Framework for Structural Health Monitoring of Steel Bridges by Computer Vision - Publikacja - MOST Wiedzy


Framework for Structural Health Monitoring of Steel Bridges by Computer Vision


The monitoring of a structural condition of steel bridges is an important issue. Good condition of infrastructure facilities ensures the safety and economic well-being of society. At the same time, due to the continuous development, rising wealth of the society and socio-economic integration of countries, the number of infrastructural objects is growing. Therefore, there is a need to introduce an easy-to-use and relatively low-cost method of bridge diagnostics. We can achieve these benefits by the use of Unmanned Aerial Vehicle-Based Remote Sensing and Digital Image Processing. In our study, we present a state-of-the-art framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability. The presented framework of image processing procedure is suitable for diagnostics of steel truss riveted bridges. In our considerations, we used photographic documentation of the Fitzpatrick Bridge located in Tallassee, Alabama, USA.


  • 9


  • 7

    Web of Science

  • 8


Autorzy (3)

Cytuj jako

Pełna treść

pobierz publikację
pobrano 54 razy
Wersja publikacji
Accepted albo Published Version
Creative Commons: CC-BY otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Publikacja w czasopiśmie
artykuły w czasopismach
Opublikowano w:
SENSORS nr 20, strony 1 - 21,
ISSN: 1424-8220
Rok wydania:
Opis bibliograficzny:
Marchewka A., Ziółkowski P., Aguilar-Vidal V.: Framework for Structural Health Monitoring of Steel Bridges by Computer Vision// SENSORS -Vol. 20,iss. 3 (2020), s.1-21
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s20030700
Bibliografia: test
  1. Li, Q.; Yao, M.; Yao, X.; Xu, B. A real-time 3D scanning system for pavement distortion inspection. Meas. Sci. Technol. 2010, 21, 15702, doi:10.1088/0957-0233/21/1/015702. otwiera się w nowej karcie
  2. Yanaka, M.; Hooman Ghasemi, S.; Nowak, A.S. Reliability-based and life-cycle cost-oriented design recommendations for prestressed concrete bridge girders. Struct. Concr. 2016, 17, 836-847, doi:10.1002/suco.201500197. otwiera się w nowej karcie
  3. Fang, Y.; Sun, L. Developing A Semi-Markov Process Model for Bridge Deterioration Prediction in Shanghai. Sustainability 2019, 11, 5524. otwiera się w nowej karcie
  4. Liu, K.; El-Gohary, N. Learning from Class-Imbalanced Bridge and Weather Data for Supporting Bridge Deterioration Prediction. In Advances in Informatics and Computing in Civil and Construction Engineering; otwiera się w nowej karcie
  5. Springer, Heidelberg, Germany, 2019; pp. 749-756.
  6. Zambon, I.; Vidović, A.; Strauss, A.; Matos, J. Condition Prediction of Existing Concrete Bridges as a Combination of Visual Inspection and Analytical Models of Deterioration. Appl. Sci. 2019, 9, 148. otwiera się w nowej karcie
  7. Ziolkowski, P.; Szulwic, J.; Miskiewicz, M. Deformation analysis of a composite bridge during proof loading using point cloud processing. Sensors 2018, 18, 4332, doi:10.3390/s18124332. otwiera się w nowej karcie
  8. Chroscielewski, J.; Miskiewicz, M.; Pyrzowski, L.; Rucka, M.; Sobczyk, B.; Wilde, K.; Meronk, B. Dynamic Tests and Technical Monitoring of a Novel Sandwich Footbridge. In Dynamics of Civil Structures, Volume 2; otwiera się w nowej karcie
