Details

PDF BIBTEX RIS

Title

The measurements of surface defect area with an RGB-D camera for a BIM-backed bridge inspection

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

3

Affiliation

Wójcik, Bartosz : Department of Mechanics and Bridges, Faculty of Civil Engendering, Silesian University of Technology, ul. Akademicka 5, 44-100 Gliwice, Poland ; Żarski, Mateusz : Department of Mechanics and Bridges, Faculty of Civil Engendering, Silesian University of Technology, ul. Akademicka 5, 44-100 Gliwice, Poland

Authors

Wójcik, Bartosz ; Żarski, Mateusz

Keywords

3D reconstruction ; RGB-D camera ; depth sensing ; bridge inspection ; as-is BIM

Divisions of PAS

Nauki Techniczne

Coverage

e137123

Bibliography

  1.  J.S. Kong and D.M. Frangopol, “Life-Cycle Reliability-Based Maintenance Cost Optimization of Deteriorating Structures with Emphasis on Bridges”, J. Struct. Eng. 129(6), 818–828 (2003).
  2.  B.M. Phares, G.A. Washer, D.D. Rolander, B.A. Graybeal, and M. Moore, “Routine Highway Bridge Inspection Condition Documentation Accuracy and Reliability”, J. Bridg. Eng. 9(4), 403–413 (2004).
  3.  A. Costin, A. Adibfar, H. Hu, and S.S. Chen, “Building Information Modeling (BIM) for transportation infrastructure – Literature review, applications, challenges, and recommendations”, Autom. Constr. 94, 257–281 (2018).
  4.  “SeeBridge”. [Online]. Available: https://seebridge.net.technion.ac.il/.
  5.  R. Sacks et al., “SeeBridge as next generation bridge inspection: Overview, Information Delivery Manual and Model View Definition”, Autom. Constr. 90, 134–145 (2018).
  6.  P. Hüthwohl, I. Brilakis, A. Borrmann, and R. Sacks, “Integrating RC Bridge Defect Information into BIM Models”, J. Comput. Civ. Eng. 32(3), (2018).
  7.  P. Hüthwohl and I. Brilakis, “Detecting healthy concrete surfaces”, Adv. Eng. Informatics 37, 150–162 (2018).
  8.  P. Hüthwohl, R. Lu, and I. Brilakis, “Multi-classifier for reinforced concrete bridge defects”, Autom. Constr. 105, 102824 (2019).
  9.  R. Lu, I. Brilakis and C. R. Middleton, “Detection of Structural Components in Point Clouds of Existing RC Bridges”, Comput. Civ. Infrastruct. Eng. 34(3), 191–212 (2019).
  10.  R. Lu and I. Brilakis, “Digital twinning of existing reinforced concrete bridges from labelled point clusters”, Autom. Constr. 105, 102837 (2019).
  11.  D. Isailović, V. Stojanovic, M. Trapp, R. Richter, R. Hajdin, and J. Döllner, “Bridge damage: Detection, IFC-based semantic enrichment and visualization”, Autom. Constr. 112, 103088 (2020).
  12.  C.S. Shim, H. Kang, N.S. Dang, and D. Lee, “Development of BIM-based bridge maintenance system for cable-stayed bridges”, Smart Struct. Syst. 20(6), 697–708 (2017).
  13.  N.S. Dang and C.S. Shim, “BIM authoring for an image-based bridge maintenance system of existing cable-supported bridges”, IOP Conf. Ser. Earth Environ. Sci. 143(1), 012032 (2018).
  14.  S. Dang, H. Kang, S. Lon, and S. Changsu, “3D Digital Twin Models for Bridge Maintenance”, 10th Int. Conf. Short Mediu. Span Bridg., 2018, pp. 73.1‒73.9.
  15.  C.S. Shim, N.S. Dang, S. Lon, and C.H. Jeon, “Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model”, Struct. Infrastruct. Eng. 15(10), 1319–1332 (2019).
  16.  Z. Ma and S. Liu, “A review of 3D reconstruction techniques in civil engineering and their applications”, Adv. Eng. Informatics 37, 163–174 (2018).
  17.  Q. Wang and M.K. Kim, “Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018”, Adv. Eng. Informatics 39, 306–319 (2019).
  18.  C. Popescu, B. Täljsten, T. Blanksvärd, and L. Elfgren, “3D reconstruction of existing concrete bridges using optical methods”, Struct. Infrastruct. Eng. 15(7), 912–924 (2019).
  19.  S. Izadi et al., “KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera”, in Proceedings of the 24th annual ACM symposium on User interface software and technology – UIST ’11, 2011, p. 559.
