PONE-D-24-03879Modeling the influence of lime on the unconfined compressive strength of reconstituted G-S-M-C graded soil using advanced machine learning approaches for subgrade and liner applicationsPLOS ONE

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Dr. S. M. Anas, Ph.D.(Structural Engg.), M.Tech(Earthquake Engg.)

Academic Editor

PLOS ONE

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Additional Editor Comments:

Dear Authors,

I hope this email finds you well. I am writing to inform you about the decision regarding your manuscript entitled "Modeling the influence of lime on the unconfined compressive strength of reconstituted G-S-M-C graded soil using advanced machine learning approaches for subgrade and liner applications" (PONE-D-24-03879), which you submitted to PLOS ONE.

After careful consideration, I am pleased to inform you that your manuscript has undergone peer review by three experts in the field. However, all reviewers have provided feedback indicating that major revisions are required before the manuscript can be considered for publication. Based on their recommendations and our preliminary analysis, we have decided to request major revisions from you.

Please note that upon resubmission, your revised manuscript will undergo further evaluation to ensure that the necessary revisions have been adequately addressed. We encourage you to be thorough and transparent in your revisions to expedite the review process.

Important note from Academic Editor, Dr. S. M. Anas: -

I would like to bring to your attention that citing the papers suggested by the reviewers is not mandatory for your revised manuscript. It is entirely up to you whether or not you choose to include the suggested papers in your revised version. The reviewers have provided these suggestions to enhance the quality and credibility of your research, but ultimately, the decision is yours. You have the freedom to decline including any of the suggested papers in your revised manuscript if you feel they are not relevant or do not add value to your study.

If you have any questions or need clarification on any aspect of the revision process, please do not hesitate to contact me. We appreciate your commitment to enhancing the quality of your manuscript and thank you for choosing PLOS ONE as the outlet for your research.

We look forward to receiving your revised manuscript.

Best regards,

Dr. S. M. Anas

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear Authors

Manuscript entitled” Modeling the influence of lime on the unconfined compressive strength of reconstituted G-S-M-C graded soil using advanced machine learning approaches for subgrade and liner applications “

This manuscript with Manuscript Number PONE-D-24-03879 as been written very great, and it has good novelty for advanced machine learning approaches for subgrade and liner applications of Grout, therefor I am some suggestions to improve your manuscript.

1- the title of the paper is not informative and not clear, so I suggest to the authors to revise the title.

2- The research contributions of the paper should be articulated more clearly. The abstract is not representative of the content and contributions of the paper. The abstract does not seem to properly convey the rigor of research. And also I recommend the author(s) to be more direct to cover the research gap, manuscript's goal, materials and methods, main results, main contributions, main suggestions for future research.

3- The necessity and innovation of the article should be presented to the introduction.

4- In addition, there author should highlight the advantages of which on advanced machine learning approaches in introduction section. Literature review section does not present relevant and updated works that form an adequate basis for the study's research model. The author could add a table in Introduction section and list download the more recent studies used advanced machine learning approaches for soil improvement.

5- Following, you will find some new related references which should be added to literature review:

• Salehi, M., Bayat, M., Saadat, M., & Nasri, M. (2023). Prediction of unconfined compressive strength and California bearing capacity of cement-or lime-pozzolan-stabilised soil admixed with crushed stone waste. Geomechanics and Geoengineering, 18(4), 272-283.

• Karimiazar, J., Teshnizi, E. S., O'Kelly, B. C., Sadeghi, S., Karimizad, N., Yazdi, A., & Arjmandzadeh, R. (2023). Effect of nano-silica on engineering properties of lime-treated marl soil. Transportation Geotechnics, 43, 101123. https://doi.org/10.1016/j.trgeo.2023.101123

• Baldovino, J. A., Moreira, E. B., Izzo, R. L. D. S., & Rose, J. L. (2018). Empirical relationships with unconfined compressive strength and split tensile strength for the long term of a lime-treated silty soil. Journal of Materials in Civil Engineering, 30(8), 06018008. https://doi.org/10.1061/(ASCE)MT.1943-5533.000237

• Hashemi, H., Yazdi, A., & Teshnizi, E. S. (2023). Improvement of collapsing problematic soils on the sabzevar-mashhad railway route (northeast of Iran) using traditional additives. Nexo Revista Científica, 36(03), 383-403. https://doi.org/10.5377/nexo.v36i03.16461

• Goutham, D. R., & Krishnaiah, A. J. (2024). Prediction of unconfined compressive strength of expansive soil amended with bagasse ash and lime using artificial neural network. Journal of Soft Computing in Civil Engineering, 8(1), 33-54.

• Sharifi Teshnizi, E., Mirzababaei, M., Karimiazar, J., Arjmandzadeh, R., & Mahmoudpardabad, K. (2024). Gypsum and rice husk ash for sustainable stabilization of forest road subgrade. Quarterly Journal of Engineering Geology and Hydrogeology, 57(1), qjegh2023-008. https://doi.org/10.1144/qjegh2023-00

• Sharma, L. K., & Singh, T. N. (2018). Regression-based models for the prediction of unconfined compressive strength of artificially structured soil. Engineering with computers, 34, 175-186. https://doi.org/10.1007/s00366-017-0528-8

6- For readers to quickly catch your contribution, it would be better to highlight major difficulties and challenges, and your original achievements to overcome them, in a clearer way in abstract and introduction.

7- It is suggested to compare the results of the present research with some similar studies which is done before.

8- Much more explanations and interpretations must be added for the Results, that's not enough.

9- Some key parameters are not mentioned. The rationale on the choice of the particular set of parameters should be explained with more details. Have the authors experimented with other sets of values? What are the sensitivities of these parameters on the results?

