Journal of Korean Association for Spatial Structures (한국공간구조학회논문집)

Korean Association for Spatial Structures (한국공간구조학회)
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Comparison Analysis of Machine Learning for Concrete Crack Depths Prediction Using Thermal Image and Environmental Parameters

열화상 이미지와 환경변수를 이용한 콘크리트 균열 깊이 예측 머신 러닝 분석

  • Kim, Jihyung (School of Civil, Environmental, and Architectural Engineering, Korea University) ;
  • Jang, Arum (School of Civil, Environmental, and Architectural Engineering, Korea University) ;
  • Park, Min Jae (School of Civil, Environmental, and Architectural Engineering, Korea University) ;
  • Ju, Young K. (School of Civil, Environmental, and Architectural Engineering, Korea University)
  • 김지형 (고려대학교 건축사회환경공학과) ;
  • 장아름 (고려대학교 건축사회환경공학과) ;
  • 박민재 (고려대학교 건축사회환경공학부) ;
  • 주영규 (고려대학교 건축사회환경공학부)
  • Received : 2021.05.17
  • Accepted : 2021.05.31
  • Published : 2021.06.15
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Abstract

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

Keywords

Acknowledgement

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구입니다. (No. NRF-2018R1A4A1026027,2020R1A2C3005687)

References

  1. Omar, T., Nehdi, M. L., & Zayed, T., "Infrared thermography model for automated detection of delamination in RC bridge decks", Construction and Building Materials, Vol.168, pp.313~327, 2018, doi: 10.1016/j.conbuildmat.2018.02.126
  2. Sham, J., Chen, N., & Long, L. "Surface crack detection by flash thermography on concrete surface", Insight-Non-Destructive Testing and Condition Monitoring, Vol.50, No.5, pp.240~243, 2008, doi: 10.1784/insi.2008.50.5.240
  3. Mulaveesala, R., Dua, G., & Arora, V., "Applications of Infrared Thermography for Non-destructive Characterization of Concrete Structures", Advances in Structural Health Monitoring, 2019, doi: 10.5772/intechopen.83636
  4. Omar, T., & Nehdi, M. L., "Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography", Automation in Construction, Vol.83, pp.360~371, 2017, doi: 10.1016/j.autcon.2017.06.024
  5. Tong, X., Guo, J., Ling, Y., & Yin, Z. (2011). A new image-based method for concrete bridge bottom crack detection. Proceedings of the 2011 International Conference on Image Analysis and Signal Processing, China, pp.568~571, doi: 10.1109/IASP18108.2011
  6. Nguyen, H. N., Kam, T. Y., & Cheng, P. Y. (2012). A novel automatic concrete surface crack identification using isotropic undecimated wavelet transform. Proceedings of the 2012 International Symposium on Intelligent Signal Processing and Communications Systems, Taiwan, pp.766~771, doi: 10.1109/ISPACS20401.2012
  7. Zhu, J., & Song, J., "An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges", Applied Sciences, Vol.10, No.3, pp.972, 2020, doi: 10.3390/app10030972
  8. Fisher, R. A., "Statistical Methods for Research Workers", Breakthroughs in Statistics, Springer, pp.66~70, 1992, doi: 10.1007/978-1-4612-4380-9_6
  9. Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep Sparse Rectifier Neural Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, USA, pp.315~323, Retrieved from http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf
  10. Dumitru, C., & Maria, V., "Advantages and Disadvantages of Using Neural Networks for Predictions", Ovidius University Annals, Series Economic Sciences, Vol.13, No.1, pp.444~449, 2013
  11. Lee, J. (2020). Evaluation of concrete crack depth using UAV, thermal images, and artificial intelligence (Master's thesis). Korea University, Seoul, South Korea.
  12. Liaw, A., & Wiener, M., "Classification and Regression by randomForest", R News, Vol.2, No.3, pp.18~22, 2002, Retrieved from https://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf
  13. Zhang, C., & Ma, Y., "Ensemble Machine Learning: Methods and Applications", Springer Science & Business Media, 2012.
  14. Friedman, J. H., "Greedy Function Approximation: A Gradient Boosting Machine", Vol.29, No.5, Annals of Statistics, pp.1189~1232, 2001, doi: 10.1214/aos/1013203451
  15. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J., "Practical Machine Learning Tools and Techniques", 2nd ed., Morgan Kaufmann, pp.578, 2005.
  16. Zhang, D., "A Coefficient of Determination for Generalized Linear Models", The American Statistician, Vol.71, No.4, pp.310~316, 2017, doi: 10.1080/00031305.2016.1256839
  17. De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F., "Mean Absolute Percentage Error for regression models", Neurocomputing, Vol.192, pp.38~48, 2016, doi: 10.1016/j.neucom.2015.12.114
  18. Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P., "Understanding variable importances in forests of randomized trees", Advances in Neural Information Processing Systems, Vol.26, 2013
  19. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Duchesnay, E., "Scikit-learn: Machine learning in Python", Journal of Machine Learning Research, Vol.12, pp.2825~2830, 2011, Retrieved from https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf
  20. Altmann, A., Tolosi, L., Sander, O., & Lengauer, T., "Permutation importance: a corrected feature importance measure", Bioinformatics, Vol.26, No.10, pp.1340~1347, 2010, doi: 10.1093/bioinformatics/btq134
  21. Cutler, D. R., Edwards Jr., T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J., "Random Forests for Classification in Ecology", Ecology, Vol.88, No.11, pp.2783~2792, 2007, doi: 10.1890/07-0539.1
  22. Molnar, C., "Interpretable machine learning", Lulu.com, 2020.