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Doctoral Dissertation Announcement
Candidate: Fadi Fawzi Abu-Amara
Doctor of Philosophy
Department: Electrical and Computer Engineering
Title: An Automated Framework for Defect Detection in Concrete Bridge Decks Using Fractals and Independent Component Analysis
Dr. Ikhlas Abdel-Qader, Chair
Dr. Osama Abudayyeh
Dr. Massood Atashbar
Date: Monday, June 14, 2010 10:00 a.m. - Noon
College of Engineering and Applied Sciences, D-212
Bridge decks deteriorate over time as a result of deicing salts, water penetration, freezing-and-thawing, 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.
Experimental results using GPR scans on simulated bridge decks indicate that the fractal-based feature extraction algorithm has 89.74% accuracy of detecting defects and localizing them horizontally. Results also indicate that the deconvolution algorithm estimates the depth of defects with significant accuracy. Finally, the algorithm has 90.91% classification accuracy for delamination and air-void defects.
This framework detects, localizes, and classifies subsurface defects inside simulated concrete bridge decks using only the underlying GPR B-scan without the need for a training dataset. The framework is completely automated, eliminating human interpretation errors and reducing condition assessment time and cost.