Acoustic emission propagation characteristics and damage source localization of asphalt mixtures

• Published in: Construction & building materials 2020-08, Vol.252, p.119086, Article 119086
• Main Authors: Qiu, Xin, Wang, Yujie, Xu, Jingxian, Xiao, Shanglin, Li, Chenlei
• Format: Article
• Language: English
• Subjects: Acoustic emission; Asphalt mixture; Source localization; Wavelet decomposition; Wavelet threshold denoising
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2020.119086

•Acoustic emission could accurately detect damage sources of asphalt mixtures.•The AE signal has an effective propagation range due to signal attenuation.•The proposed denoising process could minimize the noise interference.•The enhanced TDOA method could improve the accuracy of the source localization. Fracture of asphalt materials has been a critical issue that affects the fatigue performance and service life of asphalt pavements. Acoustic emission (AE) technique, as a kind of non-destructive testing (NDT) method, can effectively detect minor damage in various materials. The objective of this paper was to propose a damage source localization method suitable for understanding the fracture behavior of asphalt mixtures by AE detection. Firstly, the pencil-lead-break (PLB) tests were conducted on asphalt mixture beams to explore the propagation characteristics of AE signals, and a reasonable layout scheme of AE sensors was determined for monitoring the AE process of asphalt mixtures. Secondly, the wavelet threshold denoising method was utilized to extract effective information from AE signals associated with the pencil-lead breaks on the surface of asphalt mixture beam by simultaneously considering the wavelet basis functions, decomposition levels and threshold rules. Finally, an effective time difference of arrival (TDOA) estimation method combining noise reduction, wavelet decomposition and cross-correlation processing was established to accurately locate the damage source of asphalt mixtures. The results show that there is less serious attenuation of amplitude and energy of AE parameters and frequency spectrum of AE waveforms within the range of 100 mm in asphalt mixtures. The larger signal-to-noise ratio (SNR) and the smaller root mean square error (RMSE) of denoised AE signals indicate that the threshold denoising method optimized by the Fruit Fly Optimization Algorithm (FOA) is more effective for AE detection of asphalt mixtures. The improved TDOA estimation method could obtain a minimum TDOA value, and the calculated locations of AE events are closer to the actual PLB points.