International conference on construction engineering and project management (국제학술발표논문집)

  • 2024.07a
  • /
  • Pages.487-493
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  • 2024
  • /
  • 2508-9048(eISSN)
Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Mobile-based Dimension Measurement for Precast Concrete Panels Using Deep Learning and Image Processing

  • Dinh Quang Duy (Department of Global Smart City, Faculty of Engineering, Sungkyunkwan University) ;
  • Ganesh Kolappan Geetha (Department of Mechanical Engineering, Indian Institute of Technology (IIT Bhilai)) ;
  • Sung-Han Sim (School of Civil, Architectural Engineering and Landscape Architecture, Faculty of Engineering, Sungkyunkwan University)
  • Published : 2024.07.29
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Abstract

Presently, prefabricated concrete panels are extensively employed in diverse construction projects across the globe due to their exceptional quality. To maintain the overall quality of these construction projects, it is crucial to ensure that the dimensions of precast concrete panels align with their designated design specifications. Therefore, it is essential to develop a methodology capable of quickly and accurately measuring the dimensions of precast concrete panels. Currently, there are many advanced technologies used to examine the dimensions of prefabricated concrete panels such as terrestrial laser scanning, which is prone to time consuming and cost inefficiencies. To address these limitations, this study suggests a computer vision-based approach that utilizes April Tag markers and images taken from a mobile phone to measure and evaluate the dimensions and quality of precast concrete panels. The proposed algorithm operates as follows: Initially, the RGB image coordinates are converted to the world coordinate systems using April tag markers. Following, the masks of the precast concrete components are extracted using the state-of-the-art Segment Anything Model (SAM). Finally, an algorithm based on image processing technique is developed to estimate the dimensional properties of precast concrete panels. The effectiveness of the proposed method is validated through preliminary experiments conducted in the field-scale precast slabs, and the result is evaluated by comparing to the manual measurement result.

Keywords

Acknowledgement

This research was conducted with the support of the "National R&D Project for Smart Construction Technology (RS-2020-KA156887)" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.

References

  1. Reichenbach, S., & Kromoser, B. (2021). State of practice of automation in precast concrete production. Journal of Building Engineering, 43, 102527. 
  2. Liu, Z. J., Pyplacz, P., Ermakova, M., & Konev, P. (2020). Sustainable construction as a competitive advantage. Sustainability, 12(15), 5946. 
  3. Wong, R., Hao, J. L., & Zou, P. X. W. (2003, December). The application of precast concrete technology in buildings and civil structures construction: Hong Kong experience. In Proceedings of the Second International Conference on Construction in the 21st Century (CITC-II), Sustainability and Innovation in Management and Technology, Hong Kong, China (pp. 10-12). 
  4. Jaillon, L., Poon, C. S., & Chiang, Y. H. (2009). Quantifying the waste reduction potential of using prefabrication in building construction in Hong Kong. Waste management, 29(1), 309-320. 
  5. Ahmad, S., Soetanto, R., & Goodier, C. (2019). Lean approach in precast concrete component production. Built Environment Project and Asset Management, 9(3), 457-470. 
  6. Ma, Z., Liu, Y., & Li, J. (2023). Review on automated quality inspection of precast concrete components. Automation in Construction, 150,
  7. Long, X., Yu, M., Liao, W., & Jiang, C. (2023). A deep learning-based fatigue crack growth rate measurement method using mobile phones. International Journal of Fatigue, 167, 107327. 
  8. Ma, Z., Liu, Y., & Li, J. (2023). Review on automated quality inspection of precast concrete components. Automation in Construction, 150, 104828.
  9. Wang, Q., Kim, M. K., Cheng, J. C., & Sohn, H. (2016). Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning. Automation in Construction, 68, 170-182. 
  10. Li, F., Thedja, J. P. P., Sim, S. H., Seo, J. O., & Kim, M. K. (2023). Range Image-Aided Edge Line Estimation for Dimensional Inspection of Precast Bridge Slab Using Point Cloud Data. Sustainability, 15(16), 12243. 
  11. Yoon, S., Wang, Q., & Sohn, H. (2018). Optimal placement of precast bridge deck slabs with respect to precast girders using 3D laser scanning. Automation in construction, 86, 81-98. 
  12. Kim, M. K., Sohn, H., & Chang, C. C. (2014). Automated dimensional quality assessment of precast concrete panels using terrestrial laser scanning. Automation in Construction, 45, 163-177. 
  13. Kim, M. K., Cheng, J. C., Sohn, H., & Chang, C. C. (2015). A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning. Automation in Construction, 49, 225-238. 
  14. Kim, M. K., Wang, Q., Park, J. W., Cheng, J. C., Sohn, H., & Chang, C. C. (2016). Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM. Automation in construction, 72, 102-114.
  15. Kim, M. K., Thedja, J. P. P., & Wang, Q. (2020). Automated dimensional quality assessment for formwork and rebar of reinforced concrete components using 3D point cloud data. Automation in Construction, 112, 103077. 
  16. Shu, J., Li, W., Zhang, C., Gao, Y., Xiang, Y., & Ma, L. (2023). Point cloud-based dimensional quality assessment of precast concrete components using deep learning. Journal of Building Engineering, 70, 106391. 
  17. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., ... & Girshick, R. (2023). Segment anything. arXiv preprint arXiv:2304.02643. 
  18. Kayhani, N., Zhao, W., McCabe, B., & Schoellig, A. P. (2022). Tag-based visual-inertial localization of unmanned aerial vehicles in indoor construction environments using an on-manifold extended Kalman filter. Automation in Construction, 135, 104112. 
  19. Westman, E., & Kaess, M. (2018). Underwater AprilTag SLAM and calibration for high precision robot localization. In tech. rep. Pittsburgh, PA: Carnegie Mellon University. 
  20. Dubrofsky, E. (2009). Homography estimation. Diplomova prace. Vancouver: Univerzita Britske Kolumbie, 5.