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http://dx.doi.org/10.9712/KASS.2021.21.2.99

Comparison Analysis of Machine Learning for Concrete Crack Depths Prediction Using Thermal Image and Environmental Parameters  

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)
Publication Information
Journal of Korean Association for Spatial Structures / v.21, no.2, 2021 , pp. 99-110 More about this Journal
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
Concrete crack; Crack depth prediction; Thermography; Machine learning; AdaBoost;
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