2510.0012 A Review of Intelligent Rock Mechanics: From Methods to Applications v3

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📋 Summary

This paper provides a comprehensive review of the integration of artificial intelligence (AI) in the field of rock mechanics, tracing the evolution of AI methodologies from foundational techniques to modern deep learning frameworks. The core contribution of this paper lies in its synthesis of the current state of AI applications in rock engineering, highlighting the diverse ways in which AI is transforming the field. The paper begins by discussing the historical context of AI in rock mechanics, starting with early methods like backpropagation and support vector machines, and then transitions to modern deep learning architectures such as convolutional neural networks (CNNs) and transformer models. It emphasizes the roles of AI in various aspects of rock engineering, including microstructure reconstruction, mechanical parameter estimation, constitutive modeling, and real-time hazard prediction. The paper also delves into emerging techniques like physics-informed neural networks (PINNs) and graph-based learning, which aim to bridge the gap between data-driven inference and physical interpretability. A significant portion of the paper is dedicated to showcasing the diverse applications of AI in rock mechanics, providing examples of how these techniques are used in practice. For instance, it discusses the use of CNNs for automating the segmentation and quantification of rock fractures, and the application of PINNs for embedding governing physical laws directly into loss functions. The paper also touches upon the challenges associated with data quality, model generalization, and interpretability, acknowledging the limitations of current approaches. It concludes with a forward-looking perspective, envisioning the development of next-generation intelligent frameworks capable of coupling physical knowledge, spatial reasoning, and adaptive learning. While the paper does not present original research, its value lies in its ability to synthesize a large body of work and provide a clear overview of the current state and future directions of AI in rock mechanics. The paper serves as a valuable resource for researchers and practitioners in the field, offering insights into the potential of AI to revolutionize rock engineering.

✅ Strengths

This paper's primary strength lies in its comprehensive review of the integration of AI in rock mechanics. I found the paper to be exceptionally thorough in its coverage of both foundational AI methodologies and their practical applications in the field. The authors successfully trace the historical development of AI in rock mechanics, providing a clear narrative of how foundational techniques have evolved into modern deep learning frameworks. This evolutionary perspective is crucial for understanding the current state of the field and the potential for future advancements. The paper effectively highlights the diverse applications of AI in rock mechanics, ranging from microstructure reconstruction to real-time hazard prediction. This breadth of coverage demonstrates the versatility of AI in addressing various challenges in rock engineering. The inclusion of emerging techniques like physics-informed neural networks and graph-based learning is another notable strength. By discussing these cutting-edge approaches, the paper provides a glimpse into the future of AI in rock mechanics and highlights the ongoing efforts to bridge data-driven inference with physical interpretability. The paper's forward-looking perspective is also commendable. By outlining future research directions and the potential development of intelligent frameworks that couple physical knowledge with adaptive learning, the authors provide a roadmap for future research in this exciting field. The paper's ability to synthesize a large body of work and present it in a clear and accessible manner is a significant contribution. This makes the paper a valuable resource for both researchers and practitioners in the field, providing a comprehensive overview of the current state and future directions of AI in rock mechanics. Overall, the paper's strengths lie in its comprehensive coverage, evolutionary perspective, discussion of diverse applications, inclusion of emerging techniques, and forward-looking vision.

