Date of Award
Doctor of Philosophy
Electrical and Computer Engineering
Dr. Ikhlas M. Abdel-Qader
Dr. Osama Abudayyeh
Dr. Massood Atashbar
Bridge decks deteriorate over time as a result of deicing salts, freezing-andthawing, and heavy use, resulting in internal defects. According to a 2006 study by the American Society of Civil Engineers, 29% of bridges in the United States are considered structurally deficient or functionally obsolete. Ground penetrating radar (GPR) is a promising non-destructive evaluation technique for assessing subsurface conditions of bridge decks. However, the analysis of GPR scans is typically done manually, where the accuracy of the detection process depends on the technician's trained eye. In this work, a framework is developed to automate the detection, localization, and characterization of subsurface defects inside bridge decks. This framework is composed of a fractal-based feature extraction algorithm to detect defective regions, a deconvolution algorithm using banded-ICA to reduce overlapping between reflections and to estimate the depth of defects, and a classification algorithm using principal component analysis to identify main features in defective regions. This framework is implemented and simulated using MATLAB and GPR real scans of simulated concrete bridge decks.
This framework, as demonstrated by the experimental results, has the following contributions to the current body of knowledge in ground penetrating radar detection and analysis techniques, and in concrete bridge deck condition assessment: 1) developed a framework that integrated detection, localization, and classification of subsurface defects inside concrete bridge decks, 2) presented a comparison between the most common fractal methods to determine the most suitable one for bridge deck condition assessment, 3) introduced a fractal-based feature extraction algorithm that is capable of detecting and horizontally labeling defective regions using only the underlying GPR B-scan without the need for a training dataset, 3) developed a deconvolution algorithms using EFICA to detect embedded defects in bridge decks, 4) introduced an automated identification methodology of defective regions which can be integrated into a CAD system that allows for better visual assessment by the maintenance engineer and has the potential to eliminate human interpretation errors and reduce condition assessment time and cost, and 6) presented an investigation and a successful attempt to classify some of the common defects in bridge decks.
Abu-Amara, Fadi, "An Automated Framework for Defect Detection in Concrete Bridge Decks Using Fractals and Independent Component Analysis" (2010). Dissertations. 491.