Review Reports - Research on 3D Defect Information Management of Drainage Pipeline Based on BIM

Round 1

Reviewer 1 Report

The paper presents a case study of 3D defect information management of drainage pipeline based on BIM. Even the the real and practical application is presented, there are many methodological flaws which need to be improved. In the current design, the paper is positioned as a professional paper, showing a case of how an information system (BIM based) was developed for pipeline maintenance. However, the problem researched is not clear. The stakeholders afected are also not explained. The paper is also very weak on literature when explaining the current developments in this particular area, or what similar studies did in this direction. The methodology is the weakest part. Please remember that the from the paper it must be clear how the same thing can be repeated on different project with different environment (market, country etc.). This is not clear.

The discussion is completely missing, where the authors would confront their findings with the similar undergoings. This is connected with the scarce introduction and reveals the shallow research design. The conclusion, thus, are too bold and not connected with the research problem.

Therefore I suggest that the whole paper is improved by sticking to the classical IMRaD Structure and keeping the interesting BIM application to the pipeline maintenance case.

Here are the Essential Ingredients of a Publication

Introduction: presentation of the problem and clear explanation why this problem is important, and for whom it is important
Research question: clear definition of the question and clear explanation why is this question important (who is waiting to obtain an answer to this question)
Prior research: who has done work in similar areas, what were their successes and failures in trying to obtain answers to similar/related questions?
Proposed methodology for your research: how will I answer my research question?
Detailed description of research results, i.e. answer to your research question using the methodology as above
Conclusions and directions for future research

Author Response

Response to Reviewer Comments

Manuscript Title:

Research on 3D defect information management of drainage pipeline based on BIM

Manuscript #: buildings-1563892

General:

The authors sincerely thank the editor and reviewer for their careful review of our manuscript and for their helpful comments to improve our manuscript. We have considered all the reviews carefully and have revised our manuscript accordingly. Specific responses are briefly summarized under each comment below. We hope that we have addressed most, if not all, of the comments sufficiently. Thank you for considering our revised manuscript for publication in Buildings.

Comments from reviewers:

Reply to reviewer #1

Dear reviewer:

Thank you very much for your valuable questions and advices to improve our manuscript. We have studied your comments carefully and tried our best to revise the paper according to the comments. Specific responses are briefly summarized under each comment below. We hope that we have addressed most, if not all, of the comments sufficiently. Thank you for considering our revised manuscript for publication in Buildings.

Best regards!

Yours sincerely,

Niannian Wang

However, the problem researched is not clear. The stakeholders afected are also not explained.

Author's Response:

Thank you very much for your question. In the first paragraph of the introduction, we briefly introduced the research problems and the affected stakeholders, but it was not clear enough, so we added the following contents in the fourth paragraph of the introduction：

“To sum up, there is a lack of research on quantitative detection of pipeline surface defects, and the application of BIM-based information management platform in three-dimensional defects of drainage pipeline is even less. To facilitate the maintenance personnel to repair the pipeline in time and ensure the safety of urban residents, this study proposes a method to quantify the damage volume and surface area of point cloud of pipe surface defects based on Kinect DK depth camera. Then the drainage pipeline 3D defect information management platform is integrated with BIM and point cloud. It not only solves the problem that the existing pipeline damage cannot be quantitatively evaluated, but also improves the lack of three-dimensional information of pipeline operation and maintenance system.”

The paper is also very weak on literature when explaining the current developments in this particular area, or what similar studies did in this direction.

Author's Response:

Thank you very much for your question about the weak literature in this manuscript. We have made further supplements to the research status in the introduction, as follows:

“Moazzam et al.^[18] collected pavement depth images from concrete and asphalt pavement using a low-cost Kinect sensor. According to depth analysis and pavement image analysis, the area and approximate volume of the pothole are calculated. Turkan et al.^[19] used ground-based laser scanners and proposed a method based on adaptive wavelet neural network (WNN) to automatically detect concrete cracks and other forms of damage.

