Assessment of concrete macrocrack depth using infrared thermography |
Bae, Jaehoon
(Department of Architectural Design, College of Engineering Science, Chonnam National University)
Jang, Arum (School of Civil, Environmental, and Architectural Engineering, Korea University) Park, Min Jae (School of Civil, Environmental, and Architectural Engineering, Korea University) Lee, Jonghoon (School of Civil, Environmental, and Architectural Engineering, Korea University) Ju, Young K. (School of Civil, Environmental, and Architectural Engineering, Korea University) |
1 | Sokavcic, A. (2010), Application of Infrared Thermography to the Non-Destructive Testing of Concrete Structures, Ph.D. Dissertation, Gradjevinski fakultet, Sveuvcilivste u Zagrebu. |
2 | Speakman, J.R. and Ward, S. (1998), "Infrared thermography: Principles and applications", Zoology, 101, 224-232. |
3 | Su, T.C. (2020), "Assessment of cracking widths in a concrete wall based on tir radiances of cracking", Sensors, 20(17), 4980. https://doi.org/10.3390/s20174980. DOI |
4 | Sun, Q. and Pfahringer, B. (2012). Bagging Ensemble Selection for Regression, In Australasian Joint Conference on Artificial Intelligence. Springer, Berlin, Heidelberg. |
5 | Sutton, C.D. (2005), "Classification and regression trees, bagging, and boosting", Handbook Statistics, 24, 303-329. https://doi.org/10.1016/S0169-7161(04)24011-1. DOI |
6 | Venkanna, B.K. (2010), Fundamentals of Heat and Mass Transfer, PHI Learning Pvt. Ltd., New Delhi, India. |
7 | Hao, J. and Ho, T.K. (2019), "Machine learning made easy: a review of scikit-learn package in python programming language", J. Educat. Behavioral Statistics, 44(3), 348-361. https://doi.org/10.3102/1076998619832248. DOI |
8 | Rafiei, M.H. and Adeli, H. (2018), "Novel machine-learning model for estimating construction costs considering economic variables and indexes", J. Construct. Eng. Manage., 144(12), 04018106. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001570. DOI |
9 | Pozzer, S., Dalla Rosa, F., Pravia, Z.M.C., Rezazadeh Azar, E. and Maldague, X. (2021), "Long-term numerical analysis of subsurface delamination detection in concrete slabs via infrared thermography", Appl. Sci., 11(10), 4323. https://doi.org/10.3390/app11104323. DOI |
10 | Carugo, O. (2007), "Statistical validation of the root-mean-squaredistance, a measure of protein structural proximity. Protein Engineering", Des. Selection, 20(1), 33-37. https://doi.org/10.1093/protein/gzl051. DOI |
11 | Kim, J., Jang, A., Park, M.J. and Ju, Y.K. (2021) "Comparison analysis of machine learning for concrete crack depths prediction using thermal images and environmental parameters", J. Korean. Assoc. Spat. Struct, 21, 99-110. DOI |
12 | Garreta, R. and Moncecchi, G. (2013). Learning Scikit-learn: Machine Learning in Python, Packt Publishing Ltd., Birmingham, U.K. |
13 | Liaw, A. and Wiener, M. (2002), Classification and Regression by RandomForest. R news, 2(3), 18-22. |
14 | Montgomery, D.C., Peck, E.A. and Vining, G.G. (2021), Introduction to Linear Regression Analysis, John Wiley & Sons, Hoboken, New Jersey, U.S.A. |
15 | Park, M.J., Kim, J., Jeong, S., Jang, A., Bae, J. and Ju, Y.K. (2022), "Machine learning-based concrete crack depth prediction using thermal images taken under daylight conditions", Remote Sensing, 14(9), 2151. DOI |
16 | Planck, M. (1900). The Theory of Heat Radiation. Entropie, 144(190), 164. |
17 | Sham, F.C., Chen, N. and Long, L. (2008), "Surface crack detection by flash thermography on concrete surface", InsightNon-Destructive Testing Condition Monit., 50(5), 240-243. https://doi.org/10.1784/insi.2008.50.5.240. DOI |
18 | Raja, B.N.K., Miramini, S., Duffield, C., Sofi, M., Mendis, P. and Zhang, L. (2020), "The influence of ambient environmental conditions in detecting bridge concrete deck delamination using infrared thermography (IRT)", Struct. Control Heal. Monit., 27(4), e2506. https://doi.org/10.1002/stc.2506. DOI |
