The Application of Response Surface Methodology and Machine Learning for Predicting the Compressive Strength of Recycled Aggregate Concrete Containing Polypropylene Fibers and Supplementary Cementitious Materials

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This article discusses a highly relevant topic, specifically, research on the application of RSM and machine learning for predicting the compressive strength of recycled aggregate concrete containing polypropylene fibers and SCMs.

The research presents a comprehensive analysis of recycled aggregate concrete (RAC) and its mechanical properties, with a focus on optimizing compressive strength (CS) using Response Surface Methodology (RSM) and Machine Learning (ML) models. Given the increasing need for sustainable construction materials, the study is relevant. It addresses critical concerns related to the mechanical limitations of recycled aggregate (RA) and the effectiveness of supplementary cementitious materials (SCM) like fly ash (FA) and silica fume (SF).

The article is relevant, but in my opinion, very few changes need to be made.

• In my opinion, the abstract should be shortened. This primarily applies to the introduction (lines 11-14). Also, the abstract should not include numerical results (lines 20-25).
• The study does not explore regional variations in material properties, which may impact real-world applications.
• The study relies heavily on existing datasets rather than conducting new experimental trials to verify model predictions.
• The study focuses on mechanical properties but does not address the cost implications of RA, FA, and SF use.
• The conclusions are consistent with the evidence and arguments presented and answer the main question posed.
• The references are appropriate but should be updated with newer ones (preferably not older than 5 years). To increase the relevance of the study, the following works should be added to the References section: Pstrowska, K., Gunka, V., Prysiazhnyi, Y., Demchuk, Y., Hrynchuk, Y., Sidun, I., ... & Bratychak, M. (2022). Obtaining of formaldehyde modified tars and road materials on their basis. Materials, 15(16), 5693.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Comments and suggestions are in the attached file.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

  In this paper, application of RSM and machine learning for predicting the compressive strength of recycled aggregate concrete containing polypropylene fibers and SCMs was investigated. I recommend the publication of this manuscript after minor revision.

• Please note the significant digits, such as 0.8860 in abstract.
• Try to avoid using the active voice when the literatures were mentioned.
• In the section of “Introduction”, the novelty and motivation of the work needs to be described further.
• Is the strength data in the literatures measured by a unified standard?
• What does the unit “kg/cm3” refer to in Table 1?
• Don’t write full names and abbreviations every time. E.g. Line 154
• The serial number of equation 3 is wrong. (Line 257-258)
• Was the optimized results verified by experiments?

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf