Chen, Z., Zou, Lilong ORCID: https://orcid.org/0000-0002-5109-4866 and Du, Y. (2023) Internal decay inspection of tree trunks using 3D point cloud and reverse time migration of ground penetrating radar data. NDT & E International, 137.

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Abstract

The tree trunk necrosis and collapses in urban environments have caused large amounts of damage that threaten human safety. Several non-destructive testing methods have been applied to monitor and detect decay inside the trunks. Ground penetrating radar (GPR) has gained recognition due to its high efficiency and high resolution. However, the irregular shape of the trunks and their complicated internal structures make it still challenging to interpret the GPR data. In this paper, a combined approach is proposed to image the inner structures inside the irregular-shape trunks. Firstly, a 3D point cloud technique and the Canny edge detection algorithm are applied to obtain the curved outline of the trunk and the corresponding coordinates of each GPR A-scan data. Then, back-propagation (BP) and reverse time migration (RTM) are applied to reconstruct an image of the trunk cross-section. Finally, a de-noising method based on the total variation regularization is applied to suppress artifacts in the migrated images. Numerical experiment results show that the RTM algorithm based on the zero-time imaging condition can image the inner structure of the tree cross-section more accurately than the BP algorithm at comparable computation costs. Two diseased tree trunks further verify the proposed approach with subsistent decay and cavities in the laboratory and the field, respectively. The decay and cavities are well-focused in the migrated images. A visual inspection validates the field experiment results after the tree trunk was sawed down. It is concluded that the proposed approach can be applied to the internal decay inspection of tree trunks with high efficiency.

Item Type:	Article
Identifier:	10.1016/j.ndteint.2023.102853
Subjects:	Construction and engineering > Civil and environmental engineering
Depositing User:	Marc Forster
Date Deposited:	12 Sep 2024 10:28
Last Modified:	01 Oct 2024 06:06
URI:	https://repository.uwl.ac.uk/id/eprint/12428

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