Cost-Effective Inspection of Rebar Spacing and Clearance Using RGB-D Sensors
Round 1
Reviewer 1 Report
The authors propose an algorithm that facilitates inspecting the positions of rebars and the cover of concrete using a new generation low-cost RGB-D sensor to find discrepancies that need to be corrected before pouring the concrete. The experimental and comparative results indicate the proposed approach as a promising tool to detect and post-process RC structure inspection cost-effective, non-defective, and safe. The paper is well written and presents a good contextualization and effective contributions, no further observations
Author Response
RE: Thanks for your comments. We appreciate your time and comments on our manuscript.
Reviewer 2 Report
In a general way, the paper describes the development and implementation of an algorithm that automatically inspects the positions of rebars with the data obtained by a low-cost RGB-D sensor. The manuscript is well structured and organized. In addition, the manuscript present new and relevant results to the SHM and concrete community.
However, there are some minor changes that must be revised:
- Flowchart of figure 2 must be better described and detailed. Also, the figure quality (dpi) need improvements.
- Figure 4 and 5 could be incorporated in the same flowchart (figure 2)
- More details is needed to show how the images were experimentally obtained (figures?)
Author Response
RE：Thanks for your comment and suggestion to make the reading easier by combining flowcharts and we agree it is beneficial to simplify as much as possible. Following your recommendation, we have combined old Figure 2 and old Figure 4 into a more general flowchart (new Figure 2). We agree it is more comprehensive this way and it can cover the general framework of the paper. In addition, we make new Figure 2 in a higher quality as required, thank you for bringing this up. On the other hand, Figure 5 is a more specific explanation for the development of algorithm, and it would be overwhelming for the reader to have to understand the programming details this early in the paper, so we think it can be standing on its own later on in the manuscript, which is new Figure 5. However, we can consider a new combination if the reviewer believes it is necessary.
Finally, we have added more details about the data collection using RGB-D sensor to section 3.1 data acquisition part as shown below:
“Data collection using RGB-D sensor can be conducted by creating a recording of the scanning using k4arecorder.exe. Then we can use the Azure Kinect Viewer to play back a recording. To be specific, launch k4aviewer.exe first, then Unfold the Open Recording tab and open your recording. The recording of dataset will be shown as below.”
Reviewer 3 Report
The paper is focused on project model, developing an interesting integrated approach. The approach is well explained and the example allows readers to understand the whole problem, and also to replicate it. Despite the paper is well presented and structured, I suggest introducing the main results in the abstract in brief, so that it is possible to have an idea about the overall ones. Moreover, the introduction, in the presented form, is quite negligible. I suggest restructuring it explaining in-depth the problem. The English form has to be revised for the presence of some typos. The literature review appears not completely updated.
Some latest results on safety and risk assessment can be found in: - Reliability estimation of reinforced slopes to prioritize maintenance actions Bahootoroody, F., Khalaj, S., Leoni, L., De Carlo F. Di Bona, G., Forcina, A. International Journal of Environmental Research and Public Health, 2021, 18(2), pp. 1-12, 373 - Critical risks method (CRM): A new safety allocation approach for a critical infrastructure Di Bona, G., Forcina, A., Falcone, D., Silvestri, L. Sustainability (Switzerland), 2020, 12(12), 4949
Author Response
RE：Thank you for your constructive suggestions. We have addressed them all and we summarize here the changes we did:
1. We have modified the abstract by adding the main results.
……, The results conclude that the RGB-D sensor can get a cost-effective inspection for rebar spacing and clearance with an acceptable tolerance. The evaluation of rebar spacing result shows the maximum standard deviation for rebar spacing is 0.34 inch (8.64 mm) between longitudinal rebar 2 and 3, which is true as the rebar construction and traditional tape measurement results. The concrete cover estimation result shows the maximum standard deviation for rebar cage concrete cover is 0.19 inch (4.83 mm) for longitudinal rebar 3. The issues of new RGB-D sensor scans setting and the test results will be helpful for practitioners in improving construction quality.
2. In the meantime, we modified the introduction part based on your comments. Incorrect rebar placement and incorrect concrete cover will result in cracking, spalling, short lived structures as shown below.
The error between the rebar construction and rebar design drawing should not exceed the tolerance. The incorrect rebar placement and correct cover can lead to reduced durability due to possible cracks, spalling, and rebar corrosion. The consequences of low-quality rebar construction will result in the short service life of structures and the possibility of safety issues of collapses. Additionally, the rebars must be appropriately surrounded by the concrete; thus, spacing between the rebars should be large enough for the gravel in concrete and the vibrator to pass among them. Therefore, rebar placement inspection could impact structural performance, serviceability, safety, and future maintenance cost [4].
3. We modified the literature by adding few latest results related to this research. We agree adding these references benefit the general reader who will become more familiar with the context of sustainability in a more comprehensive way. We have added them in the research background chapter, under “Literature Review”:
BahooToroody, Farshad, et al. "Reliability estimation of reinforced slopes to prioritize maintenance actions." International Journal of Environmental Research and Public Health 18.2 (2021): 18(2), pp. 1-12, 373.
Di Bona, G., Forcina, A., Falcone, D., & Silvestri, L. (2020). Critical risks method (CRM): A new safety allocation approach for a critical infrastructure. Sustainability, 12(12), 4949.
