Paper
17 May 2005 Wavelet coefficient analysis for the quantitative determination of damage in tendons and cables
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Abstract
Wires, bars, multi-wire strands made of steel or composite materials are widely used in civil infrastructures as tensioning members in cable-stayed bridges, suspension bridges and prestressed concrete. The health monitoring of these components is a long-standing challenge in the NDE community. In the last few years, the authors have been conducting a study on the application of ultrasonic guided waves for the structural health monitoring of bars and multi-wire strands. This paper presents an application of a signal processing technique based on the Discrete Wavelet Transform (DWT) for the detection and the quantification of damage (in the form of small notches) in loaded seven-wire steel strands. The DWT is applied to ultrasonic signals generated and detected via magnetostrictive transducers. The detection and the quantification of damage in the strands are accomplished by constructing and computing a damage index based on the variance and the root mean square of the wavelet coefficient vector of the ultrasonic damage signatures. It is shown that the logarithmic value of the damage index is linearly dependent on the damage size. In the last portion of the paper an eight-dimensional damage index is constructed and it is fed to an artificial neural network that classifies the size and the location of the notch.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Piervincenzo Rizzo and Francesco Lanza di Scalea "Wavelet coefficient analysis for the quantitative determination of damage in tendons and cables", Proc. SPIE 5765, Smart Structures and Materials 2005: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, (17 May 2005); https://doi.org/10.1117/12.598304
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Cited by 1 scholarly publication.
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KEYWORDS
Wavelets

Discrete wavelet transforms

Feature extraction

Ultrasonics

Signal detection

Defect detection

Receivers

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