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Article
Peer-Review Record

Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning

Sustainability 2022, 14(20), 12990; https://doi.org/10.3390/su142012990
by Ehsan Mansouri 1,2,3, Maeve Manfredi 4 and Jong-Wan Hu 5,6,*
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3: Anonymous
Sustainability 2022, 14(20), 12990; https://doi.org/10.3390/su142012990
Submission received: 21 August 2022 / Revised: 27 September 2022 / Accepted: 3 October 2022 / Published: 11 October 2022

Round 1

Reviewer 1 Report


Comments for author File: Comments.pdf

Author Response

Please check the attached file. Thank you.

Author Response File: Author Response.pdf

Reviewer 2 Report

The work is okay. Authors need to enhance the quality of the article. 

The article requires a lot of corrections/updates in its current state. The area of AI is blooming among all the sectors this creates an opportunity for this paper to be considered as in this work authors used advanced AI techniques. However, the article needs revisions to enhance the quality of the work. Some of the comments/suggestions/recommendations are as follows.

1. In the abstract, the first two lines can be improvised to gain the attention of the readers.

2. In Line 27: “increase of 23%” Specify – is this about population, infrastructure, or concrete use?

3. In Line 38: Transporting “Gas” or “Fly Ash”.

4. In Line 48-49: Check reference “24” for the statement.

5. Line 108-109 is not having a clear meaning. Update the line so it can be meaningful.

6. In Line 251: According to Table 2

7. Mention when performance indices are good and when they are not. For example – MSE/MAE will be good when it is near zero and R2 is good when its value reaches one.

8. In Line 257: “Candidate” or “iterations”

9. Equations: All the equations will be in a similar format and mentioned in the text as well with their references (from where those equations have been taken) (For MAE/MSE/RMSE etc. authors can refer to https://doi.org/10.1155/2022/9404807  and https://doi.org/10.3390/su14042404 )

10. Figures: Text in all the figures will be in the same font and size for an attractive appearance. Many figures are blurry; authors can update the figures in the file or else they can upload a separate file for the figures on the Journal website. Mention all the figures in the text as well and explain them.

11. Tables: Mention Tables in the text and provide an explanation in the text about the data in the table. Every table, figure, and equation will be mentioned in the text like Figure 1 and Figure 2 are mentioned in the text in 163rd Line and 172th Line respectively.

12. Abbreviations: All the abbreviated terms will be mentioned in their full form at the very first time when it is used (like in Line 37- Fly Ash (FA) must be written, in later author can use FA in the rest of the article) then abbreviation can be used throughout the paper. There are many abbreviated terms which don’t have full form in the article so it is hard for readers to understand.

13. Draw a research methodology diagram.

14. Remove Error! Ref. Source not found” from the text and check all the references again both in the end and in the text as well.

15. Try to remove passive sentences from the text.

Author Response

Please check the attached file. Thank you.

Author Response File: Author Response.pdf

Reviewer 3 Report

The author's work deserves recognition, but there are still some problems to be improved

1. The introduction is too redundant. A literature review chapter should be added and part of the introduction should be included in this chapter.

2. The author mentioned that “Researchers have been investigating the role of Artificial Intelligence (AI) methods in the development of models that are reliable, accurate and consistent for solving structural engineering problems.” And the author describes some models, such as ANN, LSSVM, ARIMA. However, LSTM has advantages in prediction, which is not mentioned by the authors.

Authors can cite and add literature:[1]Regional Manufacturing Industry Demand Forecasting: A Deep Learning Approach.

Literature [1] points out that LSTM model has advantages over BP neural network, SVM, RF, etc. in prediction.

3. Can table 1 be used as an appendix? It is not very beautiful in the text and also affects the reader's reading.

4,The title of the discussion chapter should be changed to "experiment and results"

5. There are a lot of “Error! Reference source not found.” in the article. The author needs attention.

6. The discussion should be added again, including the main findings, shortcomings and future research aspects. Refer to [2] discussion chapter framework.

[2] The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Author Response

Please check the attached file. Thank you.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

I have no other comments, and I think the paper can be accepted in present form.

Author Response

Dear Reviewer 1 Thank you for your good comments. Please check the attached file. Jong Wan Hu

Author Response File: Author Response.docx

Reviewer 2 Report

The article is updated according to the previous suggestions. However, there are still some points to be considered to upgrade the quality of the manuscript further. These are:

1) Reference section needs some formatting and correction. "24" is written after reference 25.

2) Remove template lines from author contribution (first two and last two lines).

3) Check the First line of funding (name).

4) Read the article for grammatical and other errors before final submission.

5) Figure 1 is the research methodology for this work and must end at some point. The current methodology is circular and does not have an endpoint. Rectification is needed.

Author Response

Dear Reviewer 2 Thank you for your good comments. Please check the attached file. Jong Wan Hu

Author Response File: Author Response.docx

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