Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1A2C3005687, NRF-2021R1A5A1032433, No. NRF-2020R1A2C3005687, NRF-2021R1A5A1032433, NRF-2022R1C1C1003594).
References
- 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.
- 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.
- 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. https://doi.org/10.3390/s22072762
- 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.
- 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.
- 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.
- Garreta, R. and Moncecchi, G. (2013). Learning Scikit-learn: Machine Learning in Python, Packt Publishing Ltd., Birmingham, U.K.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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. https://doi.org/10.9712/KASS.2021.21.2.99
- 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.
- 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.
- Liaw, A. and Wiener, M. (2002), Classification and Regression by RandomForest. R news, 2(3), 18-22.
- 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.
- Massoud, K. (2002), Principles of Heat Transfer, Wiley-IEEE, Hoboken, New Jersey, U.S.A.
- 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.
- 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.
- 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. https://doi.org/10.3390/rs14092151
- Planck, M. (1900). The Theory of Heat Radiation. Entropie, 144(190), 164.
- 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.
- 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.
- 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.
- 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. https://doi.org/10.1109/TPAMI.2009.187
- 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.
- 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.
- 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.
- 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.
- 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.
- Sokavcic, A. (2010), Application of Infrared Thermography to the Non-Destructive Testing of Concrete Structures, Ph.D. Dissertation, Gradjevinski fakultet, Sveuvcilivste u Zagrebu.
- Speakman, J.R. and Ward, S. (1998), "Infrared thermography: Principles and applications", Zoology, 101, 224-232.
- 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.
- Sun, Q. and Pfahringer, B. (2012). Bagging Ensemble Selection for Regression, In Australasian Joint Conference on Artificial Intelligence. Springer, Berlin, Heidelberg.
- 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.
- Venkanna, B.K. (2010), Fundamentals of Heat and Mass Transfer, PHI Learning Pvt. Ltd., New Delhi, India.