  20.  J. Hoła, J. Bień, Ł. Sadowski, and K. Schabowicz, “Non-destructive and semi-destructive diagnostics of concrete structures in assessment of their durability”, Bull. Polish Acad. Sci. Tech. Sci. 63(1), 87–96 (2015).
  21.  J. Bień, T. Kamiński, and M. Kużawa, “Taxonomy of non-destructive field tests of bridge materials and structures”, Arch. Civ. Mech. Eng. 19(4), 1353–1367 (2019).
  22.  J. Bień, M. Kużawa, and T. Kamiński, “Strategies and tools for the monitoring of concrete bridges”, Struct. Concr. 21(4), 1227–1239 (2020).
  23.  “OpenCV AI Kit”. [Online]. Available: https://www.kickstarter.com/projects/opencv/opencv-ai-kit.
  24.  B. Liu, H. Cai, Z. Ju, and H. Liu, “RGB-D sensing based human action and interaction analysis: A survey”, Pattern Recognit. 94, 1–12 (2019).
  25.  Y.-D. Hong, Y.-J. Kim, and K.-B. Lee, “Smart Pack: Online Autonomous Object-Packing System Using RGB-D Sensor Data”, Sensors 20(16), 4448 (2020).
  26.  M.R. Jahanshahi, F. Jazizadeh, S.F. Masri, and B. Becerik-Gerber, “Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor”, J. Comput. Civ. Eng. 27(6), 743–754 (2013).
  27.  D. Roca, S. Lagüela, L. Díaz-Vilariño, J. Armesto, and P. Arias, “Low-cost aerial unit for outdoor inspection of building façades”, Autom. Constr. 36, 128–135 (2013).
  28.  C. Bellés and F. Pla, “A Kinect-Based System for 3D Reconstruction of Sewer Manholes”, Comput. Civ. Infrastruct. Eng. 30(11), 906–917 (Nov. 2015).
  29.  M. Abdelbarr, Y.L. Chen, M.R. Jahanshahi, S.F. Masri, W.M. Shen, and U.A. Qidwai, “3D dynamic displacement-field measurement for structural health monitoring using inexpensive RGB-D based sensor”, Smart Mater. Struct. 26(12) (2017).
  30.  Z. Xu, S. Li, H. Li, and Q. Li, “Modeling and problem solving of building defects using point clouds and enhanced case-based reasoning”, Automation in Construction 96(February), 40–54 (2018).
  31.  M. Nahangi, T. Czerniawski, C.T. Haas, and S. Walbridge, “Pipe radius estimation using Kinect range cameras”, Autom. Constr. 99 (March 2017), 197–205 (2019).
  32.  G.H. Beckman, D. Polyzois, and Y.J. Cha, “Deep learning-based automatic volumetric damage quantification using depth camera”, Automation in Construction 99(November 2018), 114–124 (2019).
  33.  H. Kim, S. Lee, E. Ahn, M. Shin, and S.-H. Sim, “Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion”, Struct. Heal. Monit., 1–13 (2020).
  34.  Intel, “Intel® RealSenseTM Camera D400 series Product Family Datasheet” (2019).
  35.  C. Rother, V. Kolmogorov, and A. Blake, “‘GrabCut’: interactive foreground extraction using iterated graph cuts”, in ACM SIGGRAPH 2004 Papers on – SIGGRAPH ’04, 2004, p. 309.
  36.  Y. Li, J. Sun, C.K. Tang, and H.Y. Shum, “Lazy snapping”, ACM SIGGRAPH 2004 Pap. SIGGRAPH 2004, 303–308 (2004).
  37.  M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models”, Int. J. Comput. Vis. 1(4), 321–331 (Jan. 1988).
  38.  M.A. Fischler and R.C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”, Commun. ACM 24(6), 381–395 (1981).
  39.  D. Holz and S. Behnke, “Fast Range Image Segmentation and Smoothing Using Approximate Surface Reconstruction and Region Growing”, 2013, 61–73.
  40.  M. Garland and P.S. Heckbert, “Surface simplification using quadric error metrics”, in Proceedings of the 24th annual conference on Computer graphics and interactive techniques – SIGGRAPH ’97, 1997, 209–216.
  41.  Intel, “Intel® RealSenseTM Camera: Depth testing methodology”, 2018.
  42.  B. Wójcik and M. Żarski, “Asesment of state-of-the-art methods for bridge inspection: case study”, Arch. Civ. Eng. 66(4), 343‒362 (2020).
  43.  K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN”, IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386–397 (2017).

Date

24.04.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.137123

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; Early Access; e137123
×