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

Abstract

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