Chen et al.^[20] developed a standards-based FM system based on BIM technology, and discussed the capture, classification, integration and transmission methods of key FM information. Qian et al.^[21] used BIM bridge model technology and combined with computer vision technology of sensor data to provide computer visualization for bridge deck management and maintenance departments. Lai et al.^[22] proposed an ABIM-based collaborative design and project management platform to address data interoperability issues. Chen et al.^[23] used BIM technology to visualize underground pipe network, so as to quickly and effectively detect the collision relationship between pipes and pipes and between pipes and other underground facilities. Xu et al.^[24] integrated BIM and GIS to analyze urban underground pipe network planning and management, environmental monitoring and evaluation, disaster warning and loss assessment, etc..”

Moazzam I , Kamal K , Mathavan S , et al. Metrology and visualization of potholes using the microsoft kinect sensor[C]// International IEEE Conference on Intelligent Transportation Systems. IEEE, 2013.
Turkan Y , Hong J , Laflamme S , et al. Adaptive wavelet neural network for terrestrial laser scanner-based crack detection[J]. Automation in Construction, 2018, 94(OCT.):191-202.
Chen L , Shi P , Tang Q , et al. Development and application of a specification-compliant highway tunnel facility management system based on BIM[J]. Tunnelling and Underground Space Technology, 2020, 97:103262-.
Qian Z , Li Y , Chen Y . Research on Bridge Deck Health Assessment System Based on BIM and Computer Vision Technology[J]. Journal of Physics: Conference Series, 2021, 1802(4):042047 (10pp).
Lai H , Deng X , Chang T . BIM-Based Platform for Collaborative Building Design and Project Management[J]. Journal of Computing in Civil Engineering, 2019, 33(3):05019001.1-05019001.15.
Chen J , Guo X , Rao H , et al. Application of 3DVisualization of Underground Pipeline Based on BIM Technology[J]. Chinese Journal of Engineering Geophysics, 2018.
Xu W , Zhou Y . Application of BIM + GIS Used in Information Management of Underground Pipeline Network in University Campus[J]. Construction Technology, 2017.

The methodology is the weakest part.

Author's Response:

We appreciate the reviewer’s comments. Due to the lack of specific introduction of methodology, we have modified the manuscript. In the section 3 of the manuscript, the methods used in this study are introduced in detail. The details are as shown in the section 3.1, section3.2, section3.3 and section3.4 of the manuscript.

The discussion is completely missing.

Author's Response:

Thank you very much for your comment. We think it is very reasonable. In view of this problem, we have discussed the results of the experimental study in this manuscript on page 13 of section 5. And the details are as follows:

“5. Results discussion

According to the results of the above experiments, it can be seen that most of the errors between the quantified damage volume and the real volume are within 10%, and the calculated average errors with respect to different volumes are 6.14%, 4.16%, 9.85%, 8.03%, 10.24%, respectively. Some errors are large, which may be caused by the error in the process of data preprocessing. In addition, in the process of shooting with depth camera, the infrared projector is not aligned with the horizontal centerline of the pipeline, The shape and location of damage will affect the accuracy of measurement. However, the maximum error in Table 3 is 17.54%, which is smaller than the maximum errors of 25.29% and 25.92% in the experimental results of Gustavo H. Beckman et al.[14] and Liu et al.[15], indicating that the accuracy is high and can be used. Due to the damage of the actual surface is difficult to measure in the reality, so we used two methods to its surface area is calculated, and the calculation result is the same. According to the quantified damage volume and the size of damage depth, it can be seen that the calculated surface area is reasonable. We use the same method to calculate the size of the surface area and volume. It shows that the final surface area is close to the real value and the calculation method is accurate.”

Therefore I suggest that the whole paper is improved by sticking to the classical IMRaD Structure and keeping the interesting BIM application to the pipeline maintenance case.

Author's Response:

Thank you very much for your advice. We have modified the structure of the full manuscript according to your suggestions, as shown in the introduction:

“The rest of this paper is organized as follows: Section 2 constructs the basic research framework of this paper. Section 3 is the methodology. Section 4 introduces the experiments. Section 5 is a results discussion. Section 6 introduces BIM model construction and the function of drainage pipeline 3D defect information management platform based on BIM, and the last section is a summary of the whole paper. ”

Even in practical application, there are many methodological defects.

