In this study, acoustic emission (AE) tests were conducted to detect and locate wire fracture in strands that are widely used in cable-stayed and suspension bridges. To effectively separate fracture signals from unwanted noises, distinct features of fracture, fracture-induced echo, and artificial tapping signals as well as their dependence on loading levels are characterized with short-time Fourier transform. To associate fracture scenarios with their acoustic features, two 20-foot-long (~6.1 m) 270 ksi (~1,862 MPa) steel strands of seven wires were tested with one wire notched off at center and support, respectively, up to 90% of its cross section area by 10% increment. Up to 80% reduction in cross section area of the notched wire, each strand was loaded to 20 kips (~89 kN) corresponding to 35% of the minimum breaking strength and the acquired AE parameters such as hits, energy, and counts were found to change little. With a reduction of 90% of the section area of one wire, both strands were found to be fractured under approximately 16.5 kips (~73.4 kN). The hits, energy, and counts of AE signals were all demonstrated to suddenly change with the fracture of the notched wire. However, only the counts of AE signals distributed over the length of the strands allow the localization of fracture point. The frequency band of fracture signals is significantly broader than that of either fracture-induced echo or artificial tapping noise. The time duration of artificial tapping noises is substantially longer than that of either fracture or fracture-induced echo. These distinct characteristics can be used to effectively separate fracture signals from noises for wire fracture detection and localization in practice.
Hongya Qu, Tiantian Li, and Genda Chen, "Bridge cable fracture detection with acoustic emission test (Conference Presentation)," Proc. SPIE 10169, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017, 1016910 (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 28, 2017; Published: 11 May 2017); https://doi.org/10.1117/12.2258397.5427848139001.
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