PONE-D-23-41239Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning methodPLOS ONE

Dear Dr. Zhang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================Dear Authors,

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Shaker Qaidi

Academic Editor

PLOS ONE

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Additional Editor Comments:

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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: Partly

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

Reviewer #1: I Don't Know

Reviewer #2: N/A

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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

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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

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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: Review Comments to Author

Review for “Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning method”

Reviewer Comments:

Data modeling analysis of GFRP tubular filled concrete column based on a small sample deep meta-learning method is investigated in this study. The reviewer suggests it can be reconsidered if the authors can address the comments as follows:

1. Page 1, the manuscript in the title page (Title, Author, Affiliations, Abstract) lacks the line number, which makes it too difficult for the reviewer to locate the possible comments on this study.

2. Page 1, Author lists, Tianyi Deng1☯, Chengqi Xue1☯, Gengpei Zhang1*. What does the symbol “☯” mean for the authors? Maybe it represents the authors’ contribution equally to this research. In addition, what is this symbol “*” stands for? Corresponding author or other meanings? Considering the E-mail address is provided in the next line. Please clarify. Add the corresponding author before the E-mail address.

3. Page 1, the affiliation “, Yangtze University, Jingzhou City, Hubei Province, China”. The school of affiliation is correct? The reviewer recommends “School of Electronics and Information”. Please confirm this. The postcode is neglected in the affiliation.

4. Page 3, Line 76, Introduction, “using concrete filled GFRP column data as the test object to solve the modeling and analysis problem of small sample data in building structural units”. The abbreviation GFRP is the first time appeared in the manuscript without providing the full name. Please check the whole manuscript for similar issues.

5. The manuscript is entitled “GFRP tubular filled concrete column”. However, the reviewer could not find any information about why GFRP tubular filled concrete columns were chosen for the research object. Please explain the reasons.

6. The illustration of the Figs. 1~4, 7~8 was low resolution with poor quality. The reviewer suggests the authors redraw the flowchart by using Visio software. Special attention should be paid to the font, preferably Times New Roman.

7. Table 1, function “f and F” should be changed into “ and ”. Please check all the symbols in the whole manuscript. In the process, (X, Y), y=f(x), f=F(), please use the MathType software for the symbols, Italic Font.

8. Figs. 5 and 6 should be cited because they are not your own tested specimens.

9. Figs. 9~13 is the same style, they could combine in the same figure with sub-figure (a)~(d). Considering your expertise, please change the drawing style from Excel to Origin, Matlab, or Python. Vector graph is highly recommended in scientific graphs. Figs. 14~18 is a similar situation.

10. Page 10, Tables 2 and 3, “ultimate displacement” should be replaced by “Ultimate displacement”, which can consist of “Evaluation criteria” and “Evaluation criteria”.

11. Page 15, Lines 351~352, This method is superior to SVMR, GPR, and RBFNN in solving small sample data modeling of beams/columns. Does the proposed method consider GFRP-reinforced concrete beams? Please clarify.

12. Page 15, Lines 354~357, H/D and D/T should be replaced by “ and ”. Please check similar issues in the whole manuscript.

13. Please add DOI for the references listed.

14. Much literature has been reviewed in the paper, but less critical. The latest research concerning this topic should be addressed and added in the introduction. In other words, you should clearly explain what contribution that has been made by former research and, in particular, what limitation or weakness that exists in each previous research. You should identify the gap in the knowledge from the review of the previous research to justify the significance of YOUR current research topic.

15. Some assumptions are stated in various sections. Justifications should be provided for these assumptions. What is the basic principle of SVR, GPR, RBFNN, and SSDML? Evaluation of how they will affect the results should be made.

16. What are the concrete and GFRP material properties of the tested specimens? How to calculate the ultimate bearing capacity?

17. The authors should clarify the novelty of this manuscript.

18. The background and research needed on GFRP should be demonstrated with more words in the introduction section.

19. The authors should clarify how this method is to be used for GFRP composite structures in civil engineering.

20. The data points in this research are not quite enough. How the authors can guarantee the accuracy of this modeling approach?

21. Page 13, line 308. As shown in Fig 14 Fig 18, the ultimate displacement of the column decreases with the increase of the symmetry ratio. Figs. 14 and 18.

22. The comparison of the data is too common with only qualitative analysis, please rewrite the style with quantitative analysis.

In its current version, the reviewer recommends the document for Major Revision in PLOS ONE.

