Development of a semi-autonomous pulse and receive concrete inspection system

Atkinson, Tim, Pearson, Matthew Robert ORCID: https://orcid.org/0000-0003-1625-3611, McCrory, John Patrick, Marks, Ryan and Pullin, Rhys ORCID: https://orcid.org/0000-0002-2853-6099 2024. Development of a semi-autonomous pulse and receive concrete inspection system. Presented at: 36th Conference of the European Working Group on Acoustic Emission, Potsdam, Germany, 18-20 September 2024. e-Journal of Nondestructive Testing. , vol.29 (10) 10.58286/30247

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Abstract

Concrete structures are being subjected to increasing loads and used beyond their intended lifespan. When combined with operators shrinking maintenance budgets it is imperative structures are monitored for damage. Acoustic Emission (AE) and AE Tomography offer a method for damage detection and characterisation. However, they are encumbered by their equipment and setup requirements, and best used on areas where damage is already known to have occurred. An autonomous pulse catch system could be used to identify these areas. To explore the potential of such a system, seven concrete beams differentiated by their aggregate size were tested using an automated pulse and receive system. The effect of aggregate size, and pulse frequency on attenuation and wavespeed are investigated. The pulse regime consisted of narrowband pulses ranging from 30 kHz to 500 kHz. The results show that high frequency pulses experienced greater attenuation than low frequency pulses. Whilst a link appears between the size of the aggregate and the rate of attenuation of high frequency signals. A strong negative correlation between amplitude loss and pulse frequency was observed. For the specimen containing coarse aggregate, pulse velocity was constant between 100 kHz and 500 kHz, whereas specimens containing coarse aggregate experienced frequency dependant attenuation. This testing shows the significant potential for an automated pulse and receive system, which could lead to the development of an autonomous health monitoring system. In addition, the approach can be instrumental in developing large data sets, that can be used in Machine Learning processes, to develop a deeper understanding of AE signal propagation in concrete materials.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
ISSN:	1435-4934
Date of First Compliant Deposit:	16 January 2025
Date of Acceptance:	14 August 2024
Last Modified:	17 Jan 2025 09:53
URI:	https://orca.cardiff.ac.uk/id/eprint/175238

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