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Infrared thermography for detecting defects in concrete structures

    Gene F. Sirca Jr. Affiliation
    ; Hojjat Adeli Affiliation

Abstract

The traditional methods for inspecting large concrete structures such as dams and cooling towers require erecting large amounts of scaffolding to access the surface of the concrete structure in order to sound the concrete with an impact device or hammer to expose the damaged or defective areas.  Another method for accessing the surface of a large concrete structure is to employ climbing inspections which poses a considerable safety risk. These traditional methods are used to determine defect or damage within a few inches of the surface. In addition to the logistic difficulty of these methods a hammer can cause damage if care is not taken. Further, it can cover only a small area. Infrared Thermography (IRT), also referred to as thermal imaging, utilizes the infrared spectrum to show differences in heat dissipating from a structure using a thermal imaging camera. This paper presents a review of the IRT research for detecting defects in concrete structures. Health monitoring and damage detection of large structures such as bridges and high-rise buildings has been a very active area of research in recent years. The two main approaches explored by researchers are vibration-based health monitoring and camera-based vision technology. IRT remains to be another promising technology for economical health monitoring of structures.

Keyword : concrete structures, defect detection, damage detection, Infrared Thermography, health monitoring of structures

How to Cite
Sirca Jr., G. F., & Adeli, H. (2018). Infrared thermography for detecting defects in concrete structures. Journal of Civil Engineering and Management, 24(7), 508-515. https://doi.org/10.3846/jcem.2018.6186
Published in Issue
Nov 13, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Adeli, H.; Hung, S. L. 1995. Machine learning – Neural networks, genetic algorithms, and fuzzy systems. New York: John Wiley and Sons.

Adeli, H.; Park, H. S. 1998. Neurocomputing for design automation. Boca Raton, Florida: CRC Press. https://doi.org/10.1201/9781315214764

Aggelis, D. G.; Kordatos, E. Z.; Soulioti, D. V.; Matikas, T. E. 2010. Combined use of thermography and ultrasound for the characterization of subsurface cracks in concrete, Construction and Building Materials 24(10): 1888–1897. https://doi.org/10.1016/j.conbuildmat.2010.04.014

Aggelis, D. G.; Kordatos, E. Z.; Strantza, M.; Soulioti, D. V.; Matikas, T. E. 2011. NDT approach for characterization of subsurface cracks in concrete, Construction and Building Materials 25(7): 3089–3097. https://doi.org/10.1016/j.conbuildmat.2010.12.045

Al-Hadhrami, L. M.; Maslehuddin, M.; Shameem, M.; Ali, M. R. 2012. Assessing concrete density using infrared thermographic (IRT) images, Infrared Physics & Technology 55(5): 442–448. https://doi.org/10.1016/j.infrared.2012.04.004

Amezquita-Sanchez, J. P.; Adeli, H. 2016. Signal processing techniques for vibration-based health monitoring of structures, Archives of Computational Methods in Engineering 23(1): 1–15. https://doi.org/10.1007/s11831-014-9135-7

Cha, Y. J.; Buyukozturk, O. 2015. Structural damage detection using modal strain energy and hybrid multi-objective optimization, Computer-Aided Civil and Infrastructure Engineering 30(5): 347–358. https://doi.org/10.1111/mice.12122

Cheng, C.-C.; Cheng, T.-M.; Chiang, C.-H. 2008. Defect detection of concrete structures using both infrared thermography and elastic waves, Automation in Construction 8(1): 87–92. https://doi.org/10.1016/j.autcon.2008.05.004

Dai, H.; Wang, W.; Zhang, H. 2015. A multiwavelet neural network-based response surface method for structural reliability analysis, Computer-Aided Civil and Infrastructure Engineering 30(2): 151–162. https://doi.org/10.1111/mice.12086

Farrag, S.; Yehia, S.; Qaddoumi, N. 2016. Investigation of mix-variation effect on defect-detection ability using infrared thermography as a nondestructive evaluation technique, Journal of Bridge Engineering 21(3). https://doi.org/10.1061/(ASCE)BE.1943-5592.0000779

Guimaraes, M. 2014. Enhanced inspection of hydroelectric concrete structures. Report 3002004459. Electric Power Research Institute, Palo Alto, CA.

