Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks
Round 1
Reviewer 1 Report
Through the explanatory variables in the production process of concrete paving blocks and the amount of water absorption in a company in Quito, Ecuador, the study investigated various models to predict the tensile splitting strength. The first set of predictor variables includes thickness, width, length, mass of the fresh paving block, and percentage of water absorption. The fresh paving block's density and water absorption rate make up the second group of predictor factors. The weakness is with the way parts of the manuscript are written. A scientific paper must be written clearly and concisely. Therefore, the authors need to review the entire manuscript and rewrite parts of it. I have specified some parts that should be corrected. A few details are as below:
1. Abstract: A brief and concise abstract should be considered. In its current form, it seems long and less concise.
2. Introduction needs to be reorganized; it is unpleasant to read. The utilization of smaller sentences may be a potential approach. In particular, previous studies' limitations were not mentioned, which led to the current study's motivation being weak and unclear. Please update the points.
3. Since ML models are well-known, this detail / fundamental information should be skipped to focus on essential other points.
4. It is common practice in soft computing models to take place in the well-known overfitting problem. Authors are kindly requested to include a paragraph about this crucial issue and comment on their findings. Based on their optimum developed and proposed model, a good approach will be to include a set of graphs and comment on the smoothness of derived curves that declare that overfitting does not occur. For example, they could prepare a set of graphs with the tensile splitting strength on the vertical axis while the percentage of water absorption is horizontal for different age values or any other parameter, keeping all the other parameters constant. Furthermore, such diagrams will reveal the strongly nonlinear nature of the problem [Armaghani, D.J., Asteris, P.G. (2021). A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength, Neural Computing and Applications, 33(9), pp. 4501-4532, DOI: 10.1007/s00521-020-05244-4; Asteris,
P.G., Lourenço, P.B., Adami, C.A., Roussis, P.C., Armaghani, D.J., Cavaleri, L., Chalioris, C.E., Hajihassani, M., Lemonis, M.E., Mohammed, A.S., Pilakoutas, K. (2022). Revealing the nature of metakaolin-based concrete materials using Artificial Intelligence Techniques, Construction and Building Materials, 322,126500, https://doi.org/10.1016/j.conbuildmat.2022.126500.
Author Response
Dear Editor
Dear Reviewer
"Please see the attachment."
Author Response File: 
Author Response.pdf
Reviewer 2 Report
In this study, a range of indicators are employed as variables in computer prediction models to estimate the strength performance of concrete paving blocks. There are some problems, which must be solved before it is considered for publication. Major revisions are suggested for this manuscript.
1. Delete the period punctuation in the title.
2. The abstract should be revised.
3. "Multivariate analysis " needs to be added as a keyword.
4. It is necessary to rewrite “Relative work” because it cannot be just a summary of the literature. This section needs to be made concise.
5. Please eliminate any text in Part 2 that is irrelevant to this study or has little significance.
6. the text in Table 1 can be rearranged.
7. Parts 3 and 4 should be merged to write the result and discussion or to add explanation of the results. The research result and analytical procedure should be presented. The author devotes a lot of space to show the figures. Further analysis and explanations are necessary. please revise this part.
8. the conclusions Part needs to be revised. Lines 798-800, 808, for instance, do not adhere to the writing requirements for scientific publications.
This study focuses on selecting the most appropriate prediction model and predicting the splitting performance of cement paving bricks. The study closes the knowledge gap in cement paving brick splitting properties prediction. Instead of just testing models, the authors are recommended to take into account model optimization and comparative investigations. The authors' conclusions address the key problems brought up and are supported by the available data. Major revisions are suggested for this article. Units must be labeled in the figures, for example, Figures 2, 3, 9, 12, and 20.
Author Response
Dear Editor
Dear Reviewer
"Please see the attachment."
Author Response File: Author Response.pdf
Reviewer 3 Report
This article aims to predict the tensile splitting strength through the quality control 64 variables of the vibro-compacting process and the water absorption test of paving blocks. I have following comments to improve the quality of this manuscript:
1. line 133: please correct the typo ZnO2 and other similar errors throughout the manuscript.
2. Can you provide better figures in Table 1? They are not clear enough.
3. Discuss the limitation or scope of the prediction method in this study.
Author Response
Dear Editor
Dear Reviewer
"Please see the attachment."
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
It is evident that the authors responded adequately to the comments.
Reviewer 2 Report
"Multivariate analysis for prediction of splitting tensile strength in concrete paving blocks" predicts the tensile strength of cement paving blocks and identifies the optimal predictive model.
The study fills the knowledge gap in predicting the splitting properties of cement paving bricks.
The author has carefully revised the manuscript in response to my previous review comments, significantly enhancing its clarity and academic rigor.
The author has addressed formatting issues, added relevant discussions to enhance the academic significance of this manuscript, and improved the figures for better readability.
The author's responses to each of my suggestions were well-considered.
I recommend accepting this manuscript.
another suggestion will be that the conclusions could be more concise.
