Signal-Based Acoustic Emission Clustering for Differentiation of Damage Sources in Corroding Reinforced Concrete Beams
نویسندگان
چکیده
Corrosion in reinforced concrete (RC) structures is a major durability issue that requires attention terms of monitoring, order to assess the degraded condition and reduce financial costs for maintenance repair. The acoustic emission (AE) technique has been found be useful monitor damage due steel corrosion RC. However, further development monitoring protocols still necessary towards on-site application. In this paper, hierarchical clustering algorithm based on cross-correlation developed applied automatically distinguish sources during process. verified dummy samples corroding RC prisms. It able two clusters which first one containing AE signals corrosion, absorption, hydration, micro-cracking, second macro-cracking. Electromagnetic interference can distinguished as third cluster filtered subsequently. Due overlapping characteristics, differentiation not possible. Afterwards, scaled up sets beams, set with uniform zone, other local zone. addition, sample scale, successfully differentiate macro-cracking from micro-cracking. therefore serve an additional tool extent corrosion-induced damage.
منابع مشابه
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app12042154