the result of the recorded data from the camera. It makes the process more complicated in terms of computational time and effort. However, the preprocessing step in CloudCompare will help to remove the background and divide the top and bottom layer based on the Z elevation. Eventually, implementing the algorithm on the refined data can be done. Further Research The results show that the RGBD camera can be a low-cost alternative to enhance construction inspectors’ capabilities in the global assessment of RC concrete construction, spe- cifically rebar size distinction. The future focus of research is to incorporate RGBD cameras with AI techniques to develop tools to assist the construction inspectors in (a) checking size, number, location, and spacing against shop drawings and plans (b) checking rebar for proper clearance between bars, joints, and side forms (c) checking the adequacy of ties and tie downs (d) checking splice locations and lengths against shop drawings and plans (e) assuring bars with suitable coating are being used as noted in the plan (f) detecting coating deficien- cies and (g) recording the placement of rebar in daily reports submitted by the inspector. If successful, the same principles can be extended to assist the construction inspector with post-placement of rebar activities, including assuring the rebar is not displaced during concrete placement and the proper cover is maintained. The final product will be to generate a report of deficiencies on-site so that they can be addressed in a timely fashion to promote durability while avoiding construc- tion delays. Additionally, the digital as-built model showing the rebar location and size can be used to report and record the progress of rebar construction in real time. Requirements on rebar spacing can also be incorporated into the process as an additional measure to assure design drawings meet the appli- cable specifications (such as AASHTO and ACI). This research also has potential for other applications, such as changes in safety equipment and obstructions/depressions in the con- struction area to increase worker safety, as well as verification of pipeline/bridge layouts. Another application that can be considered for the RGBD camera is to combine it with other technologies such as UAVs. The RGBD camera can be mounted on the UAV, which can help the inspector to collect data from a large area in a short time. It should be noted that the camera needs to be mounted on the drone, so a 3D printed setup or gimbal can be used to reduce the vibration and subsequent noises in the data. Then, the inspector would be able to have a digital as-built model of the construction site. Additionally, the inspector can use AR technology and wear a headset on-site to see the real-time 3D data created by the RGBD camera in front of their eyes as it shows which rebar should be replaced in holograms. Also, in the future, the authors would like to compare the results from RGBD and LiDAR, as each has its own advantages. The RGBD camera’s application is not limited to the structural construc- tion site it can also be utilized in quality control of under- ground utilities as the inspector crew can create a 3D point cloud data model of the underground facilities to detect the defects. Conclusion The RGBD camera used in this study has the capability of generating 3D point cloud data of the structure with different features and modes based on the user-specific application. The authors presented a method of using this camera to rec- ognize rebar #3 between rebar of different sizes using the Pratt circle fitting algorithm to estimate the diameter. The results demonstrated that the developed algorithm can automati- cally and quickly acquire measurements comparable to a tape measure utilizing the inexpensive handheld RGBD camera. This is useful for the inspector to distinguish between rebar of similar sizes, as it is hard to differentiate between them from a distance with human eyes. This paper also explained the importance of using tech- nologies for automation in the quantification of inspection. It decreases the amount of time required for manually recording data and has the capability of creating permanent construc- tion records. It also lets the inspectors monitor the whole structure’s state precisely and comprehensively and creates a large amount of data that could be a source of information and comparison for the next inspection. The results showed that the RGBD camera holds promise as a low-cost alternative to enhance construction inspectors’ capabilities in the global assessment of RC concrete construction, specifically rebar size distinction. ACKNOWLEDGMENTS The authors of this paper acknowledge the support from the Department of Civil, Construction, and Environmental Engineering