2510.0012 A Review of Intelligent Rock Mechanics: From Methods to Applications v4

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📋 Summary

This paper provides a comprehensive review of the integration of artificial intelligence (AI) methodologies into the field of rock mechanics, tracing the evolution from early AI techniques to modern deep learning frameworks. It highlights the applications of AI in various aspects of rock mechanics, including microstructure reconstruction, mechanical parameter estimation, constitutive modeling, and real-time hazard prediction. The paper discusses emerging techniques like physics-informed neural networks (PINNs) and graph-based learning, which aim to bridge data-driven inference with physical interpretability. Despite the progress, the paper acknowledges challenges such as data quality, model generalization, and interpretability, emphasizing the need for standardized datasets and interdisciplinary collaboration. The conclusion points towards the future development of intelligent frameworks that couple physical knowledge with spatial reasoning and adaptive learning, aiming to transition rock mechanics from empirical modeling to autonomous systems. The paper's significance lies in its potential to transform rock mechanics from an empirical science to a data-driven, intelligent science, opening up new avenues for research and application. However, the paper could benefit from a more detailed discussion on the practical implementation challenges of these AI methodologies in real-world engineering projects, including computational resources, specialized expertise, and integration with existing workflows.

✅ Strengths

The paper is well-structured and written in clear, concise language, making it accessible to readers across various disciplines. It provides a thorough and comprehensive review of the state-of-the-art in intelligent rock mechanics, covering a wide range of AI methodologies and their applications in the field. The paper effectively traces the historical development of AI in rock mechanics, from early techniques to modern deep learning frameworks, which is valuable for understanding the current state of the art. It highlights the versatility and potential of AI in transforming rock mechanics from an empirical science to a data-driven, intelligent science, opening up new avenues for research and application. 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. This is a crucial aspect of AI in rock mechanics, as it ensures that the models are not only accurate but also physically meaningful. The paper's emphasis on the potential of AI to revolutionize rock mechanics and its forward-looking perspective on developing next-generation intelligent frameworks capable of coupling physical knowledge with spatial reasoning and adaptive learning are particularly noteworthy. These points underscore the paper's contribution to the field and its vision for the future.

❌ Weaknesses

While the paper provides a comprehensive overview of the applications of AI in rock mechanics, it lacks a critical analysis of the limitations and challenges associated with these methods. For instance, the paper could delve deeper into the issues of data scarcity, quality, and the generalizability of AI models in rock mechanics. Specifically, the paper does not adequately address the heterogeneity and anisotropy inherent in rock materials, which can significantly impact the performance of AI models trained on limited datasets. The paper mentions data limitations in the 'CHALLENGES AND FUTURE DISCUSSIONS' section, stating, 'High-quality, representative data are the fuel for AI models, yet in rock mechanics such data can be scarce or hard to share.' However, it does not provide a detailed examination of how these specific material properties affect model performance, which is a critical aspect of rock mechanics. This omission could lead to an overestimation of the current capabilities of AI in this field, and a more nuanced discussion would strengthen the paper's critical analysis. I am confident in this assessment based on the paper's content and the reviewer's comments, and I believe this issue has a substantial impact on the paper's conclusions regarding the practical applicability of AI in rock mechanics.

Although the paper mentions the need for interdisciplinary collaboration, it does not provide specific examples or case studies where such collaborations have led to significant advancements in the field. The paper states, 'Interdisciplinary collaboration is essential for developing standardized datasets and improving model interpretability and trust.' However, it lacks concrete instances of successful interdisciplinary projects, which would strengthen the argument and provide a roadmap for future research. The absence of such examples makes it difficult to assess the practical benefits and challenges of interdisciplinary collaboration in this context. I am highly confident in this assessment, as the paper's emphasis on collaboration without illustrative examples is a clear gap in its content. This issue has a substantial impact on the paper's ability to inspire and guide future collaborative efforts.

