North American civil infrastructures are aging. According to recent (2016) Canadian infrastructure report card, 33% of the Canadian municipal infrastructures are either in fair or below fair condition. The current deficit of replacing fair and poor municipal bridges (covers 26% of bridges) is 13 billion dollars. According to the latest report (2017) by American Society of Civil Engineers, the entire American infrastructure have been given a D+ condition rating. This includes some of the structural elements of infrastructures that pose a significant risk and there is an urgent need for frequent and effective inspection to ensure the safety of people. Visual inspection is a commonly used technique to detect and identify surface defects in bridge structures as it has been considered the most feasible method for decades. However, this currently used methodology is inadequate and unreliable as it is highly dependent on subjective human judgment. This labor-intensive approach for inspection requires huge investment in terms of an arrangement of temporary scaffoldings/permanent platforms, ladders, snooper trucks, and sometimes helicopters. To address these issues associated with visual inspection, the completed research suggests three innovative methods; 1) Combined use of Fuzzy logic and Image Processing Algorithm to quantify surface defects, 2) Unmanned Aerial Vehicle (UAV)-assisted American Association of State Highway and Transportation Officials (AASHTO) guideline-based damage assessment technique, and 3) Patent-pending multi-device robotics-based sensor data acquisition system for mapping and assessing defects in civil structures. To detect and quantify subsurface defects such as voids and delamination using a UAV system, another patent-pending UAV-based acoustic method is developed. It is a novel inspection apparatus that comprises of an acoustic signal generator coupled to a UAV. The acoustic signal generator includes a hammer to produce an acoustic signal in a structure using a UAV. An outcome of this innovative research is the development of a model to refine multiple commercially available NDT techniques’ data to detect and quantify subsurface defects. To achieve this, a total of nine 1800 mm × 460 mm reinforced concrete slabs with varying thicknesses of 100 mm, 150 mm and 200 mm are prepared. These slabs are designed to have artificially simulated defects like voids, debonding, honeycombing, and corrosion. To determine the performance of five NDT techniques, more than 300 data points are considered for each test. The experimental research shows that utilizing multiple techniques on a single structure to evaluate the defects, significantly lowers error and increases accuracy compared to that from a standalone test. To visualize the NDT data, two-dimensional NDT data maps are developed. This work presents an innovative method to interpret NDT data correctly as it compares the individual data points of slabs with no defects to slabs with simulated damage. For the refinement of NDT data, significance factor and logical sequential determination factor are proposed. / Graduate / 2020-09-06