Research Information System
42nd International Modal Analysis Conference, Florida, United States Of America, 29 January - 01 February 2024, pp.129-135, (Full Text)
Acoustic emission (AE) has recently gained a significant interest as a promising technique for monitoring damage progress in various structures including buildings, bridges, pipelines, and storage tanks. It relies on analyzing the acoustic activity, primarily associated with cracking phenomena, to assess structural integrity. However, one crucial parameter derived from AE signals is the time of arrival (ToA) of acoustic events, which is challenging to pick correctly. Accurate estimation of ToA is vital in localizing damage sources and enabling early detection of probable defects. Traditional approaches for ToA estimation often suffer from sensitivity to environmental and operational factors, such as imperfect coupling between AE transducers and the structures. To address this challenge, this study investigates the application of a one-dimensional convolutional neural network (1D CNN) for precise ToA estimation in AE signals. Experimental data acquired during compression tests on concrete specimens were utilized to train and test the model over windows of 300 and 1024 sample points in AE waveforms. By capturing a batch of representative acoustic features defined on a time basis and monitoring their evolution over time, the model was able to estimate ToA accurately in the window with 300 sample points. This proposed deep learning-based approach demonstrated promising potential for enhancing the accuracy and reliability of damage localization and early defect detection in various structural applications.