10- Why do you choose only graded soil (G,S,M, and C) and Lime percent in this research? You know type of clay minerals and reaction to lime additive for improving of soil are really important, how can you effect it in your models?

11- You should add the flow chart in this manuscript for methodology.

12- Why did you manually separate the training and Validation datasets? Couldn't they be automatically separated and selected by artificial intelligence models?

13- Have you controlled these models in other additives?

14- In the tables of training and Validation datasets, are some UCS in 7 days more than 28 days? What was the reason for this?

15- The conclusion section would be improved by starting with the research goal. The author could present the practical implication of this study. However, further suggestions for future research could be addressed and inserting a paragraph by stating the limitations of this study.

Thanks

Reviewer #2: The work titled "Modeling the Influence of Lime on the Unconfined Compressive Strength of Reconstituted Gravel-Sand-Silt-Clay (G-S-M-C) Graded Soil Using Advanced Machine Learning Approaches for Subgrade and Liner Applications" presents valuable insights. However, to enhance the paper, the following comments should be addressed:

1. Title: The title of the paper is commendably descriptive, but its length may pose readability challenges. Additionally, the use of uncommon abbreviations such as G-S-M-C might deter readers unfamiliar with geotechnical engineering terminology. A concise and accessible title without abbreviations would enhance readability and broaden the paper's appeal.

2. Abstract: The abstract serves as a crucial gateway to the paper's content. To improve its effectiveness, consider reorganizing it to follow a logical structure that highlights the main findings of the research. Structuring the abstract to include sections for Introduction, problem statement, methodology, results, and conclusion would provide readers with a clear roadmap of the paper's contents. Furthermore, replacing the abbreviation G-S-M-C with a more commonly understood term would enhance accessibility to a broader audience within geotechnical engineering.

3. Introduction: While the introduction effectively sets the stage for the study, providing additional background information could strengthen its impact. Including more context about the research problem and its significance would help readers better understand the motivation behind the study. Moreover, concluding the introduction with a paragraph emphasizing the importance, novelty, and originality of the research would further engage readers and highlight the paper's contributions to the field.

4. Data Collection and Statistical Analysis: Presenting the data collection process in a tabular format would enhance clarity and facilitate easier interpretation for readers. Additionally, avoiding the use of abbreviations in subsection titles would contribute to a more professional presentation of the research.

5. Figures 7 and 8: Adding a line Y=X to these figures would improve clarity and aid in understanding the data presented.

6. RSM Model Analysis: Considering the complexity of equations 8 and 9, relocating them to an appendix rather than the main section of the paper would streamline the presentation and improve readability.

7. Results: In addition to reporting positive findings, it's essential to discuss any unexpected or negative results encountered during the study. Comparing the results with those of previous studies would provide valuable context and highlight the novelty of the findings.

8. Conclusions: Streamlining the conclusions to focus on key findings and their scientific implications would enhance clarity and impact. Furthermore, adding closing remarks summarizing all conclusive points would provide a cohesive ending to the paper and reinforce its scientific contributions.

Reviewer #3: The paper “Modeling the influence of lime on the unconfined compressive strength of reconstituted G-S-M-C graded soil using advanced machine learning approaches for subgrade and liner applications” has been thoroughly studied and reviewed. This paper has presented four machine learning (ML) techniques, such as Genetic Programming (GP), Artificial Neural Networks (ANN), Evolutionary Polynomial Regression (EPR), and the Response Surface Methodology in modeling the unconfined compressive strength (UCS) of soil-lime mixtures for subgrade and landfill liner design and construction. This is one of the best model presentations I have reviewed recently, which proposed a very important concept in solving environmental problems related to highway foundations and landfill liners. The authors have done excellently well in both English language presentation, study background, methodology, results presentation and analyses and this promises to spur and open up research projects in this area. The conclusions are supported by the results of the research work. Meanwhile, there exist very minor revisions needed to update the already established standard of research work;

1. What could be responsible for the low performance of the GP compared to the other models?

2. What is the reason behind the excellent performance of the RSM as a symbolic regression technique?

3. The practical application of the symbolic closed-form equations 3, 4, 6-9 is required to be mentioned in the results discussion section.

With these comments addressed in the manuscript, the research paper stands out as an excellent work in the field of engineering and technology in solving geo-environmental engineering problems.

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Reviewer #1: Yes: Ebrahim Sharifi Teshnizi

Reviewer #2: Yes: Meysam Bayat

Reviewer #3: Yes: Mahmood Ahmad

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Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications

PONE-D-24-03879R1

Dear Dr. Onyelowe,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Dr. S. M. Anas, Ph.D.(Structural Engg.), M.Tech(Earthquake Engg.)

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear Authors,

I hope this email finds you well. I am writing to inform you about the decision on your revised manuscript entitled "Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications" (PONE-D-24-03879R1), which has undergone further review.

I am pleased to inform you that the revised manuscript was sent to the previous reviewers for reevaluation. I am delighted to convey that all three reviewers have recommended the paper for publication and expressed satisfaction with the authors' responses to their comments.

Based on the recommendations of the reviewers and a preliminary assessment of the revised manuscript, I have decided to take an Accept decision, subject to the approval of the editorial board.

Once again, congratulations on the successful revision of your manuscript. I will keep you updated on any further progress regarding the editorial board's decision.

Thank you for your contribution to PLOS ONE.

Best regards,

Dr. S. M. Anas

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: N/A

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors correlate the Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications from the literature. The study could be of importance to the future readers who would want to use these correlations in their study.

This manuscript was improved for all comments.

Thanks

Reviewer #2: The new version of the paper is wholly modified compared to the original version, and the article is acceptable for publication.

Reviewer #3: Most of the reviewer concerns are addressed so recommended to the Editor Highness for acceptance of this manuscript.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Mahmood Ahmad

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