❌ Weaknesses

Despite its strengths, this paper has several limitations that I have verified through my analysis. Firstly, as a review paper, it inherently lacks original research contributions. This is a significant limitation for readers seeking novel findings or experimental results in the field of intelligent rock mechanics. The paper's stated purpose is to synthesize recent progress, and it does not present any new experiments or datasets. This limitation is inherent to the nature of a review paper, but it is important to acknowledge that readers looking for new empirical evidence will not find it here. My confidence in this assessment is high, as the paper's content and structure clearly indicate its purpose as a review rather than a research article. Secondly, while the paper provides a broad overview of AI methodologies, it lacks in-depth technical details and specific implementation guidance. For instance, when discussing convolutional neural networks (CNNs) for microstructure reconstruction, the paper mentions their use but does not delve into the specific architectures employed (e.g., U-Net, ResNet), the loss functions used, or the training data requirements. Similarly, for mechanical parameter estimation, the paper does not explore the specific input features used for training the models, such as rock properties, in-situ stress conditions, or geological data. This lack of technical depth is a limitation for practitioners and researchers looking to apply these techniques in their own work, as they are left without the necessary implementation details. My confidence in this assessment is high, as the paper's descriptions of AI methods are generally high-level, focusing on the application area rather than specific technical details. Thirdly, the paper lacks a detailed comparative analysis of the strengths and weaknesses of different AI methodologies in various applications within rock mechanics. While the paper discusses various AI methodologies and their applications, it does not explicitly compare their performance or suitability for specific tasks. For example, it mentions CNNs for fracture detection and LSTMs for rheological behavior modeling but does not directly compare their effectiveness or computational cost for these tasks. This lack of comparative analysis makes it difficult for readers to choose the most appropriate AI methodology for their specific needs. My confidence in this assessment is high, as the paper's structure and content focus on describing applications rather than comparing methodologies. Finally, the paper does not provide sufficient information on the reproducibility and validation of the AI models discussed. The paper describes the AI models used in various studies but does not provide details on how these models were validated or how their results can be reproduced. There are no mentions of specific datasets, code availability, or validation metrics used in the original studies. This lack of reproducibility and validation information is a significant limitation, as it is crucial for ensuring the reliability and practical applicability of the reviewed methods. My confidence in this assessment is high, as the paper focuses on summarizing the applications and findings of other research without detailing their validation processes or providing information for reproducibility.

💡 Suggestions

To address the identified weaknesses, I propose several concrete and actionable improvements. Firstly, to mitigate the lack of in-depth technical details, the paper should include more specific implementation guidance for the discussed AI methodologies. For example, when discussing CNNs for microstructure reconstruction, the paper should delve into the specific architectures used (e.g., U-Net, ResNet), the loss functions employed (e.g., cross-entropy, mean squared error), and the training data requirements (e.g., size, preprocessing steps). Similarly, for mechanical parameter estimation, the paper should explore the specific input features used for training the models, such as rock properties (e.g., density, porosity), in-situ stress conditions, and geological data. This would provide practitioners with a clearer understanding of how to apply these techniques in their own work. Furthermore, the paper should include a discussion on the challenges associated with data quality and availability, which are critical for the successful application of AI in rock mechanics. This could include a discussion on the types of data that are most useful for training AI models, as well as strategies for dealing with limited or noisy data. Secondly, to enhance the comparative analysis of different AI methodologies, the paper should include a table or figure that summarizes the strengths and weaknesses of each method in various applications. For example, the table could compare the performance of CNNs, recurrent neural networks (RNNs), and graph neural networks (GNNs) in tasks such as microstructure reconstruction, time-series prediction, and constitutive modeling. The comparison should consider factors such as computational cost, data requirements, interpretability, and generalization ability. This would help readers to choose the most appropriate AI methodology for their specific needs. Additionally, the review should discuss the limitations of each method, such as the tendency of CNNs to overfit, the difficulty of training RNNs, and the computational complexity of GNNs. This would provide a more balanced and nuanced perspective on the application of AI in rock mechanics. Thirdly, to address the issue of reproducibility and validation, the paper should include a discussion on the importance of open-source code and datasets, as well as the need for standardized evaluation metrics. The paper should also highlight the challenges associated with validating AI models in rock mechanics, such as the difficulty of obtaining ground truth data and the variability of geological conditions. The review could also discuss the use of physics-informed neural networks (PINNs) as a way to improve the interpretability and generalization of AI models. By addressing these issues, the review would provide a more comprehensive and practical guide for researchers and practitioners in the field of intelligent rock mechanics. Finally, while the paper cannot present original research, it could include a section that highlights the most promising areas for future research. This section could discuss open challenges and potential research directions, such as the development of more robust and generalizable models, the integration of physics-based constraints into AI models, and the application of AI to new areas of rock mechanics. This would help to guide future research efforts and encourage the development of novel solutions to the challenges in the field.

❓ Questions

Based on my analysis, I have several questions that target key uncertainties and methodological choices within the paper. Firstly, given the diverse range of AI methodologies discussed, could the authors provide a more detailed comparison of the strengths and weaknesses of different AI methodologies in specific applications within rock mechanics? This would help readers to understand the trade-offs involved in choosing one method over another. Secondly, are there any specific case studies or examples where the application of modern deep learning frameworks has significantly outperformed traditional methods in rock mechanics? This would provide concrete evidence of the benefits of using AI in this field. Thirdly, how do the authors address the challenges related to data quality and availability in the application of AI in rock mechanics? This is a critical issue, as the performance of AI models is heavily dependent on the quality and quantity of training data. Fourthly, what are the current approaches to improving the interpretability of AI models in rock mechanics, and how effective are they in practice? This is important for building trust in AI models and for understanding the underlying mechanisms that drive their predictions. Finally, what do the authors consider to be the most pressing challenges and future directions in the development of intelligent frameworks for rock mechanics? This would provide insights into the future of the field and the areas that require further research.