Reviewer #2: This paper proposes an innovative deep meta learning modeling approach called SSDML to address key small sample data challenges in machine learning models for civil engineering applications. The authors demonstrate empirically on a concrete-filled GFRP column dataset that SSDML outperforms other methods by leveraging data augmentation and meta-learning to improve prediction accuracy. Strengths include the novel integration of techniques, detailed experimental results, and supplemental numerical analysis enriching field knowledge. Considerable potential exists for extending this work by tuning model hyperparameters, incorporating additional datasets, and adapting the framework using transfer learning. Further model optimization and validation will help strengthen the conclusions and better quantify method advantages. Overall the study reflects substantive research contributions advancing small sample modeling capabilities, though scope remains for building out the technical evidence base and enhancing engineering interpretability. If addressed, the gaps identified should not diminish the merits of this promising approach tackling a discipline priority problem.

1. In the abstract, consider revising the language to be more concise. Some sentences could be shortened without losing key information.

2. In the introduction, provide more context and motivation early on regarding why small sample modeling is important for this application. What key challenges does it aim to address?

3. In the data augmentation section, provide more specific details on the models used and parameters selected. Were these optimized in any way? How was model performance evaluated?

4. Were any data preprocessing or feature engineering steps taken before model fitting? This could help improve accuracy.

5. For the meta learning methods, consider including a bit more background explanation of how they differ and their relative advantages.

6. In the data and modeling section, explain the rationale for the network architecture selected. Was any hyperparameter tuning conducted?

7. For the model testing results in Tables 2 and 3, include the sample sizes used. Also explain if a validation set was held out.

8. In the model testing conclusion section, specifically compare the performance lift from data augmentation vs. the final SSDML model to quantify that added impact.

9. For the discussion section analysis, consider visualizing the trends with plots for easier interpretation.

10. In the conclusion, summarize the key advantages empirically demonstrated by the SSDML approach compared to alternatives.

11. Carefully proofread the paper to fix any grammatical issues or awkward phrasing. Focus on clear, concise language.

12. Review citation formats to ensure consistency throughout.

13. Consider additional references demonstrating successful applications of meta learning and data augmentation to small sample modeling.

14. Provide more details on the specific models used as baselines (SVMR, GPR, etc.) in terms of the algorithms, libraries implementations, and parameter settings.

15. Explain any data splitting, cross-validation, or other validation strategies used to reduce overfitting and evaluate model generalizability.

16. Discuss any limitations or assumptions in the data modeling approach.

17. Elaborate on the rationale and process for selecting the specific input features used.

18. Provide more details on the genetic algorithms and parameters used for data augmentation.

19. Explain the basis for quantifying model accuracy specifically using R^2 and MAPE. Also consider additional metrics.

20. Discuss the potential for extending this approach to other types of building structure modeling tasks using transfer learning.

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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: Yes: Mahmoud Akeed

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PONE-D-23-41239R1Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning methodPLOS ONE

Dear Dr. Zhang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jun 03 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

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

Academic Editor

PLOS ONE

Additional Editor Comments:

Dear Authors,

I hope this email finds you well. I am Dr. S. M. Anas, the academic editor assigned to handle the revision of your manuscript entitled "Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning method" [PONE-D-23-41239R1], for PLOS ONE. This manuscript has been reassigned to me as the previous editor was unable to respond to the editorial board.

Upon reviewing the feedback from the previous reviewers and the additional reviewers' recommendations, I have made a preliminary assessment of your revised manuscript. While one reviewer expressed satisfaction with your responses, another reviewer remains unsatisfied and opposes publication in its current form. Additionally, mixed recommendations and decisions were obtained from the newly invited reviewers by the previous academic editor.

Considering this feedback, I have decided to request a Major Revision of your manuscript. I kindly ask you to carefully address all the reviewers' comments, paying close attention to areas of concern raised by the reviewers who were unsatisfied with the previous version of your manuscript.

Please ensure that you provide detailed responses to each comment and make appropriate revisions to the manuscript to strengthen its scientific quality and clarity.

Once you have addressed the reviewers' comments and made the necessary revisions, please submit the revised manuscript along with a detailed response letter outlining the changes made and how you have addressed each comment.

If you have any questions or require further clarification, please do not hesitate to contact me. I look forward to receiving your revised manuscript.

Thank you for your attention to this matter.

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. 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: (No Response)

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

Reviewer #5: (No Response)

Reviewer #6: (No Response)

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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: Partly

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Partly

Reviewer #6: No

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

Reviewer #1: I Don't Know

Reviewer #2: N/A

Reviewer #3: N/A

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: No

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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: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: No

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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

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: No

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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 reviewer suggests it can be rejected because the authors cannot answer the comments carefully and correctly. The comments as follows:

1. The style of the author response is simple and elaborate without the real answer to the comments and how the new version is presented in the revised manuscript. No lines and pages was indicated in the response to reviewers.