The paper could benefit from a more detailed discussion on the ethical implications of using AI in rock mechanics, especially concerning automated decision-making in critical infrastructure projects. The paper does not address the potential risks associated with relying on AI models for critical decisions, such as slope stability analysis or tunnel design, without proper validation and understanding of the model's limitations. It also does not discuss the issue of bias in AI models, which can arise from biased training data or flawed algorithms. The lack of ethical considerations is a significant oversight, as it could lead to misuse or over-reliance on AI models in high-stakes scenarios. I am highly confident in this assessment, as the ethical implications of AI in engineering are increasingly important and the paper's omission is a notable gap. This issue has a substantial impact on the paper's comprehensiveness and its relevance to real-world applications.

While the review paper provides a comprehensive overview of the current state of intelligent rock mechanics, it could benefit from a more detailed discussion on the practical implementation challenges of these AI methodologies in real-world engineering projects. For example, the paper should discuss the computational resources required for training and deploying AI models, the need for specialized expertise, and the challenges in integrating AI models into existing engineering workflows. The paper mentions computational efficiency in the 'CHALLENGES AND FUTURE DISCUSSIONS' section, stating, 'While many AI models can run predictions in milliseconds once trained, the training process or the integration with large-scale simulations can be computationally demanding.' However, it does not provide a detailed analysis of the specific computational resources needed or the practical steps required to integrate AI models into engineering workflows. Additionally, the paper could address the issue of model interpretability and how to communicate AI-based decisions to engineers and stakeholders. I am highly confident in this assessment, as the practical implementation of AI in engineering is a critical aspect that is not sufficiently covered. This issue has a substantial impact on the paper's utility for practitioners and researchers looking to apply AI in rock mechanics.

💡 Suggestions

To enhance the critical analysis of AI methodologies, the authors should include a dedicated section that discusses the limitations of current approaches in detail. This section should address the challenges of data scarcity and quality, specifically highlighting the difficulties in obtaining large, diverse, and labeled datasets for rock mechanics. The authors should also discuss the impact of rock heterogeneity and anisotropy on model performance, and how these factors can lead to overfitting or poor generalization. Furthermore, the authors should explore the limitations of different AI models, such as the black-box nature of deep learning models, and the need for techniques to improve model interpretability and trustworthiness. This section should also include a discussion on the sensitivity of AI models to noise and outliers in the data, and how this can affect the reliability of predictions. Concrete examples of how these limitations manifest in rock mechanics applications would be beneficial. I believe these suggestions are actionable and would significantly improve the paper's critical analysis.

To strengthen the discussion on interdisciplinary collaboration, the authors should provide specific examples of successful projects where AI experts and rock mechanics experts have worked together. These examples should highlight the specific contributions of each discipline and how their collaboration has led to novel solutions. For instance, the authors could discuss a case study where AI experts developed a new data-driven model for rock mass classification, which was then validated and refined by rock mechanics experts. The authors should also discuss the challenges of interdisciplinary collaboration, such as the differences in terminology and methodology between the two fields, and how these challenges can be overcome. This section should also emphasize the importance of clear communication and mutual respect between AI experts and rock mechanics experts. Furthermore, the authors could suggest frameworks or platforms that facilitate such collaborations. I think these suggestions are realistic and would provide valuable insights for researchers and practitioners interested in interdisciplinary work.

To address the ethical implications of using AI in rock mechanics, the authors should include a section that discusses the potential risks and challenges associated with automated decision-making. This section should address the issue of bias in AI models, and how this can lead to unfair or discriminatory outcomes. The authors should also discuss the need for transparency and accountability in AI-based decision-making, and how to ensure that AI models are used responsibly. Furthermore, the authors should explore the potential impact of AI on the job market, and how to mitigate any negative consequences. This section should also emphasize the importance of human oversight and validation in AI-based decision-making, and how to ensure that AI models are used as a tool to augment human expertise, rather than replace it. Concrete examples of ethical dilemmas in rock mechanics applications would be beneficial. I believe these suggestions are essential for a forward-looking and responsible review of AI in rock mechanics.

