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Article
Peer-Review Record

Back Analysis of Surrounding Rock Parameters of Large-Span Arch Cover Station Based on GP-DE Algorithm

Appl. Sci. 2022, 12(24), 12590; https://doi.org/10.3390/app122412590
by Fu Zheng 1, Annan Jiang 1,*, Xinping Guo 1, Qinghua Min 2 and Qingfeng Yin 2
Reviewer 1: Anonymous
Reviewer 2:
Appl. Sci. 2022, 12(24), 12590; https://doi.org/10.3390/app122412590
Submission received: 1 November 2022 / Revised: 28 November 2022 / Accepted: 29 November 2022 / Published: 8 December 2022
(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

Round 1

Reviewer 1 Report

1.     Why difficult to characterize and classify the rock mass in case of large span. If large or small, the insitu parameters are determined from the exploration.

2.     Detail description about the project and excavation sequence with map is missing in the article. Con

3.     Use appropriate terminology; anti-analysis?

4.     Figure 3 is not appropriate approach to show the geological condition.

5.     Instead of labelling, FLAC has the option to show the legend of different formations.

6.     Discuss the numerical part in detail; meshing, input parameters, relaxation conditions, FISH functions used, etc.

7.     Discuss in details the labelled parameters in Table 3.

8.     Units are required for each parameter in each table. Units are missing for a couple of parameters. (Table 3)

9.     Show the data of section 4.5 in tabulated form and discuss it.

10.  Discuss the results comprehensively.

11.  Conclusion must be based on the results of this study and must be concise.

Author Response

Thank you for your comments concerning our manuscript entitled “Back analysis of surrounding rock parameters of large-span arch cover station based on GP-DE algorithm” (ID: applsci-2037648). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied the comments carefully and have made a correction which we hope meets with approval. Revised portions are marked  on the paper.

Author Response File: Author Response.docx

Reviewer 2 Report

L23: were analyzed and compared (not was)

L51: ELM extreme learning machine not limit …

At the end of the Introduction section, the authors should describe clearly the contributions of their paper while situating their work against other works. The literature review should be critical enough (I suggest to include the main works in a table including the reference, the study location, characteristics of the site, used method(s), results of the study (accuracy), advantages, disadvantages, etc.)

L125: I think that according to the nature of the problem you are studying (design problem), minimizing the number of functions evaluations in the optimization process is not a key problem. You are not solving a real-time optimization problem where number of iterations (and therefore computation time) is important. The most important problem in your case is the safety. So, the authors should reformulate or give more consistent arguments for what they are saying.

L133: Please, remove this line.

L137: Please, remove this line.

L156: are the upper … not is ….

I think that the optimization problem formulation in Eq. (10) is not so consistent since minimizing the sum of two functions is not equivalent to minimizing the same two functions separately (both functions should be convex to do so). Please, check carefully this point and re-consider.

It seems for me that working on data generated from a simulation model (finite element in your case) can’t be trusted particularly if the authors used a model they constructed by their own. Otherwise, could you please clarify whether the simulation model was generated from the optimization parameters you try to find later?

I think also that the comparative study you developed to compare your method to well-established model (ANN, SVM, etc) is not meaningful except in the case all simulations are conducted under the same conditions.

Decision: Major revision.

Author Response

Thank you for your comments concerning our manuscript entitled “Back analysis of surrounding rock parameters of large-span arch cover station based on GP-DE algorithm” (ID: applsci-2037648). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied the comments carefully and have made a correction which we hope meets with approval. Revised portions are marked on the paper.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

1.      Figure 3 description is not appropriate. Could be the longitudinal geological profile and tunnel x-section or any other appropriate description.

2.      A confusion in the revised manuscript. The authors used FLAC3D or ansys software?

3.      Use appropriate/full figures in Figure 10. In current version, the manuscript is not revealing that the figures are from FLAC.

4.      Avoid discussion in the conclusion part. Concise it.

5.      Include the project location map.

6.      Show the legend of Figure 4.

7.      In table 3, the authors have specified the range. How they selected the input parameter.

8.      Minor linguistic errors are in the manuscript.

Author Response

感谢您对我们题为“基于GP-DE算法的大跨度拱盖站围岩参数回演分析”的稿件(ID:applsci-2037648)的评论。这些意见都很有价值,对我们的论文的修改和完善很有帮助,对我们的研究具有重要的指导意义。我们仔细研究了这些意见,并作了更正,希望得到批准。修订的部分在纸上标记。

Author Response File: Author Response.docx

Reviewer 2 Report

The authors have considered all my concerns properly. I do recommend acceptance of the manuscript. 

Author Response

Thank you for accepting our manuscript. your comments concerning our manuscript entitled “Back analysis of surrounding rock parameters of large-span arch cover station based on GP-DE algorithm” (ID: applsci-2037648) are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research.

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