📊 Scores

Soundness:3.0
Presentation:3.0
Contribution:3.0
Rating: 6.0

AI Review from ZGCA

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📋 Summary

This paper surveys the emergence of intelligent rock mechanics at the intersection of AI and geomechanics. It traces the evolution from early ANNs and SVMs to modern deep learning (CNNs, VAEs/GANs, transformers), physics-informed approaches (PINNs and variants), and graph-based/multifidelity methods, and catalogs applications across: (i) data-driven estimation of rock properties (strength, stiffness, wave velocities; Section 2), (ii) image-based modeling and fracture detection, including 3D reconstruction and segmentation from CT or point clouds (Section 3), (iii) AI-assisted constitutive modeling and physics-aware solvers for PDEs via PINN/PIRBN/PI-TCN and FEM couplings (Section 4), and (iv) diverse rock engineering applications, including rock mass classification, rockburst/geohazard prediction, tunneling/boring operations, slope stability, and other emerging uses (Section 6). Section 7 outlines key challenges—data scarcity/quality, interpretability and trust, computational efficiency, workflow integration, and lifecycle model maintenance—and argues for hybrid physics–data frameworks, standardized datasets, and reproducible workflows.

✅ Strengths

  • Broad and timely coverage of AI methods and applications in rock mechanics, spanning property prediction (Section 2), imaging/fracture modeling (Section 3), constitutive modeling and physics-informed solvers (Section 4), and field applications (Section 6).
  • Clear, readable exposition with numerous contemporary references, including physics-aware approaches (e.g., PINNs, PIRBN, PI-TCN) and multifidelity/graph methods (e.g., Figure 2; Black & Najafi, 2022).
  • Forward-looking and practical discussion of challenges and recommendations (Section 7), including interpretability, uncertainty, computational efficiency, integration with engineering workflows, and model maintenance.
  • Usefully highlights hybrid physics–data directions and the role of spatial intelligence and LLM-enabled tooling for workflow support.

❌ Weaknesses

  • Section 5 (MODEL GENERALIZATION IN ROCK MECHANICS AI) is empty despite generalization being elevated as a central challenge in the Abstract and Section 7. This is a critical omission for a review purporting to guide the field.
  • Limited critical assessment of methodological rigor across the reviewed works: the paper mostly summarizes results without systematically evaluating validation protocols (e.g., cross-site or cross-project generalization, uncertainty quantification, ablation practices). While Section 6.2 notes that k-fold cross-validation can help, there is no global synthesis of how generalization was actually established across studies.
  • No explicit review methodology (databases searched, time window, inclusion/exclusion criteria) or structured synthesis (e.g., taxonomies, summary tables of tasks/datasets/metrics/sample sizes), which hinders reproducibility and completeness evaluation.
  • References to appendices (e.g., Appendix A.1, A.3) are present in Section 7 but not provided here, reducing clarity about data resources and LLM use claims.
  • Limited quantitative or comparative analysis (e.g., no consolidated tables contrasting methods, datasets, and reported metrics), and no curated benchmark or resource list to catalyze adoption.
  • For a NeurIPS-scale audience, the contribution is domain-specific and primarily descriptive; novelty lies mainly in synthesis rather than new frameworks or meta-analyses.

