The purpose of this study was to establish a framework capable of integrating traditional non-destructive evaluation (NDE) and emerging automated unmanned aerial vehicle (UAV)-based techniques to provide improved performance assessment of bridges. The framework focuses on addressing the principal challenges associated with studying the service life of bridge structures: (a) the long time scales (which requires accelerated aging), (b) the diverse outputs related to bridge condition (in terms of data collected through UAV, NDE, and visual inspection), and (c) an advanced data interpretation and fusion framework for automated detection and quantification of bridge surface and subsurface defects.

By leveraging the access to the unique dataset generated by the Bridge Evaluation and Accelerated Structural Testing (BEAST) facility, this study aimed to identify the potential synergies among bridge degradation, remaining service life, and the results taken from the multimodal sensing technologies (i.e., UAV and NDE techniques). Data processing frameworks based on deep learning and a systematic UAV data collection strategy were developed to automatically detect the surface defects from HD images and subsurface defects from Infrared thermography (IR) images.

New multi-source NDE data fusion methods based on discrete wavelet transforms and improved Dempster-Shafer evidence combination theory were proposed to provide a more comprehensive concrete bridge deck assessment.