific omissions, particularly in the area of physics-informed neural networks (PINNs). For instance, Reviewer 4 provided a list of relevant PINN publications that are not cited in the paper, indicating a gap in the coverage of this specific area. This lack of exhaustiveness undermines the paper's claim to be a comprehensive review and raises concerns about the authors' awareness of the latest developments in the field. The paper's structure, while generally well-organized, is also a point of concern. Reviewer 1 notes that the paper's organization, while following the flow of information in rock engineering, might not be the most intuitive for a review paper, as it mixes methodological descriptions with application areas. This structure makes it difficult to get a clear overview of the different AI/ML techniques used in rock mechanics and their specific applications. Furthermore, the paper's discussion of interpretability is limited. While the authors acknowledge the "black box" nature of some AI models and the need for interpretability, they do not delve into specific techniques for achieving this in the rock mechanics context. Reviewer 1 suggests discussing methods like LIME or SHAP, which are not mentioned in the paper. This lack of detail limits the paper's practical value, as interpretability is a crucial requirement for the adoption of AI/ML models in engineering practice. The paper's treatment of uncertainty quantification is also superficial. While the authors mention the need for uncertainty quantification, they do not provide a detailed discussion of specific techniques or their application in rock mechanics. Reviewer 1 suggests discussing Bayesian neural networks or ensemble methods, which are not explored in depth. This omission is significant, as uncertainty quantification is essential for reliable decision-making in geotechnical engineering. The paper's discussion of data quality is also limited. While the authors acknowledge the challenges associated with data scarcity and quality, they do not provide a detailed analysis of the types of data used in rock mechanics or the specific challenges associated with each type. Reviewer 1 suggests discussing the impact of noise, outliers, and missing data, which are not adequately addressed in the paper. This lack of detail limits the paper's practical value, as data quality is a critical factor in the success of AI/ML models. The paper's discussion of model generalization is also insufficient. While the authors acknowledge the risk of overfitting, they do not provide a detailed analysis of the factors that affect generalization or the specific techniques used to mitigate this risk. Reviewer 1 suggests discussing cross-validation and domain adaptation, which are not explored in depth. This omission is significant, as model generalization is essential for the reliable application of AI/ML models to new datasets. Finally, the paper's lack of a dedicated discussion on the limitations of current AI/ML approaches is a significant weakness. While the authors acknowledge some limitations, they do not provide a comprehensive analysis of the potential pitfalls of relying solely on data-driven methods. Reviewer 1 suggests discussing the limitations of current AI/ML approaches and the need for hybrid approaches, which are not adequately addressed in the paper. This omission limits the paper's critical perspective and its ability to guide future research in the field. In summary, while the paper provides a useful overview of AI/ML in rock mechanics, its lack of exhaustiveness, limited discussion of interpretability, uncertainty quantification, data quality, model generalization, and limitations significantly weaken its overall contribution.
To address the identified weaknesses, I recommend several concrete improvements. First, the authors should conduct a more comprehensive literature search, particularly focusing on physics-informed neural networks (PINNs) and other hybrid approaches. This would involve including the specific references provided by Reviewer 4, as well as other relevant publications in this area. The authors should also expand their search to include journals and conferences that focus on the intersection of AI/ML and physics-based modeling. This would ensure that the paper provides a more complete and up-to-date overview of the field. Second, the authors should restructure the paper to improve its clarity and accessibility. I suggest organizing the paper by AI/ML technique, rather than by the flow of information in rock engineering projects. This would make it easier for readers to understand the different techniques and their specific applications. For example, there could be separate sections on neural networks, support vector machines, and physics-informed neural networks, each with its own subsections on specific applications in rock mechanics. This would also allow for a more focused discussion of the strengths and limitations of each technique. Third, the authors should significantly expand their discussion of interpretability. This should include a detailed explanation of techniques such as LIME and SHAP, and their application in the rock mechanics context. The authors should also discuss the importance of feature importance analysis and sensitivity analysis in understanding the behavior of AI/ML models. This would provide readers with practical guidance on how to interpret the results of AI/ML models and make informed decisions. Fourth, the authors should provide a more detailed discussion of uncertainty quantification. This should include a detailed explanation of techniques such as Bayesian neural networks and ensemble methods, and their application in rock mechanics. The authors should also discuss the importance of confidence intervals and prediction intervals in assessing the reliability of AI/ML models. This would provide readers with a better understanding of the limitations of AI/ML models and how to account for uncertainty in their predictions. Fifth, the authors should significantly expand their discussion of data quality. This should include a detailed analysis of the types of data used in rock mechanics, as well as the specific challenges associated with each type. The authors should also discuss the impact of noise, outliers, and missing data on the performance of AI/ML models, and the techniques used to mitigate these issues. This would provide readers with a better understanding of the importance of data quality and how to ensure the reliability of their AI/ML models. Sixth, the authors should provide a more detailed discussion of model generalization. This should include a detailed analysis of the factors that affect generalization, such as model complexity, data size, and feature engineering. The authors should also discuss the techniques used to improve generalization, such as cross-validation and domain adaptation. This would provide readers with a better understanding of how to ensure that their AI/ML models generalize well to new datasets. Finally, the authors should include a dedicated section on the limitations of current AI/ML approaches. This should include a critical analysis of the potential pitfalls of relying solely on data-driven methods, and the need for hybrid approaches that combine data-driven models with physics-based understanding. This would provide readers with a more balanced perspective on the strengths and weaknesses of AI/ML in rock mechanics and guide future research in the field. By addressing these weaknesses, the authors can significantly improve the quality and impact of their paper.
Several questions arise from my analysis of the paper that I believe warrant further consideration. First, given the paper's focus on the application of AI/ML in rock mechanics, how can we ensure that these models are not merely fitting to noise or spurious correlations in the data, but are actually capturing the underlying physical processes? This question is particularly relevant given the complexity of rock mechanics and the potential for data quality issues. Second, the paper acknowledges the "black box" nature of some AI models. However, how can we develop more interpretable AI models that provide insights into the underlying mechanisms of rock behavior, rather than just providing predictions? This question is crucial for building trust in AI/ML models and ensuring their adoption in engineering practice. Third, the paper highlights the need for standardized datasets. However, what specific steps can be taken to create such datasets, and how can we ensure that they are representative of the diverse range of rock mechanics problems? This question is important for addressing the data scarcity issues that hinder the development of robust AI/ML models. Fourth, the paper discusses the potential of physics-informed neural networks (PINNs). However, how can we effectively integrate physical constraints into neural networks, and what are the limitations of this approach? This question is important for understanding the potential and challenges of using PINNs in rock mechanics. Fifth, the paper mentions the use of AI/ML for tasks such as rock mass classification and rockburst prediction. However, how can we validate the performance of these models in real-world scenarios, and what are the risks associated with relying solely on AI/ML for these critical tasks? This question is important for ensuring the safe and reliable application of AI/ML in rock engineering. Sixth, the paper does not provide a detailed discussion of the computational cost of training and deploying AI/ML models. How can we develop more efficient AI/ML algorithms that can be used in resource-constrained environments? This question is important for making AI/ML more accessible to practitioners in the field. Finally, the paper does not explicitly address the ethical implications of using AI/ML in rock mechanics. What are the potential ethical concerns associated with the use of AI/ML in this field, and how can we ensure that these technologies are used responsibly? These questions are important for guiding future research and ensuring the responsible development and application of AI/ML in rock mechanics.