Acoustic Emissions Sensor and Fuzzy C-mean Clustering Based Break Detection in Post-Tensioning Tendons

Mahmoudkhani, Sadegh; Algohi, Basheer; Zhao, Jun‐Hui; Ling, Henry; Mufti, Aftab; Thomson, D. J.

doi:10.1109/sensors43011.2019.8956661

Cited by 1 publication

(1 citation statement)

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“…where W was a weight factor that simulated the effect of attenuation on the signal's amplitude, and attenuation was assumed 2 dB m −1 [41,42]. The effects of attenuation were modelled using ten distances from 2 to 20 m in 2 m increments.…”

Section: Acoustic Emission Databasementioning

confidence: 99%

Event identification in acoustic emission from wire breaks in pre-stressing/post-tensioning cables

Mahmoudkhani,

Zhao,

Cochingco

et al. 2024

Eng. Res. Express

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Steel tendons commonly used in pre-stressed/post-tensioned concrete structural systems can lose cross-section due to corrosion, eventually leading to acoustic emission (AE) events when the stress exceeds the breaking strength of the wires that make up the tendons. Reliable differentiation of wire break AE events from traffic or grout crack events is critical for monitoring large structures, even where the distance between sensors may produce highly attenuated signals. In this paper, the Fuzzy c-means clustering algorithm was employed to differentiate AEs released from breaking wires of steel tendons from a database of 13464 AEs, including wire breaks, environmental and grout crack AEs. Wire breaks and grout crack AEs were collected from axial loading tests of grouted tendons in which the load increased until a wire broke. Environmental acoustic signals were collected from a bridge. Then all the collected AEs were gathered in a database and post-processed to simulate attenuation of up to 20 m from source to sensor. To optimize the speed and reliability of the Fuzzy c-means clustering algorithm, a non-dominated sorting genetic algorithm-II (NSGA-II) was used to find the minimum number of acoustic features needed. The NSGA-II algorithm started with 201 possible acoustic features and found 12 combinations of features that resulted in more than 80% wire break detection accuracy. In contrast, less than 3% of grout cracks and 0% of environmental signals were detected as wire breaks. The proposed method is suitable for deployment in a large sensor network and has sufficiently low-computational requirements for at-the-sensor processing, eliminating the need to send high-frequency sampled data outside the sensor node.

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Section: Acoustic Emission Databasementioning

confidence: 99%