Detecting Concrete Abnormality Using Time-series Thermal Imaging and Supervised Learning

Nondestructive detecting defects (NDD) in concrete structures have been explored for decades. Although limited successes were reported, major limitations still exist. The major limitations are the high noises to signal ratio created from the environmental factors, such as cloud, shadow, water, surface texture etc. and the decision making still relies on the engineering judgment of interpretation of image content. Time-series approach, such as principle component thermography approach has been experimented with some improved results. Recent progress in image processing using machine learning approach made it possible for detecting defects thermal features in more quantitative ways. In this paper, we provide a procedure to represent the thermal feature in the time domain by principal component analysis and regress the prediction of detection by two schemes of supervised learning models. Three independent experiments were conducted in a similar laboratory setup but varied in conditions to illustrate the performance and generalization of models. Results showed the effectiveness for the detection purpose with appropriate tuning for parameters. Future studies will focus on implementing more sophisticated structured models to handle more realistic cases under natural conditions.