Reliability Assessment of Long-Service Gravity Dams Based on Historical Water Level Monitoring Data

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript presents a novel and systematic approach for the reliability assessment of long-service gravity dams. The core contribution is the pioneering use of a Generalized Extreme Value (GEV) distribution model. It is derived from historical operational data to accurately quantify systemic extreme loads. Integrating this model into the RSM-MC framework allows for a precise, quantitative risk assessment that reflects the effective control of reservoir operations. This work offers a new paradigm for operational risk recalibration in hydraulic structures. Some specific comments:

    

(1) Section 5.2 should briefly state the K-S test's limitation for extreme-tail fitting, improving rigor and linking to the engineering validation in Section 5.3.

(2) For verification, Section 5.3 must explicitly list the three design water levels (100-yr, 1000-yr, 10000-yr) used to inversely derive the Log-P3 model parameters.

(3)Acronyms (GEV, RSM, MC, etc.) must be defined at their first mention in both the Abstract and the Introduction to adhere to professional standards.

(4) Figures 5 and 6 use "Return Period (T)" on the x-axis, which relates directly to "Exceedance Probability (P)" in Section 2.1. To ensure conceptual clarity, Section 5.2 must explicitly state the mathematical relationship T = 1/P when discussing the figures.

(5) All table captions (Table 1-6) must be self-explanatory, allowing the reader to understand the table's main content just by reading the title.

(6)The structural model uses a linear elastic assumption. Since Cracking at the Plinth Heel is a tensile limit state, Section 5.5 must briefly discuss the potential influence of this linear elastic assumption on the calculated tensile stress results.

(7) Key quantitative results should be embedded (e.g., the order of magnitude difference in failure probability between the Log-P3 and GEV models) in the Abstract and Conclusion sections.

 

Author Response

Dear Editor and Reviewer 1:

Thank you for your careful review and positive evaluation of our manuscript. We are delighted to receive the "minor revision" decision. Your suggestions are vital for enhancing the scientific rigor and engineering application of our paper.

We have carefully revised the manuscript according to all your comments. Our detailed responses are provided below:

 

(1) Comment1: Section 5.2 should briefly state the K-S test's limitation for extreme-tail fitting, improving rigor and linking to the engineering validation in Section 5.3.

Author Response1: Thank you for this suggestion. We have added a discussion at the end of Section 5.2 regarding the limitation of the K-S test for fitting the extreme tail, and we explicitly link this to the engineering validation in Section 5.3, thereby improving the rigor of our argumentation.

Location of Change1: See Section 5.2 in the main text.

 

(2) Comment2: For verification, Section 5.3 must explicitly list the three design water levels (100-yr, 1000-yr, 10000-yr) used to inversely derive the Log-P3 model parameters.

Author Response2: This is a very accurate suggestion. We have explicitly listed the specific values of the three design water levels (235.50 m, 238.00 m, and 240.25 m) used for the inverse derivation of the Log-P3 model parameters in Section 5.3.

Location of Change2: See Section 5.3 in the main text.

 

(3) Comment3: Acronyms (GEV, RSM, MC, etc.) must be defined at their first mention in both the Abstract and the Introduction to adhere to professional standards.

Author Response3: We agree with this comment. We have ensured that all key acronyms (e.g., RSM, MC) are defined with their full names upon their first mention in both the Abstract and the Introduction.

Location of Change3: See Abstract.

 

(4) Comment4: Figures 5 and 6 use "Return Period (T)" on the x-axis, which relates directly to "Exceedance Probability (P)" in Section 2.1. To ensure conceptual clarity, Section 5.2 must explicitly state the mathematical relationship T = 1/P when discussing the figures.

Author Response4: Thank you for pointing out the need for this conceptual clarity. When introducing Figure 5 in Section 5.2, we have explicitly stated the mathematical relationship between Return Period (T) and Exceedance Probability (P) as T=1/P.

Location of Change4: See Section 5.2 in the main text.

 

(5) Comment5: All table captions (Table 1-6) must be self-explanatory, allowing the reader to understand the table's main content just by reading the title.

Author Response5: We have revised the captions of all tables (Table 1 through Table 6) to be more descriptive and self-explanatory, which improves the readability of the manuscript.

Location of Change5: See Captions of Table 1-6.

 

(6) Comment6: The structural model uses a linear elastic assumption. Since Cracking at the Plinth Heel is a tensile limit state, Section 5.5 must briefly discuss the potential influence of this linear elastic assumption on the calculated tensile stress results.