  9. Springer, Heidelberg, Germany, 2020; pp. 55-60. otwiera się w nowej karcie
  10. Miskiewicz, M.; Pyrzowski, L.; Wilde, M. In Dynamics of Civil Structures, Volume 2; Springer, Heidelberg, Germany, 2020; pp. 211-217. otwiera się w nowej karcie
  11. Chróścielewski, J.; Miśkiewicz, M.; Pyrzowski, Ł.; Rucka, M.; Sobczyk, B.; Wilde, K. Modal properties identification of a novel sandwich footbridge -Comparison of measured dynamic response and FEA. Compos. Part. B Eng. 2018, 151, 245-255, doi:10.1016/j.compositesb.2018.06.016. otwiera się w nowej karcie
  12. Rezaei, H.; Moayyedi, S.A.; Jankowski, R. Probabilistic seismic assessment of RC box-girder highway bridges with unequal-height piers subjected to earthquake-induced pounding. Bull. Earthq. Eng. 2019, 1-32, doi:10.1007/s10518-019-00764-4. otwiera się w nowej karcie
  13. Feng, D.; Feng, M.Q.; Ozer, E.; Fukuda, Y. A vision-based sensor for noncontact structural displacement measurement. Sensors 2015, 15, 16557-16575. otwiera się w nowej karcie
  14. Ye, X.W.; Yi, T.H.; Dong, C.Z.; Liu, T. Vision-based structural displacement measurement: System performance evaluation and influence factor analysis. Meas. J. Int. Meas. Confed. 2016, 88, 372-384, doi:10.1016/j.measurement.2016.01.024. otwiera się w nowej karcie
  15. Vicente, A.M.; Gonzalez, C.D.; Minguez, J.; Schumacher, T. A Novel Laser and Video-Based Displacement Transducer to Monitor Bridge Deflections. Sensors 2018, 18, 970. otwiera się w nowej karcie
  16. Schumacher, T.; Shariati, A. Monitoring of structures and mechanical systems using virtual visual sensors for video analysis: Fundamental concept and proof of feasibility. Sensors 2013, 13, 16551-16564. otwiera się w nowej karcie
  17. Zhao, X.; Liu, H.; Yu, Y.; Xu, X.; Hu, W.; Li, M.; Ou, J. Bridge displacement monitoring method based on laser projection-sensing technology. Sensors 2015, 15, 8444-8643, doi:10.3390/s150408444. otwiera się w nowej karcie
  18. Abdel-Qader, I.; Yohali, S.; Abudayyeh, O.; Yehia, S. Segmentation of thermal images for non-destructive evaluation of bridge decks. NDT E Int. 2008, 41, 395-405, doi:10.1016/j.ndteint.2007.12.003. otwiera się w nowej karcie
  19. Sharifzadeh, M.; Alirezaee, S.; Amirfattahi, R.; Sadri, S. Detection of steel defect using the image processing algorithms. In Proceedings of the 12th IEEE International Multitopic Conference (INMIC 2008), Karachi, Pakistan, 23-24 December 2008; pp. 125-127. otwiera się w nowej karcie
  20. Feng, D.; Feng, M.Q. Experimental validation of cost-effective vision-based structural health monitoring. Mech. Syst. Signal. Process. 2017, 88, 199-211, doi:10.1016/j.ymssp.2016.11.021. otwiera się w nowej karcie
  21. Lee, J.; Lee, K.C.; Cho, S.; Sim, S.H. Computer vision-based structural displacement measurement robust to light-induced image degradation for in-service bridges. Sensors 2017, 17, 2317. otwiera się w nowej karcie
  22. da Silva, L.S.; Simões, R.; Gervásio, H. Design of Steel Structures. In Design of Steel Structures; CRC Press: New York, NY, United States 2014; pp. 1-438 ISBN 9783433604229. otwiera się w nowej karcie
  23. MacGinley, T.J. In Structural steel design; Pearson: Boston, England, 2018. otwiera się w nowej karcie
  24. Yan, W.; Xie, Z.; Yu, C.; Song, L.; He, H. Experimental investigation and design method for the shear strength of self-piercing rivet connections in thin-walled steel structures. J. Constr. Steel Res. 2017, 133, 231-240, doi:10.1016/j.jcsr.2017.02.022. otwiera się w nowej karcie