To address the practical implementation challenges, the authors should include a detailed discussion on the computational resources required for training and deploying AI models, the need for specialized expertise, and the challenges in integrating AI models into existing engineering workflows. The authors should also discuss the issue of model interpretability and how to communicate AI-based decisions to engineers and stakeholders. This could involve exploring techniques for model explainability, such as feature importance analysis or saliency maps, and discussing how these techniques can be used to build trust in AI models. Additionally, the authors could provide guidelines or best practices for integrating AI models into engineering workflows, including the necessary steps for validation and verification. I think these suggestions are practical and would make the paper more useful for practitioners and researchers looking to implement AI in rock mechanics.

❓ Questions

1. Could the authors provide more detailed examples of how AI has been used to solve specific problems in rock mechanics, such as predicting rock bursts or optimizing tunnel design? This would help readers better understand the practical applications of the methodologies discussed in the paper.

2. The paper mentions the use of physics-informed neural networks (PINNs) and graph-based learning in rock mechanics. Could the authors elaborate on the specific advantages and limitations of these techniques compared to traditional machine learning methods? This would provide a clearer understanding of the trade-offs involved in choosing different AI methodologies.

3. How do the authors envision the future development of intelligent rock mechanics? Are there any specific research directions or technologies that they believe hold particular promise for advancing the field? This would provide valuable insights for researchers and practitioners interested in contributing to this area.

4. Could the authors discuss the challenges of obtaining sufficient labeled data for supervised learning in rock mechanics, and suggest methods to overcome these challenges? For example, how can techniques like transfer learning, data augmentation, or physics-based simulations be effectively utilized to generate synthetic data?

5. How do the authors plan to address the issue of model interpretability and trustworthiness in AI-based decision-making for critical infrastructure projects? Are there specific techniques or frameworks that can be used to ensure that AI models are transparent and their predictions are reliable?

📊 Scores

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

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📋 Summary

This paper reviews the integration of AI with rock mechanics, tracing a trajectory from early ANNs and SVMs to modern deep learning, PINNs, and graph-based approaches. It surveys data-driven estimation of rock properties (Section 2), image-based modeling and fracture detection (Section 3), AI-assisted constitutive modeling and simulation (Section 4), and applications in rock engineering including rock mass classification, rockburst prediction, tunneling and boring, and slope stability (Section 6). It highlights challenges in data quality, generalization, interpretability, and workflow integration, and calls for hybrid physics–data models, uncertainty quantification, and reproducible AI pipelines (Section 7). The Introduction also suggests a Turing-Test-inspired categorization framework for AI in rock mechanics, and the Conclusion argues for the field’s transition toward intelligent, autonomous systems.

✅ Strengths

  • Broad, up-to-date synthesis across core subareas: property estimation (e.g., UCS, moduli, velocities), imaging and fracture analysis, constitutive modeling/PINNs, and multiple applications (Sections 2–6).
  • Clear emphasis on physics-informed and hybrid approaches to improve interpretability and robustness in high-consequence engineering (Sections 1 and 7).
  • Useful aggregation of practical application cases in rock mass classification, rockburst prediction, tunneling operations, and slope stability (Section 6), with many recent references.
  • Thoughtful identification of bottlenecks: data scarcity, generalization, interpretability, computational cost, workflow integration, and maintenance of AI systems (Section 7).
  • Bridges AI architectures to geomechanics problems and motivates needs like UQ and standardized datasets (Abstract; Sections 4 and 7).