Author Response6: This is a critical engineering point. We have added a discussion in Section 5.5 regarding the influence of the linear elastic assumption on tensile stress at the dam heel, explaining that it is a conservative and standard practice in preliminary hydraulic structure reliability assessment.

Location of Change6: See Section 5.5 in the main text.

 

(7) Comment7: Key quantitative results should be embedded (e.g., the order of magnitude difference in failure probability between the Log-P3 and GEV models) in the Abstract and Conclusion sections.

Author Response7: We agree that this enhances the paper's persuasiveness. We have embedded the key quantitative results (e.g., the nearly six-fold reduction in failure probability) in the Abstract, Section 5.5, and the Conclusion to highlight the core value of our findings.

Location of Change7: See Abstract, Section 5.5, and Conclusion.

 

We believe that the quality and rigor of the manuscript have been significantly improved by these revisions.

We thank Reviewer 1 once again for the valuable time dedicated to our work. We look forward to your further review.

 

Sincerely,

Best Regards,

Yuzhou Lu

Author Contact: luyuzhou118@163.com

Reviewer 2 Report

Comments and Suggestions for Authors

This paper presents a valuable and technically sound contribution to the reliability assessment of long-service gravity dams, introducing a framework that integrates historical water level data with a Generalized Extreme Value (GEV) model and a Response Surface Method–Monte Carlo simulation approach. The methodology is innovative and well-supported by statistical analysis and engineering application. However, some aspects could be improved to enhance the clarity and robustness of the work, as follows.

• The assumption of stationarity in operational procedures over several decades should be discussed more explicitly, as changes in management or climate could affect the validity of the extrapolated risk estimates.
• The decision to fix the shape parameter in bootstrap resampling improves stability but may introduce bias; a more detailed justification or sensitivity analysis would be beneficial.
• Additionally, the simplification of material behavior to linear elasticity and the limited treatment of uncertainty in other input parameters may affect the comprehensiveness of the reliability analysis.
• Expanding the discussion on model validation and practical implications for dam safety management would further strengthen the manuscript.

Overall, the work is interesting and deserves publication after minor revision.

Author Response

Dear Editor and Reviewer 2:

Thank you for your positive affirmation and constructive comments on our manuscript. We are delighted to receive the "minor revision" decision. Your suggestions are crucial for strengthening the robustness of our study, particularly concerning the statistical model and engineering application.

We have carefully revised the manuscript according to all your comments. Our detailed responses are provided below:

 

(1) Comment1: The assumption of stationarity in operational procedures over several decades should be discussed more explicitly, as changes in management or climate could affect the validity of the extrapolated risk estimates.

Author Response1: We agree with this comment. We have explicitly added a discussion on the stationarity assumption of long-term operational procedures in Section 5.3. We acknowledge the potential non-stationarity risks from climate change or management shifts and commit to incorporating non-stationary analysis in future work.

Location of Change1: See Section 5.3 in the main text.

 

(2) Comment2: The decision to fix the shape parameter in bootstrap resampling improves stability but may introduce bias; a more detailed justification or sensitivity analysis would be beneficial.

Author Response2: Thank you for highlighting this statistical detail. We have added a more detailed justification in Section 2.2. We explicitly acknowledge that fixing the shape parameter may introduce bias but emphasize that for small-sample extreme extrapolation, ensuring engineering stability and reducing high variance is the primary requirement, thus justifying this balancing strategy.

Location of Change2: See Section 2.2 in the main text.

 

(3) Comment3: Additionally, the simplification of material behavior to linear elasticity and the limited treatment of uncertainty in other input parameters may affect the comprehensiveness of the reliability analysis.

Author Response3: Your comment is highly pertinent. We agree that the treatment of uncertainty for other random parameters could be more comprehensive. We have added a discussion at the end of Section 5.5, noting that while the current work focuses on water level load uncertainty, we commit to a more comprehensive inclusion of material and geometric parameter uncertainty in future research.

Location of Change3: See Section 5.5 in the main text.

 

(4) Comment4: Expanding the discussion on model validation and practical implications for dam safety management would further strengthen the manuscript.

Author Response4: We have added new content in the Conclusion (Section 6), elaborating on the practical implications of our GEV-RSM-MC framework for dam safety management and operational decision support, such as guiding risk prevention and resource allocation through operational recalibration.

Location of Change4: See Conclusion (Section 6).

 

We believe that the quality of the manuscript has been elevated to the required publication standard through these revisions aimed at improving both rigor and practical value.

We thank Reviewer 2 once again for the valuable guidance provided for our work. We look forward to your further review.

 

Sincerely,

Best Regards,

Yuzhou Lu

Author Contact: luyuzhou118@163.com