  25. Trahair, N.S.; Bradford, M.A. In The behaviour and design of steel structures to as 4100: Australian; CRC Press: New York, USA, 2017; ISBN 9781482271997. otwiera się w nowej karcie
  26. Zmetra, K.M.; McMullen, K.F.; Zaghi, A.E.; Wille, K. Experimental study of UHPC repair for corrosion- damaged steel girder ends. J. Bridg. Eng. 2017, 22, 4017037, doi:10.1061/(ASCE)BE.1943-5592.0001067. otwiera się w nowej karcie
  27. Benjamin A. Graybeal Emerging UHPC-Based Bridge Construction and Preservation Solutions. In Proceeding of the AFGC-ACI-fib-RILEM Int. Symp. Ultra-High. Perform. Fibre-Reinforced Concr. (UHPFRC 2017), Montpellier, France, 2-4 October 2017, 965-974.
  28. Darby, P.; Gopu, V. Bridge Inspecting with Unmanned Aerial Vehicles R & D. Available online: (accessed on 22 January 2020).
  29. González-Jorge, H.; Martínez-Sánchez, J.; Bueno, M.; Arias, and P. Unmanned Aerial Systems for Civil Applications: A Review. Drones 2017, 1, 2, doi:10.3390/drones1010002. otwiera się w nowej karcie
  30. Khan, F.; Ellenberg, A.; Mazzotti, M.; Kontsos, A.; Moon, F.; Pradhan, A.; Bartoli, I. Investigation on bridge assessment using unmanned aerial systems. In Proceedings of the 2015 Structures Congress Structures, Portland Oregon, United States, 23-25 April 2015; pp. 404-413. otwiera się w nowej karcie
  31. Bajwa, A.S. Emerging technologies & their adoption across US DOT's: A pursuit to optimize performance in highway infrastructure project delivery. Available online: https:// (accessed on 22 January 2020).
  32. Hiasa, S.; Karaaslan, E.; Shattenkirk, W.; Mildner, C.; Catbas, F.N. Bridge Inspection and Condition Assessment Using Image-Based Technologies with UAVs. In Structures Congress 2018: Bridges, Transportation Structures, and Nonbuilding Structures -Selected Papers from the Structures Congress 2018; otwiera się w nowej karcie
  33. Sanchez-Cuevas, P.J.; Ramon-Soria, P.; Arrue, B.; Ollero, A.; Heredia, G. Robotic system for inspection by contact of bridge beams using UAVs. Sensors 2019, 19, 305, doi:10.3390/s19020305. otwiera się w nowej karcie
  34. González-deSantos, L.M.; Martínez-Sánchez, J.; González-Jorge, H.; Ribeiro, M.; de Sousa, J.B.; Arias, P. Payload for Contact Inspection Tasks with UAV Systems. Sensors 2019, 19, 3752. otwiera się w nowej karcie
  35. Burdziakowski, P. A Modern Approach to an Unmanned Vehicle Navigation. In Proceeding of the 16th International Multidiscip. Scientific GeoConference (SGEM2016), Albena, Bulgaria, 30 June-6 July 2016, pp. 747-758, doi:10.5593/sgem2016/b22/s10.096. otwiera się w nowej karcie
  36. Hu, C.; Xia, Y.; Zhang, J. Adaptive operator quantum-behaved pigeon-inspired optimization algorithm with application to UAV path planning. Algorithms 2019, 12, 3, doi:10.3390/a12010003. otwiera się w nowej karcie
  37. Avellar, G.S.C.; Pereira, G.A.S.; Pimenta, L.C.A.; Iscold, P. Multi-UAV routing for area coverage and remote sensing with minimum time. Sensors 2015, 15, 27783-27803, doi:10.3390/s151127783. otwiera się w nowej karcie
  38. Tisdale, J.; Kim, Z.; Hedrick, J. Autonomous UAV path planning and estimation. IEEE Robot. Autom. Mag. 2009, 16, 35-42, doi:10.1109/mra.2009.932529. otwiera się w nowej karcie
  39. Jung, S. Development of Path Planning Tool for Unmanned System Considering Energy Consumption. Appl. Sci. 2019, 9, 3341. otwiera się w nowej karcie
