Computerized Data Interpretation for Concrete Assessment with Air-Coupled Impact-Echo: An Online Learning Approach

Developing efficient Artificial Intelligence (AI)-enabled system to substitute human role in 1 non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel 2 impact-echo analysis system using online machine learning, which aims at achieving near-human 3 performance for assessment of concrete structures. Current computerized impact-echo systems 4 commonly employ lab-scale data to validate the models. In practice, however, the echo patterns can 5 be far more complicated due to varying geometric shapes and materials of structures. To deal with 6 a large variety of unseen data, we propose a sequential treatment for echo characterization. More 7 specifically, the proposed system can adaptively update itself to approaching human performance in 8 impact-echo data interpretation. To this end, a two-stage framework has been introduced, including 9 echo feature extraction and the model updating scheme. Various state-of-the-art online learning 10 algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we 11 collected 10940 echo instances from multiple inspection sites with each sample had been annotated 12 by human experts with healthy/defective condition labels. The results demonstrated that the 13 proposed scheme achieved favorable echo pattern classification accuracy with high efficiency and 14 low computation load. 15