Adam J. Wootton
Fault Detection in Steel-Reinforced Concrete Using Echo State Networks
Wootton, Adam J.; Day, Charles R.; Haycock, Peter W.
Abstract
The cost of maintaining and repairing the world's ageing reinforced concrete infrastructure continues to increase, and is expected to cost the United States economy alone $58 billion by 2020. Consequently, the use of non-destructive testing technologies for the early identification of faults in roads and bridges is becoming increasingly important. One such technology is the Electromagnetic Anomaly Detection (EMAD) technique, which exploits non-destructive magnetic flux leakage to detect defects in steel reinforcing meshes embedded in concrete. Despite the increasing need for such techniques, the data analysis options currently in use are limited. This paper presents an application of Echo State Networks, a recurrent neural network from the field of reservoir computing that features a short-term memory, to data obtained using the EMAD technique. Having been trained to discern real defect signals from other anomalous magnetic features, the performance of the ESNs was then compared to that of an analytical data analysis technique that is currently used to process EMAD data. It was found that average ESN performance was comparable in terms of AUC, while the optimal threshold was more consistent, greatly aiding application in the `real-world'. A qualitative analysis of the output of both methods on an unseen testing dataset also demonstrated the superiority of ESNs for practical use as a real time tool for on-site inspections.
Citation
Wootton, A. J., Day, C. R., & Haycock, P. W. (2018). Fault Detection in Steel-Reinforced Concrete Using Echo State Networks. In 2018 International Joint Conference on Neural Networks (IJCNN) (1-8). https://doi.org/10.1109/IJCNN.2018.8489761
Start Date | Jul 8, 2018 |
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End Date | Jul 13, 2018 |
Acceptance Date | Jul 1, 2018 |
Online Publication Date | Oct 14, 2018 |
Publication Date | Oct 15, 2018 |
Publicly Available Date | May 26, 2023 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1-8 |
Series Title | 2018 International Joint Conference on Neural Networks (IJCNN) |
Book Title | 2018 International Joint Conference on Neural Networks (IJCNN) |
Chapter Number | Rio de Janeiro, Brazil |
ISBN | 978-1-5090-6015-3 |
DOI | https://doi.org/10.1109/IJCNN.2018.8489761 |
Keywords | bridges (structures), condition monitoring, construction industry, data analysis, echo, electromagnetic fields, inspection, magnetic flux, nondestructive testing, real-time systems, recurrent neural nets, reinforced concrete, reservoirs, roads, signal processing, steel, structural engineering, bridges, nondestructive magnetic flux leakage, steel reinforcing meshes, recurrent neural network, short-term memory, defect signals, analytical data analysis technique, United States economy, nondestructive testing technologies, roads, echo state networks, fault detection, steel-reinforced concrete infrastructure, electromagnetic anomaly detection technique, defects detection, ESN performance, reservoir computing, optimal threshold, real-time tool, on-site inspection, repairing cost, Reservoirs, Concrete, Corrosion, Steel, Magnetic flux, Bridges, Electromagnetics, Echo State Networks, Reservoir Computing, spatially varying data, structural health monitoring, steel-reinforced concrete, magnetic flux leakage |
Publisher URL | https://doi.org/10.1109/IJCNN.2018.8489761 |
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