Chapter
Apr 26, 2012
Procedures for Fatigue Crack Growth Monitoring and Fatigue Life Prediction Using Acoustic Emission Data and Neural Networks
Authors: F. F. Barsoum [email protected], E. v. K. Hill, Y. Zhang, A. Korcak, and J. SulemanAuthor Affiliations
Publication: Structures Congress 2011
Abstract
This research applied the nondestructive testing (NDT) technique of acoustic emission (AE) to monitor fatigue cracking in steel structures and utilized artificial neural networks (ANNs) for fatigue life prediction. The neural network classification network known as a Kohonen self organizing map (SOM) was first used to classify the AE data into the different source mechanisms occurring during fatigue cracking. This research developed methodologies for placing sensors, collecting acoustic emission data and eliminating noise from the raw data. The ultimate goal of this research was to monitor for steel fatigue cracking and accurately predict remaining fatigue lives using AE flaw growth data and back propagation neural networks (BPNNs). The procedures for doing so are presented herein.
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© 2011 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Acoustics
- Air pollution
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Cracking
- Detection methods
- Emissions
- Engineering fundamentals
- Engineering mechanics
- Environmental engineering
- Fatigue (material)
- Fatigue life
- Fatigue tests
- Field tests
- Fracture mechanics
- Material mechanics
- Material properties
- Materials engineering
- Methodology (by type)
- Neural networks
- Nondestructive tests
- Pollution
- Solid mechanics
- Tests (by type)
Authors
Affiliations
M.ASCE
Associate Professor, Mechanical and Civil Engineering Department, Embry-Riddle Aeronautical University (ERAU), Daytona Beach, FL 32114, USA.E-mail: [email protected]
E. v. K. Hill
Professor, Aerospace Engineering Department, ERAU
Y. Zhang
Student, ERAU
A. Korcak
Student, ERAU
J. Suleman
Student, ERAU
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