Wootton, AJ ORCID: https://orcid.org/0000-0001-6432-0503, Day, CR ORCID: https://orcid.org/0000-0001-9707-9189 and Haycock, P (2022) Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks. Structural Health Monitoring: an international journal.

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Abstract

The degradation of roads is an expensive problem: in the UK alone, £27 billion was spent on road repairs between 2013 and 2019. One potential cost-saver is the early, non-destructive detection of faults. There are many available techniques, each with its own benefits and drawbacks. This paper builds upon the successful processing of Magnetic Flux Leakage (MFL) data by Echo State Networks (ESNs) for damage diagnostics, by augmenting ESNs with the depth of concrete cover as part of a data fusion approach. This fusion-based ESN outperformed a number of non fusion ESN comparators and a previously used analytical technique. Additionally, the fusion ESN had an optimal threshold value whose standard deviation was three times smaller than that of the nearest alternative technique, potentially prompting a move towards automated defect detection in ‘real-world’ applications.

Item Type: Article
Additional Information: https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Uncontrolled Keywords: Echo state networks, non-destructive testing, magnetic flux leakage, cover depth, steel-reinforced concrete, heterogeneous data fusion, signal processing, damage detection
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 24 Feb 2022 09:06
Last Modified: 20 May 2022 08:52
URI: https://eprints.keele.ac.uk/id/eprint/10643

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