❌ Weaknesses

  • No explicit review protocol: the paper mentions a bibliometric overview of 17 journals but provides no search strategy, inclusion/exclusion criteria, or quantitative coverage statistics. This limits reproducibility of the survey’s scope (Section 1).
  • Despite positioning itself as a blueprint for next-generation frameworks, it offers no concrete evaluation or reporting standards (metrics, statistical testing, calibration, UQ reporting, dataset documentation). This gap is especially problematic given the emphasis on safety-critical decisions (Sections 4 and 7).
  • Promised conceptual contributions are not developed: the Turing-Test-inspired categorization framework is mentioned (Introduction) but not defined; Section 5 (Model Generalization) is present as a header with no content.
  • Lacks a structured taxonomy/summary tables that map tasks to representative datasets, baselines, and metrics (e.g., UCS: RMSE/R2; fracture segmentation: mIoU/F1; rockburst: AUROC/F1; slope reliability: failure probability error/calibration; PINNs: residual norms/energy conservation).
  • Minimal discussion of datasets and benchmarks beyond narrative citations; no proposed benchmark suite or dataset curation strategy, despite repeatedly noting data scarcity and the need for standardization (Sections 2, 3, 6, and 7).
  • References to appendices (A.1, A.3) are made but not provided; there are truncated sentences (e.g., Introduction around Li et al., 2025) and an empty Section 5, which hurt presentation and completeness.

❓ Questions

  • Please detail the review methodology: which databases and time windows were searched, what keywords and inclusion/exclusion criteria were used, and how many papers per task area were ultimately included? If a bibliometric overview of 17 journals was performed, what was the protocol and the quantitative outcomes (e.g., counts, trends, co-citation)?
  • You mention a Turing-Test-inspired categorization of AI in rock mechanics (Introduction). Can you specify this framework formally (levels of cognitive resemblance, abstraction axes) and apply it consistently across Sections 2–6?
  • Section 5 (Model Generalization) has no content. What principles and empirical strategies do you propose for generalization across sites, lithologies, and sensors (e.g., domain adaptation, covariate shift diagnostics, transfer learning, physics priors)?
  • Given your emphasis on high-consequence decisions, can you propose concrete evaluation and reporting standards: task-specific metrics, statistical tests, uncertainty calibration (e.g., ECE/NLL), ablation protocols, and error characterization under distribution shift?
  • Can you provide a table mapping canonical tasks to representative datasets and metrics (e.g., UCS/Udemy-like repos, fracture CT sets, tunnel deformation logs, microseismic rockburst datasets), noting data availability, licenses, and recommended train/validation/test splits?
  • For PINNs and physics-informed surrogates (Section 4), what quantitative criteria do you recommend to assess physical fidelity (e.g., PDE residual norms, boundary-condition satisfaction, conservation/energy metrics), computational cost, and convergence reliability?
  • Several sections refer to data augmentation (e.g., CTGANs, generative models). How do you recommend validating synthetic data for safety-critical use (distributional tests, physics constraints, coverage measures), and how should such data be disclosed and documented?
  • You discuss maintenance of AI systems (Section 7). Can you propose a lifecycle protocol (drift detection, periodic retraining, performance monitoring thresholds, human-in-the-loop checks) suitable for rock engineering deployments?

⚠️ Limitations

  • The survey is narrative and lacks a reproducible selection protocol, which may bias coverage and omit important counterexamples.
  • No proposed benchmark suite or datasets table; without standardized tasks, metrics, and splits, community progress and comparability remain limited.
  • No concrete evaluation/reporting standards are provided despite the emphasis on uncertainty and interpretability (e.g., calibration metrics, significance testing, error bars, and ablation requirements).
  • Potential negative societal impacts include overreliance on black-box models in safety-critical contexts, deployment of inadequately validated systems due to data scarcity or synthetic data leakage, and domain shift leading to uncalibrated predictions across geologies. Clear guidance on validation, uncertainty, and human oversight is needed.
  • Environmental and cost impacts of training large models (PINNs in 3D, ensembles) are not quantified; guidance on efficiency, model compression, and HPC/cloud sustainability would be valuable.