Author's Response:

Thank you very much for your comments. The research of this manuscript needs further improvement, which is also our future research direction. The conclusions of this manuscript are revised as follows:

“However, some problems are also found in the research process. Due to the incomplete shooting point cloud caused by the use of cheap depth camera and some improper operations in the shooting process, the point cloud could not be completely consistent with the BIM model data of the pipeline. But these do not have a necessary impact on our research work, and will be further studied and improved in the future. The proposed platform is still in the preliminary exploration stage, and there are still some problems to be solved in practical engineering application. In future research, we will use multiple depth cameras to shoot the whole pipeline, and splice the obtained multiple point clouds with point cloud registration algorithm (ICP algorithm, etc.). Therefore we can study the entire surface of the underground pipeline. We will further develop the research on the platform.”

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper aims to use the depth camera as a sensor to quantitatively detect the volume and area of the pits on the concrete pipe and to construct the “detect information management platform”.

The idea behind this paper is interesting, the paper is well structured, the introduction encompasses the main concepts related to the topic, the research framework is well explained, and the preliminary results provide a good proof of concepts. However, in my opinion, the article in its current form is not yet suitable for publication. It should be supplemented. Here are some comments to take into consideration while revising the paper.

In section 4.1.2. Data denoising, “Statistical outlier removal is adopted to eliminate redundant outliers” How you can judge the removed points were outliers not deformation? Also, the threshold value used is missing in the text.
In page 8, “The geometric dimensions of the pipe are 49cm outer diameter, 39cm inner diameter and 210cm long”. According to the text, the thickness of the wall is equal 5 cm. Regarding to table3 page 11, table 4 page 12, if we divide the volume by the deformation area from table 4 to find the average depth, most values are more than 5 cm which means there are holes. ??!
In table 3 page 11, does it make sense to calculate the average error with respect to the distance? What does it mean? (just stop and shoot at distance that gives you the minimum error). In my opinion, you should calculate the average of error with respect to the volume because in one shoot you may have more than deformation area of different volumes.
The registration methodology of point cloud of damage with the BIM model is completely absent in the text. How the registration was done? what about the accuracy?
In my opinion, the authors have to mention the site limitation, restrictions and how they will overcome it when collecting data. How they can photograph the whole surface of the pipe located underground, in 360 degrees? How they can register multiple scans?

Author Response

Response to Reviewer Comments

Manuscript Title:

Research on 3D defect information management of drainage pipeline based on BIM

Manuscript #: buildings-1563892

General:

Comments from reviewers:

Reply to reviewer #2

Dear reviewer:

Best regards!

Yours sincerely,

Niannian Wang

In section 4.1.2. Data denoising, “Statistical outlier removal is adopted to eliminate redundant outliers” How you can judge the removed points were outliers not deformation? Also, the threshold value used is missing in the text.

Author's Response:

Thank you very much for your questions on point cloud denoising. We have carefully considered them. Point to point reply is following：

Q1: “Statistical outlier removal is adopted to eliminate redundant outliers” How you can judge the removed points were outliers not deformation?

A1: Statistical filter is mainly used for removal of obvious outliers. It does a statistical analysis of the neighborhood of each point and trims off some do not accord with standard of a certain point, removing method based on sparse outliers in the input data to point the calculation of the distance to the adjacent point distribution, for each point, it to all adjacent points average distance calculation, assuming that the result is a gaussian distribution. The shape is determined by mean and standard deviation, and points whose average distance is outside the standard range can be defined as outliers and removed from the data. Therefore, statistical filtering is mainly used to remove redundant outliers, and it can be judged that the deleted points are outliers rather than deformation points. The following changes are made on page 5 of the manuscript:

“Statistical outlier removal is adopted to eliminate obvious redundant outliers. Its principle is to select query points, make a statistical analysis on the field of each point. It eliminate some points that do not meet a certain standard and calculate the average distance between the query points and all point sets in its neighborhood as well as the mean value of these average distancesand standard deviation. It assumes that the result is gaussian distribution. The distance thresholdis expressed as:

（4）

Whereis the mean value;is the standard deviation;is the standard deviation multiple threshold;is the distance threshold.

