PONE-D-20-02288

The Deformation Monitoring of Foundation Pit by BP Neural Network and Genetic Algorithm and its Application in Geotechnical Engineering

PLOS ONE

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Zhihan Lv, Ph.D.

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: To improve the prediction accuracy of foundation pit deformation in geotechnical engineering, and to provide early warning for engineering practices, the author(s) proposes a GA-optimized BPNN algorithm. Then, the prediction results of BPNN model, SVR model, random forest model, and GA-optimized BPNN model are compared. The results show that the prediction results obtained by using both horizontal and vertical displacements as input features have less errors; the prediction results using the space-time domain as input features are closer to the expected output. Compared to the BPNN model, SVR model and random forest model, the proposed GA-optimized BPNN model has lower RMSE values, larger consensus index values, and shorter training time. Therefore, the foundation pit deformation prediction model based on BPNN and GA has strong prediction ability, which can be popularized and applied in similar geotechnical engineering. The author(s) also needs to carefully consider the following points before publication.

1: The keywords of this paper needs revising. The keyword “multi-order spatiotemporal features” is neither mentioned in the title nor in the main text. So, it cannot be used as a keyword of this paper. Please carefully revise this issue.

2: In the method section, 2.4 Deformation monitoring method based on BPNN and GA, this subsection repeats with the contents in its previous subsection, i.e., 2.3 Genetic algorithm and improved BPNN. The author(s) may simply introduce the proposed algorithm and focus on its application in deformation monitoring. Please confirm and revise.

3: In subsection 2.6, the author(s) uses “k-cross validation algorithm” and “particle swarm optimization algorithm”, please supplement the relevant references of these algorithms and cite the corresponding references here.

4: The third section of this paper is about the results and discussions. However, the author(s) only describes the experimental results without any discussions. Please also discuss the implications of these results.

Reviewer #2: This article proposes a GA-optimized BPNN algorithm to predict the deformation and displacement of the foundation pit from three aspects: simple horizontal displacement, simple vertical displacement, horizontal displacement, and longitudinal displacement. To further verify the reliability of the algorithm, the proposed GA-optimized BPNN is compared with other classic prediction algorithms, such as support vector regression models and random forest models. It is confirmed that the algorithm proposed in this article has strong prediction ability and can improve the accuracy and efficiency of the pit deformation monitoring and forecasting model, which is innovative in the field of geotechnical engineering. However, before being published, there are some revisions to be made to this article:

1. In “2.1 Identification and positioning of monitoring point center”, the authors mentioned that the K-means algorithm is used to obtain the robustness of the clustering center, so as to optimize the positioning of the foundation pit monitoring center. Since the content of this section is to introduction the identification and location method of the monitoring point center, the application of K-means algorithm in problem solving should be explained in detail.

2. This article fuses genetic algorithm (GA) with back propagation neural network (BPNN) to form an optimized BPNN. This integrated algorithm is mainly introduced in section 2.3, but the author did not introduce the GA explicitly. The reason of combining these two algorithms is also unclear. Based on what that the authors want to adopt this optimization strategy? Please explain these issues in detail.

3. The formatting of the equations and some symbols that appear in section 2 needs to be adjusted. At present, the formatting of equations and texts is chaotic.

4. In the results section, the author compares the prediction results of the model based on the GA-optimized BPNN algorithm with the SVR model and the RF model. However, as shown in the prediction results in Figure 5, the GA-optimized BPNN model is compared with the RF model. The difference between the prediction results of the two is not obvious, so it cannot be confirmed that the optimized BPNN algorithm has higher prediction accuracy. Please correct it and analyze the reason.

5. In Section 3, the authors listed the application results of the optimization algorithm. However, they did non discuss the research results. The authors should analyze the results and explore the reason why the algorithm has high predictive performance.

6. Please explain the data results in Tables 1 and 2 in the main text, and discuss the reasons for the differences in results.

7. What indicators did the authors use to compare the test results? Despite the indicators listed in the table, there lacks an detailed introduction to these indicators. So, the authors should explain the selection of evaluation indicators in the method section.

Reviewer #3: In this article, a GA-optimized BPNN algorithm is proposed to improve the prediction accuracy of foundation pit deformation in geotechnical engineering, so as to provide early warning for engineering practice. This article takes the monitoring data obtained by digital close-range photogrammetry and the center of the monitoring point optimized by the error compensation method as experimental data to predict the deformation and displacement of the foundation pit. The prediction results of BPNN model, support vector regression model, random forest model and GA-optimized BPNN model are compared. The results show that compared to the BPNN model, SVR model and random forest model, the proposed GA-optimized BPNN model has lower RMSE values, larger consensus index values, and shorter training time. Thus, it can be applied in similar geotechnical engineering projects. Before publication, the following revisions must be made:

1. The second paragraph of the introduction section introduces too much about the research content of this article. The authors may rewrite it. It is enough to simply explain the research methods and contents proposed in this article.

2. In the method section, “2.1 Identification and positioning of monitoring point center”, the authors have also mentioned a series of other algorithms, including “error compensation method”, “support vector regression”, and “K-means algorithm”, etc. However, these algorithms are not introduced in this article. So, please add the corresponding references to these algorithms.

3. In this article, section “3.1 Identification and optimization results of monitoring point center”, the authors have provided the image of the horizontal and vertical displacement of monitoring point A. But in the method section, the authors have only introduced the detection and prediction of monitoring point B and did not mentioned the monitoring point A at all. Where did the results come from? Please carefully revise this.

4. The contents of Table 1 in the results and discussion section need to be quoted in the text and its indications need to be discussed. However, the authors have not quoted Table 1 in any part of this article. Please confirm and revise.

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Reviewer #1: No

Reviewer #2: Yes: Xin Gao

Reviewer #3: No

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The Deformation Monitoring of Foundation Pit by Back Propagation Neural Network and Genetic Algorithm and its Application in Geotechnical Engineering

PONE-D-20-02288R1

Dear Dr. Guo,

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

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Zhihan Lv, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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

Reviewer #2: Yes

Reviewer #3: 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

Reviewer #2: Yes

Reviewer #3: 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: The author has modified the article according to the requirement, the article meets the publication requirement, agrees to publish, good luck.

Reviewer #2: The author has finished the revision and the quality of the article has been improved. This paper can be accepted.

Reviewer #3: In this revision, authors explain and discuss my concerns in detils. Therefore, I recommend this paper is mature enough to meet the publication quality on the PLOS ONE.

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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: Yes: Xin Gao

Reviewer #3: No