Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning
Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 1
Abstract
Identification of noise signals is one of the most challenging problems in health monitoring of a bridge structure using acoustic emission (AE) monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common methods used in identifying signals from the defects of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focuses on the AE noise signal from a bridge in an operation state and other specific loading states, which is diagnosed in the hardware filtering technology, spatial identification, and self-organizing map (SOM) neural network, to obtain the new noise recognition methods. It is found that the first two methods can indeed filter the noise signal, but the filtering rate can only reach about 50%, and can barely filter strong noise signals. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90% and 100%, respectively. The trained network was used to test 183 sample signals, and the defect signal detection accuracy reached 76% and 78.8%; therefore, the noise signal filtering effect is significantly improved.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The first author would like to acknowledge the financial support provided by the National Natural Science Foundation of China (Grant No. 51968014), Key R&D projects in the Guangxi Autonomous Region (Grant No. AA20302006), and Guangxi Key Laboratory of New Energy and Building Energy Saving (Grant No. 19-J-21-20).
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Published In
Journal of Performance of Constructed Facilities
Volume 37 • Issue 1 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 29, 2021
Accepted: Jul 27, 2022
Published online: Oct 29, 2022
Published in print: Feb 1, 2023
Discussion open until: Mar 29, 2023
ASCE Technical Topics:
- Acoustics
- Air pollution
- Artificial intelligence and machine learning
- Bridge engineering
- Bridges
- Computer programming
- Computing in civil engineering
- Defects and imperfections
- Detection methods
- Emissions
- Engineering fundamentals
- Environmental engineering
- Filters
- Filtration
- Geomatics
- Mapping
- Materials characterization
- Materials engineering
- Methodology (by type)
- Neural networks
- Noise pollution
- Pollution
- Structural engineering
- Surveying methods
- Water treatment
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