  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.
