Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition

2018 ◽  
Vol 26 (2) ◽  
pp. e2288 ◽  
Author(s):  
Saeed Eftekhar Azam ◽  
Ahmed Rageh ◽  
Daniel Linzell
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Damage Detection ◽  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Structural Systems ◽  
Artificial Neural ◽  
Proper Orthogonal

Experimental Application of a Damage Localisation Technique Based on Smoothed Proper Orthogonal Modes

2007 ◽  
Vol 347 ◽  
pp. 121-126 ◽  
Author(s):  
U. Galvanetto ◽  
L. Monopoli ◽  
Cecilia Surace ◽  
Alessandra Tassotti
Keyword(s):  
Damage Detection ◽  
Proper Orthogonal Decomposition ◽  
Real Time ◽  
Orthogonal Decomposition ◽  
Steel Beams ◽  
Acceleration Response ◽  
Experimental Application ◽  
Lab Experiments ◽  
Proper Orthogonal Modes ◽  
Proper Orthogonal

The paper presents an experimental application of the Proper Orthogonal Decomposition (POD) to damage detection in steel beams. A damaged beam has been excited with a sinusoidal force, the acceleration response at points regularly spaced along the structure has been recorded and the relevant Proper Orthogonal Modes calculated. In this way it is possible to locate damage by comparing the measured dominant Proper Orthogonal Mode with a smoothed version of it which does not exhibit apparent peaks in correspondence with the damage. One of the principal advantages of the proposed damage detection technique is that it does not require vibration measurements to be performed on the undamaged structure. Moreover the ‘optimality’ of the proper orthogonal modes only requires the use of a few (one-two) of them which can be computed in real time during lab experiments or while the structure is functioning in the field.

Sign in / Sign up

Export Citation Format

Share Document