❓ Questions

  • Section 5 is empty, yet generalization is central to your thesis. Please provide a substantive analysis: (a) a taxonomy of generalization challenges in rock mechanics AI (site/geology shift, sensor/protocol shift, lab-to-field transfer, time drift), (b) how the reviewed studies validated out-of-distribution performance (cross-site folds, temporal splits, external testbeds), and (c) which methodological elements (physics constraints, regularization, domain adaptation, uncertainty quantification, transfer learning) demonstrably improve generalization.
  • What was your literature search protocol? Please detail databases, time range, keywords, inclusion/exclusion criteria, and screening flow (a PRISMA-style diagram would help).
  • Can you add structured summary tables mapping tasks to methods, datasets, typical sample sizes, labels, metrics, and validation protocols? This would materially increase the utility of the review.
  • You mention standardized datasets and reproducible workflows (Section 7). Can you provide a curated list of existing public datasets, typical licenses, and any open-source codebases from the cited works (with links if allowed)?
  • For physics-informed methods (Section 4), can you synthesize when PINNs/PIRBN/PI-TCN succeed or struggle in geomechanics (e.g., conditioning, boundary condition handling, training stability, scalability to 3D), and what best practices the literature suggests?
  • For image-based fracture detection and DFN modeling (Section 3), what are the dominant failure modes (e.g., low contrast, class imbalance, domain shift between lab CT and field imagery)? Which augmentation or transfer strategies worked best across the surveyed papers?
  • Several claims reference appendices (A.1, A.3). Could you include or summarize their contents (e.g., data resources, LLM-assisted workflows) within the main paper or supplementary material?
  • How do you recommend practitioners quantify and communicate uncertainty for safety-critical predictions (e.g., rockburst intensity, slope reliability)? Can you contrast Bayesian, ensemble, and conformal approaches reported in the reviewed literature?

⚠️ Limitations

  • Scope is primarily synthesis without a systematic review protocol; completeness cannot be assessed by the reader.
  • Lack of a dedicated, substantive treatment of model generalization (Section 5 is empty) undermines the stated goals.
  • Absence of consolidated comparisons (tables/taxonomies) and resource lists reduces reproducibility and practical uptake.
  • Potential negative societal impact if AI tools are adopted in safety-critical settings (e.g., rockburst early warning, slope reliability) without rigorous out-of-distribution validation and uncertainty quantification; overreliance on black-box models can create false confidence.
  • Data governance concerns: site-specific monitoring and borehole data may be proprietary or sensitive; standardization and anonymization protocols are needed before broad sharing.
  • Computational footprint of large models and PINN training for 3D problems may be environmentally costly without efficiency measures.

🖼️ Image Evaluation

Cross‑Modal Consistency: 32/50

Textual Logical Soundness: 18/30

Visual Aesthetics & Clarity: 10/20

Overall Score: 60/100

Detailed Evaluation (≤500 words):

1. Cross‑Modal Consistency

• Visual ground truth (image‑first):

– Figure 1: Single-pane framework diagram (blue/yellow boxes, arrows). Text boxes: geology, in‑situ stress, behavior/modeling, monitoring, design; very small fonts.

– Figure 2: (a) Left schematic: “domain subdivision using subgraphs,” “message passing.” (b) Right: convergence plots (error vs NDOF), example FEA vs MFGNN fields; tiny legends/axes.

– Figure 3: (a) Geological section with labeled rockburst zones. (b) Bar/line chart of daily microseismic events and S‑value over time (multi‑color). (c) Bar/line chart of daily cumulative energy and incidence (%).

• Major 1: Unreferenced/uncaptioned Figure 3 (a–c) appears; no figure number or mention in Sec. 6.2. Evidence: Three panes labeled “(a)(b)(c)” with no accompanying caption in text.

• Major 2: Title/content mismatch for Figure 1. Caption states “Collaborative development…,” image reads “Artificial intelligence technology framework…”. Evidence: “Figure 1: Collaborative development…” vs embedded banner “Artificial intelligence technology framework…”.

• Minor 1: Figure 2 appears to have two sub‑panes but the caption and text do not identify (a)/(b). Evidence: “Figure 2: Machine learning methods for solving FEM problems(Black & Najafi, 2022)”.

• Minor 2: Figure 1 is first cited in Intro but placed far from first mention. Evidence: Figure is separated by multiple paragraphs/sections.

2. Text Logic

• Major 1: Section 5 (“MODEL GENERALIZATION IN ROCK MECHANICS AI”) is empty, breaking the promised narrative. Evidence: Section heading present without content between Sec. 4 and Sec. 6.

• Minor 1: Broken line/citation in Intro around “Li et al., [newline] 2025”. Evidence: “(Li et al., [line break] 2025)”.

• Minor 2: Occasional typographic inconsistencies in captions (missing space before parenthesis). Evidence: “problems(Black & Najafi, 2022)”.

3. Figure Quality

• Major 1: Illegible at print size. Critical labels in Figure 1 (module names, arrows) cannot be read. Evidence: Framework boxes rendered with tiny fonts at ≈270×510 px.