🖼️ 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:2
Significance:2
Soundness:2
Presentation:2
Contribution:2
Rating: 3

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📋 Summary

This paper provides a comprehensive review of the application of artificial intelligence (AI) and machine learning (ML) techniques in rock mechanics, aiming to bridge the gap between traditional empirical methods and data-driven approaches. The authors meticulously trace the evolution of AI in this field, starting from early methods like backpropagation and support vector machines to modern deep learning architectures such as convolutional neural networks (CNNs) and transformers. The paper highlights the growing integration of AI in various aspects of rock engineering, including rock mass classification, hazard prediction, and constitutive modeling. A significant portion of the paper is dedicated to showcasing specific case studies and examples where AI has been successfully applied to solve practical problems in rock mechanics. For instance, the authors discuss the use of CNNs for microseismic event detection in mines and physics-informed neural networks (PINNs) for solving partial differential equations related to rock behavior. The paper also acknowledges the challenges associated with these advanced techniques, such as data quality, model interpretability, and the need for interdisciplinary collaboration. The authors conclude by emphasizing the potential of AI to transform rock mechanics from an empirical science to a more data-driven and intelligent discipline, capable of handling the inherent uncertainties and complexities of geological materials. Overall, the paper 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 achievements and limitations.

✅ Strengths

One of the key strengths of this paper is its thorough and well-structured literature review. The authors have done an excellent job of tracing the historical development of AI applications in rock mechanics, starting from early methods like backpropagation and support vector machines to the latest advancements in deep learning. This historical perspective is crucial for understanding the current state of the field and the trajectory of future research. The paper also provides a comprehensive overview of the various AI techniques that have been applied to different aspects of rock mechanics, including rock mass classification, hazard prediction, and constitutive modeling. The authors effectively use specific examples and case studies to illustrate the practical applications of these techniques. For instance, the discussion of CNNs for microseismic event detection in mines and PINNs for solving partial differential equations related to rock behavior demonstrates the real-world impact of AI in this field. The paper is well-written and easy to follow, making it accessible to a broad audience, including those who may not be experts in AI. The authors also acknowledge the limitations of current AI approaches, such as data quality issues and model interpretability, which shows a balanced and critical perspective. The paper's emphasis on the potential of AI to transform rock mechanics into a more data-driven and intelligent discipline is a significant contribution, as it highlights the importance of continued research and development in this area.

❌ Weaknesses

Despite its strengths, the paper has several notable weaknesses that need to be addressed. Firstly, the paper's structure and clarity could be improved. While the paper provides a comprehensive overview of AI applications in rock mechanics, the sheer volume of information presented can be overwhelming. For example, the 'Data-Driven Estimation of Rock Properties' section (Section 2) is dense with technical details and specific examples, making it difficult to follow. Similarly, the 'AI-Assisted Constitutive Modeling and Simulation' section (Section 4) introduces numerous AI techniques and their applications without sufficient explanation of the underlying principles. This lack of clarity is further compounded by the absence of a dedicated 'Limitations' section, which would have provided a more focused discussion of the challenges and shortcomings of current AI approaches in rock mechanics. The paper does mention some limitations, such as data quality issues and model interpretability, but these are scattered throughout the text rather than being consolidated in one section. This makes it challenging for readers to grasp the full scope of the limitations and their implications for the field. Secondly, the paper's discussion of specific AI techniques is often too brief and lacks the necessary depth to be truly informative. For instance, the mention of 'physics-informed neural networks (PINNs)' in Section 4 is not accompanied by a detailed explanation of how these networks are informed by physics or how they differ from standard neural networks. Similarly, the use of 'long short-term memory (LSTM) networks' in Section 6.2 is not explained in terms of their architecture or how they capture temporal dependencies in the data. This lack of detailed explanations makes it difficult for readers who are not familiar with these techniques to fully understand the paper's content. Thirdly, the paper's claim of being a 'review' is somewhat misleading, as it reads more like a literature survey. A review paper typically provides a critical assessment of the existing literature, identifying gaps, inconsistencies, and areas for future research. While the paper does an excellent job of summarizing the current state of AI in rock mechanics, it lacks a critical evaluation of the strengths and weaknesses of different AI approaches. For example, the paper does not discuss the limitations of using CNNs for microseismic event detection or the challenges of training PINNs for complex rock behaviors. This critical assessment is essential for advancing the field and guiding future research. Fourthly, the paper's discussion of the practical implications of AI in rock mechanics is limited. While the authors mention the potential of AI to transform rock mechanics into a more data-driven and intelligent discipline, they do not provide concrete examples of how these advanced techniques can be integrated into existing engineering workflows. For instance, the paper does not discuss how the complex models presented in Sections 2 and 4 can be used by practicing engineers in real-world projects. This lack of practical context makes it difficult to assess the real-world impact of the research. Finally, the paper's discussion of the challenges associated with AI in rock mechanics is not as thorough as it could be. While the authors acknowledge issues such as data quality and model interpretability, they do not delve into the specific challenges of applying AI to the unique problems of rock mechanics. For example, the paper does not discuss the difficulties of acquiring high-quality, representative data in rock mechanics or the challenges of interpreting complex AI models in the context of geological materials. Addressing these challenges in more detail would provide a more balanced and realistic assessment of the current state of AI in rock mechanics.

