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Proceedings Paper

Neural-network-based signal monitoring in a smart structural system
Author(s): Stuart S. Chen; Sungkon Kim
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Paper Abstract

This paper focuses on the signal processing aspect of a smart structure computational support environment for health monitoring, investigating the use of neural networks to identify and locate structural damage in a steel truss structure instrumented with accelerometers and strain gauges. Cracking damage is simulated by introducing sawcuts into the main members of the structure. Results using accelerometer data alone indicate that Quickprop backpropagation neural networks constitute a promising tool for these purposes, although network performance in locating damage should be improved by use of strain data as well.

Paper Details

Date Published: 1 May 1994
PDF: 11 pages
Proc. SPIE 2191, Smart Structures and Materials 1994: Smart Sensing, Processing, and Instrumentation, (1 May 1994); doi: 10.1117/12.173945
Show Author Affiliations
Stuart S. Chen, SUNY/Buffalo (United States)
Sungkon Kim, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 2191:
Smart Structures and Materials 1994: Smart Sensing, Processing, and Instrumentation
James S. Sirkis, Editor(s)

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