Master of Science in Electrical Engineering (MSEE)
Electrical and Computer Engineering
Detection of yield zones using nondestructive testing (NDT) technology for assessing the structural integrity of the existing steel buildings/bridges is extremely important. The average energy over the “effective echoes” (in “good” signal quality) is a robust feature for the yield detection in steel structures. Nevertheless, this average-energy feature extraction requires rigorous manual data-acquisition and human operation. Therefore, in this thesis, we make the first-ever attempt to design a totally-blind and automatic steel-structure yielddetection mechanism, which requires neither the a priori information about the signal nor the human effort in calibration, operation, or data analysis. This new scheme is built upon a robust preprocessor, which involves both blind-signature-signal-extraction and zero-crossingrate thresholding, to identify the starting and terminal time points of each ultrasonic echo. Thus, the new computer-aided system can easily estimate the signal-to-noise ratios and automatically extract the effective echoes to calculate the corresponding average energy. The performance reflected by the receiver-operating characteristic (ROC) curves of the proposed method is very close to that of the conventional human-operating technique. Hence one may save much human effort in the sacrifice of very little detection accuracy by using our proposed new system.
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Zhang, Hongting, "Blind Yield Detection in Steel Structure for Automatic Nondestructive Testing Using Ultrasonic Sensors" (2011). LSU Master's Theses. 4086.