2. Page 1, the affiliation “, Yangtze University, Jingzhou City, Hubei Province, China”. The postcode is neglected in the affiliation.

3. Page 13, line 308. As shown in Fig 14 Fig 18, the ultimate displacement of the column decreases with the increase of the symmetry ratio. Please change the sentence into “As shown in Figs. 14 and Fig 18, the ultimate displacement of the column decreases with the increase of the symmetry ratio”.

4. Line 217, the references style is not in comply with the style in introduction.

5. Line 392~393, no figures is shown in Fig.7. The title of Fig.7 is missing.

6. Line 315 and Line 318, R2 should be changed into by using MathType software.

7. Fig.4, the pictures of the cross section of GFRP pipe wrapped column is inappropriate, real pictures of the specimens are recommended.

8. The data source of this research is not quite clear. The material properties of the specimens are missing.

9. There is a space between the reference and words.

In its current version, the reviewer recommends the document for Rejected in PLOS ONE.

Reviewer #2: Now, the manuscript (Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning method) is ready for the next stage of the publishing process.

Reviewer #3: This research introduces an innovative deep meta-learning method called EF-DR for modelling small sample data of GFRP-wrapped concrete-filled GFRP tubular columns. The method outperforms traditional machine learning techniques like SVMR, GPR and RBFNN. The authors provide new insights into how various input factors influence the ultimate bearing capacity and displacement of the columns. The data augmentation and meta-learning approaches are promising for dealing with limited datasets in structural engineering. However, some aspects like the network architecture and outlook need further elaboration. Overall, the manuscript makes a valuable contribution to the field, with a few areas that could be strengthened.

Comments:

1. The genetic-based data augmentation method is intriguing. Could you provide more details on how the chromosomal crossover and mutation operations are specifically implemented?

2. On page 4, you mention that "reliable real-time data collection at the engineering site is costly". Have you considered any strategies to make the data collection process more efficient and cost-effective?

3. The statement on page 6 that "The prediction accuracy of each model for the ultimate bearing capacity and ultimate displacement of the output performance indicators of the pipe column is shown in Table 2" seems to be referring to the wrong table number. Please double check the table references.

4. How did you determine the hyperparameters for the fully connected network structure used in EF-DR (number of hidden layers, nodes per layer, etc.)? Did you experiment with different architectures?

5. The analysis of how input factors impact the ultimate bearing capacity and displacement provides useful practical insights. However, the physical mechanisms behind some of the observed trends are not fully explained, e.g. the alternating effect of H/D on bearing capacity. Further discussion on the underlying reasons would be valuable.

6. There seems to be a discrepancy between the concrete strength values mentioned in the data source description (20 MPa) and the values shown in Figures 7c and 7h (20-50 MPa). Please clarify the range of concrete strengths actually investigated.

7. For the model testing, only 6 out of 72 total samples were used. Is this test set sufficient to comprehensively evaluate the model performance? Validating the results on a larger test set, if possible, could increase confidence in the findings.

8. The outlook section provides good suggestions for future work. One additional direction to consider could be extending the approach to other types of structural components beyond columns, such as beams or walls. This would demonstrate the broader applicability of the method.

9. While the results show the superior performance of EF-DR compared to traditional methods, it would be informative to discuss any limitations or potential failure cases of the proposed approach. Under what circumstances might the model struggle to make accurate predictions?

Reviewer #4: Necessary modifications have been done and convincing explanations have been given in the revised paper. Consequently, my final recommendation is “accept”.

Reviewer #5: the authors did not implement all of the reviewer comments. I advised the authors to check every single comments carefully. The comments especially on the format of the graphs and the explanation is not fully clarify.

Reviewer #6: First of all:

A. None of the comments are responded to properly.

B. This is not the reviewers’ task to look up the MS and find your responses.

C. Entirely suffers from deep disorganization, format, ill-formatted figures, inconsistent text style, missed Tables (look at L392!!!!), informal structure and used terms like ‘method outlook’ instead of ‘conclusion’, obvious ill-formatted references like 20, 21, 22, 23, …, 42????? …

D. None of the updated relevant works neither presented nor critically analyzed.

Overall, this work without any doubt is REJECTED. Some of the reasons are:

1. Incredibly poor English full of flaws and linguistic syntaxes. ‘model modeling’???? entirely MUST be reworked by the help of a native agent. Overall, unreadable.

2. concrete lack of any innovative level is clear. In comparison with advanced published works the presented results are obviously ill-posed, inconsistent and too premature.