19 | Rodriguez, J.D., Perez, A. and Lozano, J.A. (2009), "Sensitivity analysis of k-fold cross validation in prediction error estimation", IEEE. T. Pattern. Anal, 32, 569-575. DOI |
20 | Sankarasrinivasan, S., Balasubramanian, E., Karthik, K., Chandrasekar, U. and Gupta, R. (2015), "Health monitoring of civil structures with integrated UAV and image processing system", Procedia Computer Science, 54, 508-515. https://doi.org/10.1016/j.procs.2015.06.058. DOI |
21 | Altman, N. and Krzywinski, M. (2017), "Ensemble methods: Bagging and random forests", Nature Methods, 14(10), 933-935. https://doi.org/10.1038/nmeth.4438. DOI |
22 | Shan, B., Zheng, S. and Ou, J. (2016), "A stereovision-based crack width detection approach for concrete surface assessment", KSCE J. Civil Eng., 20(2), 803-812. https://doi.org/10.1007/s12205-015-0461-6. DOI |
23 | Shazali, A.S.A. and Tahar, K.N. (2019), "Virtual 3D model of Canseleri building via close-range photogrammetry implementation", Int. J. Build. Pathology Adaptation. 38(1), 217-227. https://doi.org/10.1108/IJBPA-02-2018-0016. DOI |
24 | Slonski, M. (2019), "A comparison of deep convolutional neural networks for image-based detection of concrete surface cracks", Comput. Assisted Meth. Eng. Sci., 26(2), 105-112. http://dx.doi.org/10.24423/cames.267. DOI |
25 | Muller, A.C. and Guido, S. (2016), Introduction to Machine Learning with Python: A Guide for Data Scientists, O'Reilly Media, Inc, Sebastopol, California, U.S.A. |
26 | Al-Habaibeh, A., Jalil, L., Lotfi, A. and Shakmak, B. (2019), "Novel approach for the evaluation of the dynamic thermal behaviour of a building by continuous monitoring using autonomous infrared thermography", The International Conference on Energy and Sustainable Futures (ICESF), Nottingham, UK, September. |
27 | Bae, J., Lee J., Jang, A., Ju, Y.K. and Park, M.J. (2022), "SMART SKY EYE system for preliminary structural safety assessment of buildings using unmanned aerial vehicle", Sensors, 22(7), 2762. DOI |
28 | Chen, C., He, W., Zhou, H., Xue, Y. and Zhu, M. (2020), "A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin, northwestern China", Scientific Reports, 10(1), 1-13. https://doi.org/10.1038/s41598-020-60698-9. DOI |
29 | Geron, A. (2019), Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media, Sebastopol, California, U.S.A. |
30 | Gallagher, C.V., Bruton, K., Leahy, K. and O'Sullivan, D.T. (2018), "The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings", Energy Build., 158, 647-655. https://doi.org/10.1016/j.enbuild.2017.10.041. DOI |
31 | Gromping, U. (2009), "Variable importance assessment in regression: linear regression versus random forest", Amer. Statistician, 63(4), 308-319. https://doi.org/10.1198/tast.2009.08199. DOI |
32 | In, C.W., Schempp, F., Kim, J.Y. and Jacobs, L.J. (2015), "A fully non-contact, air-coupled ultrasonic measurement of surface breaking cracks in concrete", J. Nondestructive Evaluation, 34(1), 272. https://doi.org/10.1007/s10921-014-0272-6. DOI |
33 | Jahanshahi, M.R. and Masri, S.F. (2013), "A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation", Smart Mater. Struct., 22(3), 035019. https://doi.org/10.1088/0964-1726/22/3/035019. DOI |
34 | Li, Z.W., Liu, X.Z., Lu, H.Y., He, Y.L. and Zhou, Y.L. (2020), "Surface crack detection in precasted slab track in high-speed rail via infrared thermography", Materials, 13(21), 4837. https://doi.org/10.3390/ma13214837. DOI |
35 | Massoud, K. (2002), Principles of Heat Transfer, Wiley-IEEE, Hoboken, New Jersey, U.S.A. |
36 | Li, Z., Wang, Y., Yu, Y., Fan, K., Xing, L. and Peng, H. (2019), "Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters," Medical Physics, 46(12), 5748-5757. https://doi.org/10.1002/mp.13827. DOI |
37 | Liu, Y.F., Nie, X., Fan, J.S. and Liu, X. G. (2020), "Image-based crack assessment of bridge piers using unmanned aerial vehicles and three-dimensional scene reconstruction", Comput. Aided Civil Infrastruct. Eng., 35(5), 511-529. https://doi.org/10.1111/mice.12501. DOI |