Then, for the whole point cloud, the point whose average distance is greater than that of the query point and all point sets in the neighborhood is identified as the outliers and removed from the data.”

Q2: Also, the threshold value used is missing in the text.

A2: we have added and modified the distance threshold in the corresponding position on page 11 of the manuscript：

“Firstly, statistical filtering algorithm is used to denoise the acquired point cloud data. The distance threshold is set to 2 for data denoising^[15].”

In page 8, “The geometric dimensions of the pipe are 49cm outer diameter, 39cm inner diameter and 210cm long”. According to the text, the thickness of the wall is equal 5 cm. Regarding to table3 page 11, table 4 page 12, if we divide the volume by the deformation area from table 4 to find the average depth, most values are more than 5 cm which means there are holes. ??!

Author's Response:

Thank you very much for your comment of data calculation. We apologize for the wrong conversion of units. We have made corrections to the surface area data in Table 4 on page 13 and Figure 14 on page 16, as shown below：

Tab. 4 Damage surface area: the test bench distance is 100cm-200cm

Distance（cm）		100	125	150	175	200
1	Surface area（cm²）	104.23	103.63	128.08	108.16	121.58
1	Average area（cm²）	113.14
2	Surface area（cm²）	217.86	230.51	221.79	226.87	223.03
2	Average area（cm²）	224.01
3	Surface area（cm²）	322.92	333.70	348.85	309.73	317.54
3	Average area（cm²）	326.55

Fig.14 Defect point cloud information

In table 3 page 11, does it make sense to calculate the average error with respect to the distance? What does it mean? (just stop and shoot at distance that gives you the minimum error). In my opinion, you should calculate the average of error with respect to the volume because in one shoot you may have more than deformation area of different volumes.

Author's Response:

Thank you very much for your advice. The author agree with it. According to your suggestion, we calculated the average error relative to the volume, and modified Table 3 as follows:

Tab. 3 Damage volume: the test bench distance is 100cm-200cm

Distance（cm）		100	125	150	175	200
1	Real volume（cm³）	50.3
	Calc. volume（cm³）	48.7	48.9	47.5	46.5	47.4
	Error（%）	3.18	2.78	5.57	7.55	5.77
2	Real volume（cm³）	126.0
	Calc.volume（cm³）	115.5	129.4	103.9	123.5	110.6
	Error（%）	8.73	2.70	17.54	1.98	12.22
3	Real volume（cm³）	214.5
	Calc. volume（cm³）	198.8	199.5	228.3	183.3	187.2
	Error（%）	7.32	6.99	6.43	14.55	12.73
Average error（%）		6.41	4.16	9.85	8.03	10.24

The registration methodology of point cloud of damage with the BIM model is completely absent in the text. How the registration was done? what about the accuracy?

Author's Response:

Thank you very much for your questions about the registration of damage point cloud and BIM model. Point to point reply is following:

Q1: How the registration was done?

A1: We have introduced the registration methods of the damage point cloud and BIM model, and the specific modifications in page 14 are as follows:

“After the point cloud is imported into the model, to register the position between them. Registration is mainly done in Revit software. First, after point cloud data is imported into Revit, Revit will place the world origin of point cloud, namely (0,0,0) point, at the origin of Revit project. Then, in the site plane, the Revit project origin can be regarded as the project base point. The north direction of point cloud (0,1, 0) Overlapped with "project North" in Revit to keep the coordinate system in the same direction. Then rotate the point cloud to match the position of the model. ”

Q2: what about the accuracy?

A2: After the registration, the registration accuracy of the two should be judged. The method of traversing every point in the point cloud is adopted to obtain the direction from the point cloud emission origin to the point, and the laser emission direction is obtained. Further, a forward projection and a reverse projection are carried out in this direction to try to find the plane that can be projected to, and calculate the corresponding distance. Most point clouds have high registration accuracy with an error of less than 5mm. We make the modification on page 14 as follows：

“The registered point cloud and pipeline model are shown in Figure 12. After registration, the method of traversing every point in the point cloud is adopted to obtain the direction from the point cloud emission origin to the point, and the laser emission direction is obtained. Further, a forward projection and a reverse projection are carried out in this direction to try to find the plane that can be projected to, and calculate the corresponding distance. Finally, the shortest distance is taken as the error of this point. The registration accuracy of most point clouds is within 5mm. In the following research, we will further improve the registration accuracy. However, it can be found that some point clouds are not completely consistent with the BIM model. The main causes of these errors are external interference during shooting, camera accuracy and some improper operation factors.”

