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.

Abstract
1. 서론
2. 적외선 열화상 데이터 구축 실험
    2.1 실험체 제작
    2.2 적외선 열화상 데이터 구축 실험 및 계측
3. 빅데이터 분석
    3.1 변수 분석
    3.2 상관관계 분석
    3.3 머신 러닝 모델
    3.4 결과 및 고찰
4. 결론
References

• 김지형(고려대학교 건축사회환경공학과) | Kim Jihyung (School of Civil, Environmental, and Architectural Engineering, Korea University)
• 장아름(고려대학교 건축사회환경공학과) | Jang Arum (School of Civil, Environmental, and Architectural Engineering, Korea University)
• 박민재(려대학교 건축사회환경공학부) | Park Min Jae (School of Civil, Environmental, and Architectural Engineering, Korea University)
• 주영규(고려대학교 건축사회환경공학부) | Ju Young K. (School of Civil, Environmental, and Architectural Engineering, Korea University) 교신저자