Neural network based signal monitoring in a smart structural system

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.