In my opinion, the authors have to mention the site limitation, restrictions and how they will overcome it when collecting data. How they can photograph the whole surface of the pipe located underground, in 360 degrees? How they can register multiple scans?

Author's Response:

Thank you very much for your advice. There are some limitations in data collection, the next step is to adopt multiple depth camera aboard machine, and to obtain multiple point cloud using the algorithm of point cloud registration (ICP algorithm, etc.) for Mosaic. So we can further study the entire surface of underground pipeline.We modify the conclusion as follows：

“In future research, we will use multiple depth cameras to shoot the whole pipeline, and splice the obtained multiple point clouds with point cloud registration algorithm (ICP algorithm, etc.). Therefore we can study the entire surface of the underground pipeline. We will further develop the research on the platform.”

Author Response File: Author Response.pdf

Reviewer 3 Report

The manuscript proposes a method to quantify the damage volume and surface area of point cloud of pipe structure surface defects which then integrates the drainage pipe-line 3D defect information management platform with BIM and point cloud. The paper is well-written. The reviewer would like the authors to address the following comments:

Please quantify the accuracy of the calculation in the sentence below (Abstract):

“The verification experiment results show that the accuracy of the calculation results is high.” This statement is very abstract and vague.

Please provide the details of data processing. Which procedures are applied for data processing?
Please provide the details of the data collected and the forms submitted to the database (Section 3.3)
Please provide the technical specifications required/used of the server to run this platform?
It is stated that this distance is adjusted to 100cm, 125cm, 150cm, 175cm and 200cm is convenient for data comparison and analysis. A reference or a justification is needed for the distance set between the camera and the pipeline? (Section 4.1.1)
Which mitigation measures could be considered to avoid external environmental noise? (Section 4.1.2) Pls discuss the impact of noise on the accuracy?
It is stated that the cylinder model segmentation algorithm can obtain the cylinder surface in the point cloud by nonlinear least square fitting. In addition, the RANSAC random sampling consistency robust estimation is used to obtain the cylindrical model coefficients. A reference or a justification is needed to support these selections (Section 4.1.3)
What is the accepted error between the quantified damage volume and the real volume? Pls provide a reference and justify that the obtained results in Table 3 can be used? (Section 4.1.4)
Please explain or clarify what B/S structure or a B/S model is on its first occurrence in the manuscript.
What is the LOD of the pipeline model? What should be the minimum LOD level?
Please specify the next steps and further studies.

Author Response

Response to Reviewer Comments

Manuscript Title:

Research on 3D defect information management of drainage pipeline based on BIM

Manuscript #: buildings-1563892

General:

Comments from reviewers:

Reply to reviewer #3

Dear reviewer:

Best regards!

Yours sincerely,

Niannian Wang

Please quantify the accuracy of the calculation in the sentence below (Abstract):

“The verification experiment results show that the accuracy of the calculation results is high.” This statement is very abstract and vague.

Author's Response:

Thank you very much for your comments. The expression of this sentence in the abstract is indeed not very clear, so we have modified the abstract as follows:

“The verification experiment results show that the error between the quantized volume and the real volume is mostly within 10%, the minimum error is 1.98% and the maximum error is 17.54%, indicating high accuracy.”

Please provide the details of data processing. Which procedures are applied for data processing?

