Development of a portable NDE system with advanced signal processing and machine learning for health condition diagnosis of in-service timber utility poles

Publication Type:
Conference Proceeding
Citation:
Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016, 2017, pp. 1547 - 1552
Issue Date:
2017-01-01
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© 2017 Taylor & Francis Group, London. Aiming at current shortcomings of Non-Destructive Evaluation (NDE) in health condition estimation of timber utility poles, this paper put forward a novel testing method via combination of a portable NDE system, advanced signal processing and machine learning techniques. Primarily, the multi-sensing strategy is employed and incorporated in current NDE technique to capture reflected stress wave signals, avoiding difficult interpretation of complicated wave propagation by only one sensor. Secondly, advanced signal processing methods, such as Ensemble Empirical Mode Decomposition (EEMD) and Principal Component Analysis (PCA), are introduced to extract effective wave patterns that are sensitive to structural damage. Moreover, based on captured signal features, the state-of-the-art machine learning techniques are applied to implement the condition assessment. Finally, field testing results of 26 decommissioned timber poles at Mason Park in Sydney are used to validate the effectiveness of the proposed method.
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