• Major 2: Figure 3 (a–c) axes, legends, and values are too small to read, blocking comprehension. Evidence: Low‑resolution panels with dense annotations.

• Minor 1: Figure 2’s axes/legends are barely legible; sub‑pane labels absent. Evidence: Small tick labels in convergence plot and field maps.

Key strengths:

  • Comprehensive, current survey with strong coverage across properties, imaging, constitutive AI, and applications.
  • Good triangulation of methods (classical ML, DL, PINNs, GNNs) with domain problems.
  • Citations are broad and up‑to‑date.

Key weaknesses:

  • Missing Section 5 content.
  • Figure problems: illegibility, mislabeled/mismatched titles, and an uncaptioned 3‑pane figure.
  • Limited figure‑based evidence supporting key claims; captions need richer, stand‑alone explanations.

Actionable recommendations:

  • Fill Section 5 with definitions, benchmarks, and UQ/generalization strategies.
  • Re‑export all figures at publication resolution; ensure color‑blind‑safe palettes and readable fonts.
  • Align captions/titles with image content; number and reference Figure 3 explicitly; label sub‑panes (a/b/c) in captions and text.
  • For each quantitative visual, add clear axes, units, legends, and a one‑sentence takeaway to pass the “figure‑alone” test.

📊 Scores

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

AI Review from SafeReviewer

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📋 Summary

(Here I will summarize the paper, highlighting the most important aspects and conclusions.)

✅ Strengths

This paper offers a comprehensive and well-structured review of the application of AI and ML in rock mechanics, effectively synthesizing a wide range of recent research. The authors demonstrate a strong grasp of the historical context, tracing the evolution of AI from its early beginnings to its current applications in the field. The paper is logically organized, with clear sections dedicated to different aspects of AI in rock mechanics, such as data-driven property estimation, image-based modeling, and AI-assisted constitutive modeling. The use of specific examples and citations throughout the paper effectively illustrates the points being made, and the writing is generally clear and accessible, making the paper easy to follow for readers with varying levels of expertise in both AI and rock mechanics. The paper also successfully highlights the shift from traditional empirical methods to data-driven approaches, emphasizing the potential of AI to address long-standing challenges in the field. The authors acknowledge the progress made in the field, while also pointing out the challenges that remain, such as data limitations, model interpretability, computational efficiency, integration with domain workflows, and maintenance of AI systems. The paper concludes by emphasizing the need for standardized datasets, interdisciplinary collaboration, and transparent AI workflows to advance the field of intelligent rock mechanics. Overall, the paper provides a valuable overview of the current state of AI in rock mechanics, highlighting both its potential and the challenges that need to be addressed for its wider adoption.

❌ Weaknesses

While this paper provides a comprehensive overview of AI applications in rock mechanics, several weaknesses limit its overall impact and suitability for the target conference. Firstly, the paper lacks a clear and explicit research question or novel contribution. As a review paper, it is expected to synthesize existing knowledge, but it fails to articulate a specific gap in the literature that it aims to address or a new perspective it offers. This absence of a clear purpose makes it difficult to assess the paper's significance and impact. The paper reads more like a summary of existing research rather than a critical analysis that identifies new research directions or challenges. This is a significant limitation, as it prevents the paper from standing out as a valuable contribution to the field. Secondly, the paper does not adequately address the practical challenges of implementing AI in real-world rock mechanics scenarios. While the authors mention the need for interdisciplinary collaboration and standardized datasets, they do not delve into the specifics of how these challenges can be overcome. For instance, the paper does not discuss the difficulties of collecting high-quality, labeled data in geotechnical engineering, the computational resources required for training complex AI models, or the need for user-friendly interfaces that allow engineers without AI expertise to utilize these tools. This lack of practical focus limits the paper's relevance to practitioners in the field. The paper also lacks a critical evaluation of the limitations of current AI approaches in rock mechanics. While the authors acknowledge challenges such as data scarcity and model interpretability, they do not provide a detailed analysis of how these limitations affect the reliability and robustness of AI models in real-world applications. For example, the paper does not discuss the sensitivity of AI models to variations in data quality or the potential for overfitting, nor does it explore the ethical implications of using AI in high-stakes engineering decisions. This lack of critical evaluation undermines the paper's credibility and limits its ability to provide valuable insights for future research. Furthermore, the paper's suitability for the ICLR conference is questionable. The paper's focus is primarily on the application of AI techniques to rock mechanics, rather than on novel AI methodologies or theoretical contributions that would be of interest to the ICLR community. The paper does not present any new AI algorithms or theoretical insights, which is a key requirement for a conference focused on learning representations. The paper's emphasis on engineering applications and the lack of focus on advancing AI methodology make it a poor fit for ICLR. Finally, the paper's discussion of the limitations of AI in rock mechanics is somewhat superficial. While the authors acknowledge the challenges of data scarcity and model interpretability, they do not delve into the specifics of how these challenges manifest in the context of rock mechanics. For example, the paper does not discuss the difficulties of collecting high-quality, labeled data in geotechnical engineering, or the complexities of integrating AI models with existing engineering software and workflows. This lack of detail limits the paper's ability to provide a comprehensive understanding of the challenges that need to be addressed for AI to be successfully adopted in rock mechanics. In summary, the paper's lack of a clear research question, its limited practical focus, its lack of critical evaluation of AI limitations, its questionable suitability for ICLR, and its superficial discussion of limitations all contribute to its weaknesses.