3. Doesn’t have any validation, model optimality approval based on the hyperparameter optimization… Nothing on how the overfitting, early convergence, trapping, error improvement monitoring, optimal topology, dying Relu … were treated. Just for example, how the kernel for SVMR was handled and adjusted?? Basically, which type of kernel and why???

4. Doesn’t have any certified and documented Discussion in terms of accuracy metrics, evidential analysis, solid comparison with other scholars, limitation, stability approval, uncertainty quantifications, pitfall and practical difficulties, any representative figure showing the results, physical interpretation,

5. The predictability, calibrating and sensitivity analysis should be carried out through the weight database of the trained model to show the importance of the used attributes when its optimality is confirmed. Clarify where and how the optimal weight database is stored? How it can be recalled?? Search for updating the neural network models using different sensitivity analysis methods, sensitivity analysis for neural networks, novel feature selection using sensitivity analysis…

6. REJECTED conclusion.

7. Any data analysis/data visualization in considering the selected attributes

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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: No

Reviewer #4: No

Reviewer #5: Yes: ERTUG AYDIN

Reviewer #6: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PONE-D-23-41239R2Data Modeling Analysis of GFRP Tubular Filled Concrete Column Based on Small Sample Deep meta Learning MethodPLOS ONE

Dear Dr. Zhang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jun 29 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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We look forward to receiving your revised manuscript.

Kind regards,

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

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Dear Authors,

I hope this email finds you well. I am writing to inform you about the status of your manuscript entitled "Data Modeling Analysis of GFRP Tubular Filled Concrete Column Based on Small Sample Deep meta Learning Method" [PONE-D-23-41239R2] submitted to PLOS ONE.

After careful consideration of the reviewers' comments and your responses, I'm pleased to inform you that one of the reviewers is satisfied with the revisions made to your manuscript. However, the other reviewer has provided feedback indicating that a few minor revisions are necessary.

Upon preliminary assessment, I have decided to move forward with a Minor Revision of your manuscript, subject to the approval of the editorial board. Please address the minor revisions suggested by the reviewer, and submit the revised manuscript at your earliest convenience.

Thank you for your attention to this matter. Should you have any questions or require further clarification, please do not hesitate to contact me.

Best regards,

Dr. S. M. Anas

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

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 #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 #3: Yes

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

Reviewer #1: I Don't Know

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 #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 #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: Review for “Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning method”

Reviewer Comments:

The authors have answered most of the comments. The additional comments are listed as follows:

1. Regarding the institution, the name of the school was not correct according to the official website. School of Electronic Information and Electrical Engineering.

2. In the supplementary Excel file “GFRP-Data”. The amount of the data was very small.

3. Page 1, Line 20, “Glass fiber reinforced polymer composites (Glass Fiber Reinforced Plastics, GFRP) have…”

4. The title of the figure should be displayed below the figure, not above the figure.

5. In Fig.5, please indicate the dimensions of the cross-sections. Check how to draw the cross-section of GFRP concrete-filled columns from the literature.

6. Please redraw Figs. 9 and 10 by using Origin software to fulfill the high-resolution requirement of scientific articles.

7. Reference 42 is empty.

8. The response to Reviewer#4 is missing.

9. The author responds to reviewer#6, comment 3. Please pay attention to the English language.

10. Page 1, Lines 16~18, Abstract, “The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of engineering data analysis.” The field of engineering data analysis is a huge concept, please narrow the scale of this approach.

Reviewer #3: The manuscript (Data Modeling Analysis of GFRP Tubular Filled Concrete Column Based on Small Sample Deep meta Learning Method) has been well-revised and is ready to move on to the next phase of publishing.

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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 #3: No

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Data Modeling Analysis of GFRP Tubular Filled Concrete Column Based on Small Sample Deep meta Learning Method

PONE-D-23-41239R3

Dear Dr. Zhang,

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.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

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 am pleased to inform you that your manuscript entitled "Data Modeling Analysis of GFRP Tubular Filled Concrete Column Based on Small Sample Deep Meta Learning Method" [PONE-D-23-41239R3] has been sent to the previous reviewer for reevaluation. The reviewer is now fully satisfied with your responses and has recommended the paper for publication.

Based on the reviewer's comments and a preliminary assessment of the revised manuscript, I have decided to accept your manuscript, subject to the approval of the editorial board.

The journal office will be in touch with you soon regarding the further steps for the publication process.

Congratulations on your accomplishment, and thank you for choosing PLOS ONE as your publication venue.

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

**********

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

**********

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

Reviewer #1: Yes

**********

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

**********

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

**********

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: (No Response)

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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

**********