  40. Sankarasrinivasan, S.; Balasubramanian, E.; Karthik, K.; Chandrasekar, U.; Gupta, R. Health Monitoring of Civil Structures with Integrated UAV and Image Processing System. Procedia Comput. Sci. 2015, 54, 508-515, doi:10.1016/j.procs.2015.06.058. otwiera się w nowej karcie
  41. Sieberth, T.; Wackrow, R.; Chandler, J.H. Automatic detection of blurred images in UAV image sets. ISPRS J. Photogramm. Remote Sens. 2016, 122, 1-16. otwiera się w nowej karcie
  42. Surový, P.; Yoshimoto, A.; Panagiotidis, D. Accuracy of reconstruction of the tree stem surface using terrestrial close-range photogrammetry. Remote Sens. 2016, 8, 123, doi:10.3390/rs8020123. otwiera się w nowej karcie
  43. Kahaki, S.M.M.; Nordin, M.D.J.; Ashtari, A.H. Incident detection algorithm based on radon transform using high-resolution remote sensing imagery. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics (ICEEI 2011); otwiera się w nowej karcie
  44. Bandung, Indonesia, 17-19 July 2011; pp. 1-5.
  45. Nayak, N.; Nidhi Hegde, P.N.; Anusha; Nayak, P.; Venugopala, P.S.; Kumaki, T. Morphological pattern spectrum based image manipulation detection. In Proceedings of the 7th IEEE International Advanced Computing Conference (IACC 2017); otwiera się w nowej karcie
  46. Hyderabad, India, 5-7 January 2017; pp. 596-599. otwiera się w nowej karcie
  47. Plaza, J.; Plaza, A.J.; Barra, C. Multi-channel morphological profiles for classification of hyperspectral images using support vector machines. Sensors 2009, 9, 196-218, doi:10.3390/s90100196. otwiera się w nowej karcie
  48. Sabzi, S.; Abbaspour-Gilandeh, Y.; García-Mateos, G.; Ruiz-Canales, A.; Molina-Martínez, J.M. Segmentation of apples in aerial images under sixteen different lighting conditions using color and texture for optimal irrigation. Water 2018, 10, 1634, doi:10.3390/w10111634. otwiera się w nowej karcie
  49. Pujol, F.A.; Pujol, M.; Jimeno-Morenilla, A.; Pujol, M.J. Face detection based on skin color segmentation using fuzzy entropy. Entropy 2017, 19, 26, doi:10.3390/e19010026. otwiera się w nowej karcie
  50. Taqa, A.Y.; Jalab, H.A. Increasing the reliability of skin detectors. Sci. Res. Essays 2010, 5, 2480-2490. otwiera się w nowej karcie
  51. Atherton, T.J.; Kerbyson, D.J. Size invariant circle detection. Image Vis. Comput. 1999, 17, 795-803. otwiera się w nowej karcie
  52. Yuen, H.; Princen, J.; Illingworth, J.; Kittler, J. Comparative study of Hough Transform methods for circle finding. Image Vis. Comput. 1990, 8, 71-77, doi:10.1016/0262-8856(90)90059-E. otwiera się w nowej karcie
  53. Duarte, A.; Carrão, L.; Espanha, M.; Viana, T.; Freitas, D.; Bártolo, P.; Faria, P.; Almeida, H.A. Segmentation Algorithms for Thermal Images. Procedia Technol. 2014, 16, 1560-1569, doi: otwiera się w nowej karcie
  54. Borza, D.; Darabant, A.S.; Danescu, R. Eyeglasses lens contour extraction from facial images using an efficient shape description. Sensors 2013, 13, 13638-13658. otwiera się w nowej karcie
  55. Gamarra Acosta, M.R.; Vélez Díaz, J.C.; Schettini Castro, N. An innovative image-processing model for rust detection using Perlin Noise to simulate oxide textures. Corros. Sci. 2014, 88, 141-151, doi:10.1016/j.corsci.2014.07.027. otwiera się w nowej karcie
Politechnika Gdańska

wyświetlono 44 razy

Publikacje, które mogą cię zainteresować

Meta Tagi