💡 Suggestions

To address the identified weaknesses, I recommend several concrete and actionable improvements. Firstly, the paper's structure should be reorganized to enhance clarity and readability. A more modular approach, with each section dedicated to a specific AI technique and its application in rock mechanics, would be beneficial. For example, one section could focus on the use of CNNs for image-based rock classification, detailing the specific architectures used, the datasets employed, and the performance metrics achieved. Another section could delve into the application of recurrent neural networks (RNNs) for time-series prediction in rock burst forecasting, explaining the data preprocessing steps, the model training procedures, and the validation methods. This would allow readers to better understand the specific contributions of each AI technique and their relevance to different aspects of rock mechanics. Secondly, the paper should include a dedicated 'Limitations' section that provides a comprehensive discussion of the challenges and shortcomings of current AI approaches in rock mechanics. This section should address issues such as data quality, model interpretability, and the need for interdisciplinary collaboration. For example, the authors could discuss the difficulties of acquiring high-quality, representative data in rock mechanics and the impact of data scarcity on model performance. They could also elaborate on the challenges of interpreting complex AI models and the need for techniques that can provide insights into the decision-making process of these models. Thirdly, the paper should provide more detailed explanations of specific AI techniques. For instance, when discussing physics-informed neural networks (PINNs), the authors should explain how these networks incorporate physical laws into their architecture and how this differs from standard neural networks. Similarly, when discussing long short-term memory (LSTM) networks, the authors should explain their architecture and how they capture temporal dependencies in the data. This would make the paper more accessible to readers who are not experts in AI. Fourthly, the paper should include a more critical assessment of the existing literature. This could involve comparing the performance of different AI techniques on similar tasks, analyzing the limitations of current approaches, and identifying areas where further research is needed. For example, the authors could compare the performance of CNNs and traditional image processing techniques for rock fracture detection, highlighting the strengths and weaknesses of each approach. They could also discuss the challenges of applying AI to the unique problems of rock mechanics, such as the variability of rock properties and the complexity of geological processes. Finally, the paper should provide more concrete examples of how AI can be integrated into existing engineering workflows. This could involve discussing specific case studies where AI has been used to solve practical problems in rock mechanics, such as rock mass classification, hazard prediction, and constitutive modeling. The authors should also address the practical challenges of implementing AI in real-world projects, such as the need for high-quality data, the computational resources required for training complex models, and the expertise needed to interpret the results. By addressing these practical considerations, the paper would be more relevant to both researchers and practitioners in the field.

❓ Questions

1. Given the rapid advancements in AI, what specific steps can be taken to ensure that the data used in rock mechanics research is of high quality and representative of the diverse geological conditions encountered in practice? 2. How can the interpretability of complex AI models, such as deep neural networks and physics-informed neural networks, be improved to make them more transparent and trustworthy for engineering applications? 3. What are the key challenges in integrating AI techniques into existing engineering workflows, and how can these challenges be overcome to facilitate the adoption of AI in rock mechanics? 4. In what ways can interdisciplinary collaboration between AI experts and rock mechanics specialists be fostered to address the unique challenges of applying AI to geological materials? 5. How can the limitations of current AI approaches, such as data scarcity and model overfitting, be mitigated to ensure the robustness and reliability of AI models in rock mechanics?

Rating: 3.25

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