Print Email Facebook Twitter Deep residual learning for acoustic emission source localization in A steel-concrete composite slab Title Deep residual learning for acoustic emission source localization in A steel-concrete composite slab Author Zhou, Y. (TU Delft Concrete Structures) Liang, M. (TU Delft Materials and Environment) Yue, X. (TU Delft Electronic Instrumentation) Date 2023 Abstract Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel deep learning algorithm called deep residual network (DRN), a structural health monitoring (SHM) strategy is proposed for AE source localization through classifying and recognizing the AE signals generated in different sub-regions of critical areas in structures. Hammer hits and pencil-leak break (PLB) tests were carried out on a steel-concrete composite slab specimen to register time-domain AE signals under multiple structural damage conditions. The obtained time-domain AE signals were then converted into time-frequency images as inputs for the proposed DRN architecture using the continuous wavelet transform (CWT). The DRNs were trained, validated and tested by AE signals generated from different source types at various damage states of the slab specimen. The proposed DRN architecture shows an effective potential for AE source localization. The results show that the DRN models pre-trained by the AE signals obtained in the undamaged specimen are able to accurately classify and identify the locations of different types of AE sources with 3–4.5 cm intervals even when multiple cracks with widths up to 4–6 mm are present in the wave travel paths. Moreover, the influence factors on the model performance are investigated, including structural damage conditions, sensor-to-source distances and AE sensor mounting positions; in accordance with the parametric analyses, recommendations are proposed for the engineering application of the proposed SHM strategy. Subject Acoustic emission (AE) source localizationDeep residual network (DRN)Image classificationSteel-concrete composite structuresWave propagation in damaged structures To reference this document use: http://resolver.tudelft.nl/uuid:58cbb288-c334-47c6-b310-5013ab08e67f DOI https://doi.org/10.1016/j.conbuildmat.2023.134220 ISSN 0950-0618 Source Construction and Building Materials, 411 Part of collection Institutional Repository Document type journal article Rights © 2023 Y. Zhou, M. Liang, X. Yue Files PDF 1_s2.0_S0950061823039387_main.pdf 12.51 MB Close viewer /islandora/object/uuid:58cbb288-c334-47c6-b310-5013ab08e67f/datastream/OBJ/view