Harris, D.; Brooks, C.; Ahlborn, T. 2016. Synthesis of field performance of remote sensing strategies for condition assessment of in-service bridges in Michigan, Journal of Performance of Constructed Facilities 30(5). https://doi.org/10.1061/(ASCE)CF.1943-5509.0000844

Hing, C.; Halabe, U. 2010. Nondestructive testing of GFRP bridge decks using ground penetrating radar and infrared thermography, Journal of Bridge Engineering 15(4): 391–398. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000066

Holgado-Barco, A.; González-Aguilera, D.; Arias-Sanchez, P.; Martinez-Sanchez, J. 2015. Semi-automatic extraction of road horizontal alignment from a mobile LiDAR system, Computer-Aided Civil and Infrastructure Engineering 30(3): 217–228. https://doi.org/10.1111/mice.12087

Karami, K.; Akbarabadi, S. 2016. Developing a smart structure using integrated subspace-based damage detection and semi-active control, Computer-Aided Civil and Infrastructure Engineering 31(11): 887–903. https://doi.org/10.1111/mice.12231

Kee, S.; Oh, T.; Popovics, J.; Arndt, R.; Zhu, J. 2012. Nondestructive bridge deck testing with air-coupled impact-echo and infrared thermography, Journal of Bridge Engineering 17(6): 928–939. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000350

Lehmann, B.; Ghazi, W. K.; Frank, T.; Vera, B. C.; Tanner, C. 2013. Effects of individual climatic parameters on the infrared thermography of buildings, Applied Energy 110(10): 29–43. https://doi.org/10.1016/j.apenergy.2013.03.066

Lei, Y.; Zhou, H.; Lai, Z. L. 2016. A computationally compact algorithm for real-time detection of abrupt structural stiffness degradations, Computer-Aided Civil and Infrastructure Engineering 31(6): 465–480. https://doi.org/10.1111/mice.12217

Maierhofer, C.; Arndt, R.; Rollig, M. 2007. Influence of concrete properties on the detection of voids with impulse-thermography, Infrared Physics & Technology 49(3): 213–217. https://doi.org/10.1016/j.infrared.2006.06.007

Maldague, X. 2001. Theory and practice of infrared technology for nondestructive testing. New York: John Wiley & Sons.

Maser, K.; Roddis, W. 1990. Principles of thermography and radar for bridge deck assessment, Journal of Transportation Engineering 116(5): 583–601. https://doi.org/10.1061/(ASCE)0733-947X(1990)116:5(583)

Matsumoto, M. 2014. Non-destructive bridge deck assessment using image processing and infrared thermography, in Transportation Research Board 93rd Annual Meeting, 2014. Washington: Transportation Research Board.

Naik, T.; Singh, S.; Zachar, J. 1997. Application of infrared thermography technique for evaluation of existing concrete structures, in Seventh International Conference and Exhibition “Structural Faults & Repair – 97”, July 1997, Edinburgh, Scotland.

Oh, T.; Kee, S.; Arndt, R.; Popovics, J.; Zhu, J. 2013. Comparison of NDT methods for assessment of a concrete bridge deck, Journal of Engineering Mechanics 193(3): 305–314. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000441

Park, H. S.; Lee, H. M.; Adeli, H.; Lee, I. 2007. A new approach for health monitoring of structures: Terrestrial laser scanning, Computer-Aided Civil and Infrastructure Engineering 22(1): 19–30. https://doi.org/10.1111/j.1467-8667.2006.00466.x

Park, S. W.; Park, H. S.; Kim, J. H.; Adeli, H. 2015. 3D displacement measurement model for health monitoring of structures using a motion capture system, Measurement 59: 352–362. https://doi.org/10.1016/j.measurement.2014.09.063

Perez-Ramirez, C. A.; Amezquita-Sanchez, J. P.; Adeli, H.; Valtierra-Rodriguez, M.; Camarena-Martinez, D.; Rene Romero-Troncoso, R. J. 2016. New methodology for modal parameters identification of smart civil structures using ambient vibrations and synchrosqueezed wavelet, Engineering Applications of Artificial Intelligence 48: 1–16. https://doi.org/10.1016/j.engappai.2015.10.005

Pla-Rucki, G.; Eberhard, M. 1995. Imaging of reinforced concrete: State-of-the-art review, Journal of Infrastructure Systems 1(2): 134–141. https://doi.org/10.1061/(ASCE)1076-0342(1995)1:2(134)

Qarib, H; Adeli, H. 2014. Recent advances in health monitoring of civil structures, Scientia Iranica – Transaction A: Civil Engineering 21(6): 1733–1742.