of the University of New Mexico the Transportation Consortium of South-Central States (Tran-SET, Award Number 69A355 l 747106 Grant ID: 2RTR1-2TR13) the Federal Railroad Administration (FRA) Award No. 693JJ621C000010 and the New Mexico Department of Transportation (NMDOT) for providing funding to graduate students to conduct this research. We appreciate the input and assistance from Ronan Reza. REFERENCES Akinci, B., F. Boukamp, C. Gordon, D. Huber, C. Lyons, and K. Park. 2006. “A formalism for utilization of sensor systems and integrated project models for active construction quality control.” Automation in Construc- tion 15 (2): 124–38. https://doi.org/10.1016/j.autcon.2005.01.008. Almadhoun, R., T. Taha, L. Seneviratne, J. Dias, and G. Cai. 2016. “A survey on inspecting structures using robotic systems.” International Journal of Advanced Robotic Systems 13 (6). https://doi.org/10.1177/1729881416663664. Ayele, Y. Z., M. Aliyari, D. Griffiths, and E. L. Droguett. 2020. “Automatic crack segmentation for UAV-assisted bridge inspection.” Energies 13 (23): 6250. https://doi.org/10.3390/en13236250. Biswas, K.K., and S.K. Basu. 2011. “Gesture recognition using Microsoft Kinect®.” The 5th International Conference on Automation, Robotics and Applications, Wellington, New Zealand. https://doi.org/10.1109/ ICARA.2011.6144864. Boukamp, F., and B. Akinci. 2007. “Automated processing of construction specifications to support inspection and quality control.” Automation in Construction 17 (1): 90–106. https://doi.org/10.1016/j.autcon.2007.03.002. BrÜhwiler, E. 2009. “Improving Safety and Durability of Civil Structures.” In Damage Assessment and Reconstruction after War or Natural Disaster, eds A. Ibrahimbegovic and M. Zlatar. NATO Science for Peace and Security Series C: Environmental Security. Dordrecht: Springer. https://doi. org/10.1007/978-90-481-2386-5_3. J A N U A R Y 2 0 2 3 • M AT E R I A L S E V A L U AT I O N 53 2301 ME Jan New.indd 53 12/20/22 8:15 AM

Cawley, B., and R. Duval. n.d. “E-Construction Factsheet.” Accessed 28 October 2022. aii.transportation.org/Documents/eConstruction/ econstruction-factsheet.pdf. Chemrouk, M. 2015. “The deteriorations of reinforced concrete and the option of high performances reinforced concrete.” Procedia Engineering 125:713–24. https://doi.org/10.1016/j.proeng.2015.11.112. Chen, S., D.F. Laefer, E. Mangina, S.M. Iman Zolanvari, J. Byrne. 2019. “UAV bridge inspection through evaluated 3D reconstructions.” Journal of Bridge Engineering 24 (4). https://doi.org/10.1061/(ASCE)BE.1943- 5592.0001343. Chernov, N. 2022. Circle Fit (Pratt method) (https://www.mathworks. com/matlabcentral/fileexchange/22643-circle-fit-pratt method), MATLAB Central File Exchange. Retrieved July 13, 2022. Concrete Construction. 2005. “Placing Reinforcing Steel.” Concrete Construction, 13 March 2005, concreteconstruction.net/how-to/ construction/placing-reinforcing-steel_o. Correa, D.S.O., D.F. Sciotti, M.G. Prado, D.O. Sales, D.F. Wolf, and F.S. Osorio. 2012. “Mobile Robots Navigation in Indoor Environments Using Kinect Sensor,” 2012 Second Brazilian Conference on Critical Embedded Systems, Sao Paulo, Brazil. https://doi.org/10.1109/CBSEC.2012.18. Cusson, D. 2009. “Durability of repaired concrete structures.” In Woodhead Publishing Series in Civil and Structural Engineering: Failure, Distress and Repair of Concrete Structures, ed. Norbert Delatte, 296–321. Sawston, UK: Woodhead Publishing. https://doi.org/10.1533/9781845697037.2.296. De Souza, T., A.P. Annan, J.D. Redman, and N. Hu. 2004. “Monitoring the GPR response of curing concrete.” 16th World Conference on NDT, Montreal, Canada. El-laithy, R.A., J. Huang, and M. Yeh. 2012. “Study on the use of Micro- soft Kinect for robotics applications.” Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium, Myrtle Beach, SC. https:// doi.org/10.1109/PLANS.2012.6236985. Estes, A.C., and D.M. Frangopol. 2003. “Updating bridge reliability based on bridge management systems visual inspection results.” Journal of Bridge Engineering 8 (6): 374–82. https://doi.org/10.1061/(ASCE)1084- 0702(2003)8:6(374). Gucunski, N., S.-H. Kee, H. La, B. Basily, A. Maher, and H. Ghasemi. 2015. “Implementation of a fully autonomous platform for assessment of concrete bridge decks RABIT.” Structures Congress 2015, Portland, OR. https://doi.org/10.1061/9780784479117.032. Ivanovic, A., L. Markovic, M. Car, I. Duvnjak, and M. Orsag. 2021. “Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manip- ulators.” Applied Sciences (Basel, Switzerland) 11 (18): 8279. https://doi. org/10.3390/app11188279. John Samuel, I., O. Salem, and S. He. 2022. “Defect-oriented supportive bridge inspection system featuring building information modeling and augmented reality.” Innovative Infrastructure Solutions 7: 247. https://doi. org/10.1007/s41062-022-00847-3. Kopsida, M., I. Brilakis, and P.A. Vela. 2015. “A review of automated construction progress monitoring and inspection methods.” 