Author's Response:

Thank you very much for your question. Point cloud data processing mainly includes the point cloud data denoising and segmentation, the photo source code repository program for processing. Point cloud denoising mainly adopts statistical filtering algorithm to remove excess noise points. Pipeline model is extracted through cylinder model segmentation algorithm, and then uses European clustering algorithm to extract damaged point cloud from pipeline point cloud. Damage quantization is mainly calculated by Cloud Compare software. The data processing is introduced in detail in Section 3.2 of this manuscript. It is revised on page 3 of the manuscript as follows:

“The research framework of this paper is shown in Figure 1, first of all,the development of drainage pipeline 3D defect information management platform is introduced based on the system development method, function construction framework and overall technical framework, then puts forward the methods of data processing , mainly including 3D defect point cloud processing and damage information quantitative process. Three-dimensional point cloud processing includes point cloud data denoising and segmentation. Statistical filtering algorithm is used to remove redundant noise points. Pipeline model is extracted through cylinder model segmentation algorithm, and then European clustering algorithm is used to extract damaged point cloud from pipeline point cloud. MeshLab software is used for point cloud reconstruction and the quantization of damage information is mainly calculated by Cloud Compare software. ”

Please provide the details of the data collected and the forms submitted to the database (Section 3.3)

Author's Response:

Thank you for your question. The detailed information of collected data mainly includes BIM model of pipeline, BIM model of link point cloud, point cloud shooting time, information of defect point cloud, BIM model information, information of inspection personnel and maintenance personnel, professionals communication and health status of pipeline.The changes made on page 9 of the manuscript and the forms submitted to the database in Table A of Appendix A are as follows：

“The data layer is the collection of all data of the platform. The processed data is submitted to the database through forms. The database includes model data, damage data, professional communication, inspection personnel, maintenance personnel and health status. The forms submitted to the database are in Table A of Appendix A. The database is constructed by structured query language (SQL)to facilitate inquiry and operation^[³⁸^].”

Table A. The forms submitted to the database.

Model data
Pipe name
Model link
Damage data
Pipe name		Point cloud shooting time
Material		Number of damages
Damage volume		Diameter
Model link
Professional communication
Pipe name
Detection situation
Maintenance countermeasures
Inspection personnel
Name
Age
Contact information
Technical background
Scope of work
Maintenance personnel
Name
Age
Contact information
Technical background
Scope of work
Health status
Pipe name
Health level
Service life

Please provide the technical specifications required/used of the server to run this platform?

Author's Response:

Thank you for your question. The required server specifications for running the platform are 1 core, 1 GB memory and 1 MB bandwidth.The following changes are made on page 15 of the manuscript：

“The damage data obtained from the above test and the pipeline model are integrated into the constructed information management platform. Furthermore, the functions of the platform are realized by leveraging B / S architecture and database technology. This section mainly introduces the specific implementation of the functions of the four modules of the system (BIM model browsing, defect point cloud information, professional service and health information feedback). The required server specifications for running the platform are 1 core, 1 GB memory and 1 MB bandwidth.”

It is stated that this distance is adjusted to 100cm, 125cm, 150cm, 175cm and 200cm is convenient for data comparison and analysis. A reference or a justification is needed for the distance set between the camera and the pipeline? (Section 4.1.1)

Author's Response:

Thank you very much for your suggestion. The distance between the camera and the pipeline is mainly determined according to the detection range of the Kinect DK depth camera. Table 1 of this paper is the specific parameters of the depth camera, and its detection range is 0.5m to 3.86m. Section 4.1 is modified as follows:

“The detection distance is mainly selected and set reasonably according to the parameters of depth camera in Table 1 above. In order to reduce the influence of external environmental noise, this measurement test was carried out indoors.”

Which mitigation measures could be considered to avoid external environmental noise? (Section 4.1.2) Pls discuss the impact of noise on the accuracy?

Author's Response:

Thank you for your question. (1) Since noise is inevitable in experimental shooting, in order to reduce noise interference, this paper firstly adopts indoor test, mainly to avoid excessive noise under direct sunlight. Secondly, it is necessary to shoot in a relatively quiet and clean environment to avoid the interference of surrounding noise and sundry objects. The final test operation is accurate. The content in page 6 of the manuscript is revised as follows:

“Then, for the whole point cloud, the point whose average distance is greater than that of the query point and all point sets in the neighborhood is identified as the noise point and removed from the data. Point cloud denoising can reduce the amount of data and improve the efficiency of feature extraction. However, we should try to reduce the appearance of noise. Firstly, direct sunlight should be avoided in the indoor experiment. Secondly, a relatively quiet and clean environment is required while shooting. Finally, the experiment operation should be correct to improve the accuracy of the experiment.”

