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Peer-Review Record

Identification and Evolutionary Characteristics of Major Fractures in Beishan Granite

Appl. Sci. 2023, 13(18), 10355; https://doi.org/10.3390/app131810355
by Chaosheng Wang 1,2,*, Hao Wan 1, Weiguang Ren 3,4 and Jianjun Ma 1,2
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Appl. Sci. 2023, 13(18), 10355; https://doi.org/10.3390/app131810355
Submission received: 4 August 2023 / Revised: 11 September 2023 / Accepted: 12 September 2023 / Published: 15 September 2023
(This article belongs to the Section Civil Engineering)

Round 1

Reviewer 1 Report


Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

 

Paper title: Identification and evolutionary characteristics of major fractures in Beishan granite

Summary:

The paper focused on the identification and evaluation of fractures in Beishan granite based on experimental tests and artificial intelligence applied to develop a model to identify the major fractures.

 

Some questions and notes related to the text are presented in the following items:

 

 

1.      There is a missing word on line 46.

2.      Line 104 – Can the authors explain the b value?

3.      How the specimens are collected?

4.      How the authors guarantee the quality of the specimens and the influence of the specimens treatment process?

5.      Figure 2 – please identify where is located the specimen and describe the confining system adopted

6.      What is the specification of the extensometers used for data acquisition?

7.      Lines 160-162: Are there some image of the specimen failure? It would be interesting to understand the behavior.

8.      Fig 4. Please explain why specimen 1-2 presented a different behavior – almost is not possible to identify the calm stage.

9.      Why the authors decide to use DBSCAN? What is the advantage of this method in relation to others like k-means?

10.  How the authors identify that DBSCAN predicts correctly the clusters? It is not clear.

11.  Line 298 – verify the punctuation

12.  Is Eq. 5 the activation function?

13.  Line 369 the “trainparam.epochs” expression must be replace by “number of training epochs” to be not specific to one type of software.

14.  How many data were used for training and testing the neural model? The dataset size is not masking the obtained accuracy?

15.  The conclusion are very summarized. Some topics very direct. Please clarify.

16.  Line 382 – “Conclusion” – capital letter

17.  Please revise the text (especially results and conclusion). There are some gaps, punctuation mistakes and missed words.

 

 

The paper's writing is adequate, with some aspects that must be revised. A general review of the text is advised before final submission.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

This manuscript deals with the identification and evolutionary characteristics of major fractures in Beishan granite. The authors collected  a lot of data presented in a satisfied mode.  In the introduction section  the previous studies are  too much detailed presented . Nevertheless  it is not clearly for me the originallity of this manuscript in accordance with the widely applicability in real conditions in the field. So is this model easy to be applied with high accuracy in other cases? Please refer something about this in order to be  highlighted more  your work and its novelty. What about the petrographic characteristis influence to your model? In my opinion your abstract should be presented more briefly. Please add a brief introduction of  a few lines in the conclusions section. Replace conclusions with Conclusions. 

Kind regards

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 3 Report

The revised manuscript can be published in present form

Good luck

 

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