32nd CIB W78 Conference on Construction IT, Eindhoven, Netherlands. Kuang, K.S.C., S. T. Quek, C. G. Koh, W. J. Cantwell, and P. J. Scully. 2009. “Plastic Optical Fibre Sensors for Structural Health Monitoring: A Review of Recent Progress.” Journal of Sensors 2009: Article ID 312053. https://doi. org/10.1155/2009/312053. Kuo, C., C. Kuo, S. Lin, M.C.E. Manuel, P.T. Lin, Y. Hsieh, and W. Lu. 2016. “Infrastructure Inspection Using an Unmanned Aerial System (UAS) With Metamodeling-Based Image Correction.” Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2B: 42nd Design Automation Conference. Charlotte, NC. August 21–24, 2016. V02BT03A022. ASME. https://doi.org/10.1115/DETC2016-59193. Ma, M., P. Fallavollita, T. Blum, U. Eck, C. Sandor, S. Weidert, J. Waschke, and N. Navab. 2013. “Kinect for Interactive AR Anatomy Learning.” 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). https://doi.org/10.1109/ISMAR.2013.6671803. Mascareñas, D.D.L., J.P. Ballor, O.L. McClain, M.A. Mellor, C.-Y. Shen, B. Bleck, J. Morales, L.-M.R. Yeong, B. Narushof, P. Shelton, E. Martinez, Y. Yang, A. Cattaneo, T.A. Harden, and F. Moreu. 2021. “Augmented reality for next generation infrastructure inspections.” Structural Health Monitoring 20 (4): 1957–79. doi:10.1177/1475921720953846. Masoumi, F., F. Akgül, and A. Mehrabzadeh. 2013. “Condition assessment of reinforced concrete bridges by combined nondestructive test tech- niques.” IACSIT International Journal of Engineering and Technology 5 (6): 708–11. https://doi.org/10.7763/IJET.2013.V5.647. Memarzadeh, M., and M. Pozzi. 2016. “Value of information in sequential decision making: Component inspection, permanent monitoring and system-level scheduling.” Reliability Engineering & System Safety 154:137– 51. https://doi.org/10.1016/j.ress.2016.05.014. Microsoft. 2022. “Azure Kinect DK hardware specifications.” Azure Kinect DK, Azure, Learn, last modified 2 September 2022. docs.microsoft.com/ en-us/azure/kinect-dk/hardware-specification. Nasrollahi, M., N. Bolourian, Z. Zhu, and A. Hammad. 2018. “Designing LiDAR-equipped UAV platform for structural inspection.” Proceedings of the International Symposium on Automation and Robotics in Construc- tion. Newman, T. S., and A. K. Jain. 1995. “A survey of automated visual inspec- tion.” Computer Vision and Image Understanding 61 (2): 231–62. https:// doi.org/10.1006/cviu.1995.1017. Patsadu, O., C. Nukoolkit, and B. Watanapa. 2012. “Human gesture recog- nition using Kinect camera.” 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE), Bangkok, Thailand. https://doi.org/10.1109/JCSSE.2012.6261920. Perry, B. J., Y. Guo, R. Atadero, and J. W. van de Lindt. 2020. “Streamlined bridge inspection system utilizing unmanned aerial vehicles (UAVs) and machine learning.” Measurement 164:108048. https://doi.org/10.1016/j. measurement.2020.108048. Qi, X., D. Lichti, M. El-Badry, J. Chow, and K. Ang. 2014. “Vertical dynamic deflection measurement in concrete beams with the Microsoft Kinect.” Sensors (Basel) 14 (2): 3293–307. https://doi.org/10.3390/s140203293. Ren, Z., J. Yuan, J. Meng, and Z. Zhang. 2013. “Robust part-based hand gesture recognition using kinect sensor.” IEEE Transactions on Multimedia 15 (5): 1110–20. https://doi.org/10.1109/TMM.2013.2246148. Sanford, K.L., P. Herabat, and S. McNeil. 1999. “Bridge management and inspection data: Leveraging the data and identifying the gaps.” Eighth Transportation Research Board Conference on Bridge Management, Denver, CO. Słowik, M. 2019. “The analysis of failure in concrete and reinforced concrete beams with different reinforcement ratio.” Archive of Applied Mechanics 89 (5): 885–95. https://doi.org/10.1007/s00419-018-1476-5. Rens, K. L., T. J. Wipf, and F. W. Klaiber. 1997. “Review of nondestructive evaluation techniques of civil infrastructure.” Journal of Performance of Constructed Facilities 11 (4): 152–60. https://doi.org/10.1061/(ASCE)0887- 3828(1997)11:4(152). Tosti, F., and C. Ferrante. 2020. “Using ground penetrating radar methods to investigate reinforced concrete structures.” Surveys in Geophysics 41 (3): 485–530. https://doi.org/10.1007/s10712-019-09565-5. Vera, L., J. Gimeno, I. Coma, M. Fernández. 2011. “Augmented Mirror: Interactive Augmented Reality System Based on Kinect.” In Human-Com- puter Interaction – INTERACT 2011.eds. Campos, P., N. Graham, J. Jorge, N. Nunes, P. Palanque, and M. Winckler. INTERACT 2011. Lecture Notes in Computer Science, vol 6949. Springer, Berlin, Heidelberg. https://doi. org/10.1007/978-3-642-23768-3_63. ME | RGBDCAMERAS 54 M AT E R I A L S E V A L U AT I O N • J A N U A R Y 2 0 2 3 2301 ME Jan New.indd 54 12/20/22 8:15 AM