When the external noise is large, the shooting accuracy of the depth camera may be affected. We can consider the influence of illumination on the test results, and analyze the final results by adding outdoor comparative tests to obtain the influence of noise on the accuracy. The content inpage 10 of the manuscript is revised as follows:

“In order to reduce the influence of external environmental noise, this measurement test was carried out indoors. In order to discuss the influence of noise on the experiment, we carried out a comparative experiment with or without light, and found that the experimental results are basically the same, indicating that the influence of light on the experiment is not significant. In addition, we can also analyze the effect of strong sunlight on the results by adding outdoor comparative experiments.”

It is stated that the cylinder model segmentation algorithm can obtain the cylinder surface in the point cloud by nonlinear least square fitting. In addition, the RANSAC random sampling consistency robust estimation is used to obtain the cylindrical model coefficients. A reference or a justification is needed to support these selections (Section 4.1.3)

Author's Response:

Thank you very much for your comment. As the pipe model in the point cloud is cylinder, RANSAC random sampling consistency algorithm, namely cylinder model segmentation algorithm, can extract a cylinder model from the point cloud with noise. SACMODEL_CYLINDER model is provided in PCL, which is defined as cylinder model. Pipe point cloud can be extracted separately, and the objective of this method is clear. The program is simple and easy to understand, so the cylinder model segmentation algorithm is preferred to segment the pipe point cloud. In the page 6 of this manuscript, the modification is as follows:

“RANSAC random sampling consistency algorithm, namely cylinder model segmentation algorithm, can extract a cylinder model from the point cloud with noise^[15]. SACMODEL_CYLINDER model is defined as the cylinder model provided in PCL, and this method has clear objectives, simple procedures and easy to understand. Therefore, the cylinder model segmentation algorithm is preferred to segment the pipe point cloud. The cylinder model segmentation algorithm can obtain the cylinder surface in the point cloud by nonlinear least square fitting. The algorithm uses random sampling consistency estimation to extract the key part, namely the pipe model, from the point cloud. On the basis of filtering the data, firstly, the normal of the point cloud needs to be estimated, and then the plane model in the point cloud is fitted. Secondly, the RANSAC random sampling consistency robust estimation is used to obtain the cylindrical model coefficients.”

What is the accepted error between the quantified damage volume and the real volume? Please provide a reference and justify that the obtained results in Table 3 can be used? (Section 4.1.4)

Author's Response:

Thank you for your suggestion. In references 14 and 15 of this manuscript, Gustavo H. Beckman et al. and Liu et al. quantified the damaged volume of the flat plate. Due to the inevitable influence of accidental errors in data processing, when the shooting distance of the depth camera is between 100cm-200cm, The maximum errors of quantified damage volume and actual damage volume obtained in references 14 and 15 are 25.29% and 25.92% respectively, but each test is contingent, so the acceptable error between quantified damage volume and actual damage volume is generally within 25%. The maximum error in Table 3 is 17.54%, which is smaller than 25%. Therefore, the obtained results in Table 3 can be used. In the results discussion of this manuscript, the modifications are as follows:

“In addition, in the process of shooting with depth camera, the infrared projector is not aligned with the horizontal centerline of the pipeline, The shape and location of damage will affect the accuracy of measurement. However, The maximum error in Table 3 is 17.54%, which is smaller than the maximum errors of 25.29% and 25.92% in the experimental results of Gustavo H. Beckman et al.^[14]and Liu et al.^[15], indicating that the accuracy is high and can be used.”

Please explain or clarify what B/S structure or a B/S model is on its first occurrence in the manuscript.