Rafiei, M. H.; Khushefati, W. H.; Demirboga, R.; Adeli, H. 2017. Novel approach for concrete mix design using neural dynamics model and the virtual lab concept, ACI Materials Journal 14(1): 117–127.

Renshaw, J.; Guimaraes, M.; Scott, D. 2014. Thermographic inspection of massive structures, AIP Conference Proceedings 1581: 978–984. https://doi.org/10.1063/1.4864927

Riveiro, B.; Lourenço, P. B.; Oliveira, D. V.; González-Jorge, H.; Arias, P. 2016. Automatic morphologic analysis of quasi-periodic masonry walls from LiDAR, Computer-Aided Civil and Infrastructure Engineering 31(4): 305–319. https://doi.org/10.1111/mice.12145

Shan, J.; Shi, W.; Lu, X. 2016. Model reference health monitoring of hysteretic building structure using acceleration measurement with test validation, Computer-Aided Civil and Infrastructure Engineering 31(6): 449–464. https://doi.org/10.1111/mice.12172

Siddique, N.; Adeli, H. 2013. Computational intelligence – Synergies of fuzzy logic, neural networks and evolutionary computing. West Sussex: Wiley. https://doi.org/10.1002/9781118534823

Sirieix, C.; Lataste, J. F.; Breysse, D.; Naar, S.; Derobert, X. 2007. Comparison of nondestructive testing: Infrared thermography, electrical resisitivity and capacity methods for assessing a reinforced concrete structure, Journal of Building Appraisal 3(1): 77–88. https://doi.org/10.1057/palgrave.jba.2950065

Teza, G. 2014. THIMRAN: MATLAB Toolbox for thermal image processing aimed at damage recognition in large bodies, Journal of Computing in Civil Engineering 28(4). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000368

Vaghefi, K.; Ahlborn, T.; Harris, D.; Brooks, C. 2015. Combined imaging technologies for concrete bridge deck condition assessment, Journal of Performance of Construction Facilities 29(4). https://doi.org/10.1061/(ASCE)CF.1943-5509.0000465

Vaghefi, K.; de Melo e Silfa, H.; Harris, D.; Ahlborn, T. 2011. Application of thermal IR imagery for concrete bridge inspection, in Proceedings of the PCI National Bridge Conference, 2011, Salt Lake City, UT.

Washer, G.; Fenwick, R.; Bolleni, N. 2010. Effects of solar loading on infrared imaging of subsurface features in concrete, Journal of Bridge Engineering 15(4): 384–390. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000117

Yehia, S.; Abudayyeh, O.; Nabulsi, S.; Abdelqader, I. 2007. Detection of common defects in concrete bridge decks using nondestructive evaluation techniques, Journal of Bridge Engineering 12(2): 215–225. https://doi.org/10.1061/(ASCE)1084-0702(2007)12:2(215)

Yeum, C. M.; Dyke, S. J. 2015. Vision based automated crack detection for bridge inspection, Computer-Aided Civil and Infrastructure Engineering 30(10): 759–770. https://doi.org/10.1111/mice.12141

Yuen, K. V.; Mu, H. Q. 2015. Real-time system identification: An algorithm for simultaneous model class selection and parametric identification, Computer-Aided Civil and Infrastructure Engineering 30(10): 785–801. https://doi.org/10.1111/mice.12146

Zhao, G.; Chen, J. G. 2013. Infrared thermo-graphic inspection technique for concrete retaining wall, Electronic Journal of Geotechnical Engineering 18(1): 1521–1528.

Zhong, Y.; Xiang, J. 2016. A two-dimensional plum-blossom sensor array-based multiple signal classification method for impact localization in composite structures, Computer-Aided Civil and Infrastructure Engineering 31(8): 633–643. https://doi.org/10.1111/mice.12198