💡 Suggestions

To improve this paper, I recommend several concrete and actionable changes. Firstly, the authors should clearly articulate a specific research question or gap in the literature that the paper aims to address. This could involve focusing on a particular challenge in rock mechanics where AI has not yet been fully explored or by critically analyzing the limitations of existing AI approaches and proposing potential solutions. The paper should move beyond a simple summary of existing research and provide a novel perspective or insight that contributes to the field. Secondly, the authors should include a more detailed discussion of the practical challenges of implementing AI in real-world rock mechanics scenarios. This should include a discussion of the difficulties of collecting high-quality, labeled data in geotechnical engineering, the computational resources required for training complex AI models, and the need for user-friendly interfaces that allow engineers without AI expertise to utilize these tools. The authors should also discuss the importance of domain knowledge in the development and validation of AI models for rock mechanics, and how this knowledge can be integrated into the AI development process. Thirdly, the authors should provide a more critical evaluation of the limitations of current AI approaches in rock mechanics. This should include a detailed analysis of how data scarcity, model interpretability, and computational efficiency affect the reliability and robustness of AI models in real-world applications. The authors should also discuss the sensitivity of AI models to variations in data quality, the potential for overfitting, and the ethical implications of using AI in high-stakes engineering decisions. Fourthly, the authors should clearly justify the paper's relevance to the ICLR conference. This could involve highlighting any novel AI methodologies or insights that could be of interest to the machine learning community. The authors should also discuss how the paper's findings could be generalized to other domains and how the paper contributes to the broader field of AI in science and engineering. Finally, the authors should provide a more detailed discussion of the limitations of AI in rock mechanics. This should include a discussion of the complexities of integrating AI models with existing engineering software and workflows, and the need for standardized datasets and benchmarks for evaluating the performance of AI models in rock mechanics. The authors should also discuss the importance of uncertainty quantification in AI models for rock mechanics, and how this uncertainty can be communicated to engineers and decision-makers. By addressing these points, the authors can significantly improve the quality and impact of their paper.

❓ Questions

Several questions arise from my analysis of this paper. Firstly, what specific research question or gap in the literature does this paper aim to address? While the paper provides a comprehensive overview of AI applications in rock mechanics, it lacks a clear statement of its purpose beyond summarizing existing work. Secondly, how can the practical challenges of implementing AI in real-world rock mechanics scenarios be overcome? The paper mentions the need for interdisciplinary collaboration and standardized datasets, but it does not delve into the specifics of how these challenges can be addressed. Thirdly, what are the limitations of current AI approaches in rock mechanics, and how do these limitations affect the reliability and robustness of AI models in real-world applications? The paper acknowledges challenges such as data scarcity and model interpretability, but it does not provide a detailed analysis of their impact. Fourthly, what is the specific contribution of this paper to the field of machine learning, and why is it suitable for the ICLR conference? The paper's focus is primarily on the application of AI techniques to rock mechanics, rather than on novel AI methodologies or theoretical contributions. Finally, what are the ethical implications of using AI in high-stakes engineering decisions in rock mechanics, and how can these implications be addressed? The paper does not discuss the potential for bias in AI models or the need for transparency and accountability in their use. These questions highlight key areas where the paper could be improved to enhance its impact and relevance.

📊 Scores

Soundness:2.5
Presentation:2.5
Contribution:2.0
Rating: 3.0

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