Author's Response:

Thank you for your question. B/S structure is Browser/Server abbreviation, refers to the Browser/Server mode. In this structure, the user work interface is realized through the Browser, a little part of the transaction logic is implemented in the front end (Browser), but the main transaction logic is implemented in the Server side (Server), forming the so-called three-tier 3-tier structure. Section 3.4.3 is modified as follows:

“The platform is mainly built on the basis of B/S structure mode（B/S structure is Browser/Server abbreviation, refers to the Browser/Server mode. In this structure, the user work interface is realized through the Browser, a little part of the transaction logic is implemented in the front end (Browser), but the main transaction logic is implemented in the Server side (Server), forming the so-called three-tier structure.）”

10.What is the LOD of the pipeline model? What should be the minimum LOD level?

Author's Response:

Thank you for your question. (1) LOD of pipeline model refers to the degree of model meticulousness, which refers to the process in which the pipeline model unit develops from the lowest level of conceptual design to the highest level of demonstration accuracy. (2) The minimum LOD level for the pipeline model is LOD100, which is the concept drawing stage. The content in page 14 of the manuscript is revised as follows:

“Figure 11 is the single pipe model established by Revit software. The minimum LOD level for the pipeline model is LOD100. LOD of pipeline model refers to the degree of model meticulousness, which refers to the process in which the pipeline model unit develops from the lowest level of conceptual design to the highest level of demonstration accuracy.”

11.Please specify the next steps and further studies.

Author's Response:

Thank you very much for your comments. What we are going to do next is to use the machine equipped with multiple depth cameras to shoot the whole pipeline, and splice the obtained multiple point clouds with point cloud registration algorithm (ICP algorithm, etc.) to improve the accuracy of calculation. Further research and improve the information management platform, so that it can be really applied to the actual project. We have modified the conclusion as follows:

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Congratulations to the authors.

Author Response

Thank you very much for reviewer's recognition of our manuscript.

Reviewer 2 Report

The manuscript has been significantly improved. Still some minor corrections to warrant publication. I suggest to the authors to proofread the English style of the paper, the manuscript contains some errors that must be corrected.

Here are some specific comments to take into consideration while revising the paper

In page 4, The depth image is a set of Z-coordinate values of each pixel of the image, In millimeters. I think “in millimeters”
In page 11, “The distance threshold D is set to 2 for data denoising”. Unit must be added 2 mm or 2 cm…
In page 14, The minimum LOD level for the pipeline model is LOD100. It should be “The minimum Level of Detail (LOD) for the pipeline …… ” .
In page 14, The caption of Figure 12 should be modified
In page 17, “The research contributions are as follows: (1) based on the pipeline surface damage”. It should be (1) based on the…..

Author Response

Dear reviewer:

Best regards!

Yours sincerely,

Niannian Wang

In page 4, The depth image is a set of Z-coordinate values of each pixel of the image, In millimeters. I think “in millimeters”

Author's Response:

Thank you very much for your comment. We have made corresponding modifications on page 4 of the manuscript as follows:

“The depth image is a set of Z-coordinate values of each pixel of the image, in millimeters.”

In page 11, “The distance threshold D is set to 2 for data denoising”. Unit must be added 2 mm or 2 cm…

Author's Response:

Thank you very much for your comment. We have made the following modifications on page 11 of the manuscript:

“Firstly, statistical filtering algorithm is used to denoise the acquired point cloud data. The distance threshold D is set to 2mm for data denoising^[15]. ”

3.In page 14, The minimum LOD level for the pipeline model is LOD100. It should be “The minimum Level of Detail (LOD) for the pipeline …… ” .

Author's Response:

Thank you very much for your advice. We have made the following modification on page 14 of the manuscript:

“ The minimum Level of Detail (LOD) for the pipeline model is LOD 100. ”

4.In page 14, The caption of Figure 12 should be modified.

Author's Response:

Thank you very much for your advice. We have modified the caption of Figure 12 as follows:

Figure 12. BIM model combined with point cloud data

5.In page 17, “The research contributions are as follows: (1) based on the pipeline surface damage”. It should be (1) Based on the…..

Author's Response:

Thank you very much for your comment. The corresponding changes we made on page 17 are as follows：

“The research contributions are as follows: (1) Based on the pipeline surface damage, a method to quantify the volume and surface area of mesh reconstruction of defective point cloud of drainage pipeline is proposed, which is convenient for the later repair of the pipeline. ”

Author Response File: Author Response.pdf