Title :
A neural network approach to structure damage assessment
Author :
Faravelli, L. ; Pisano, A.A.
Author_Institution :
Dept. of Struct. Mech., Pavia Univ., Italy
Abstract :
Presents a method for damage detection in multi-bay planar truss structures. A neural network is trained by transfer functions of the structural system. The approach allows one to avoid all the problems which characterize the techniques based on system parameter identification. The neural network architecture and size, the choice of the learning rule and of the corresponding parameters are discussed. The neural network approach is able to uniquely identify the damaged element in almost all of the investigated cases
Keywords :
failure (mechanical); failure analysis; learning (artificial intelligence); neural net architecture; parameter estimation; structural engineering computing; transfer functions; damage detection; learning rule; multi-bay planar truss structures; neural network architecture; neural network size; neural network training; structure damage assessment; system parameter identification; transfer functions; Actuators; Aerodynamics; Artificial neural networks; Feedforward neural networks; Finite element methods; Monitoring; Neural networks; Parameter estimation; Signal processing; Transfer functions;
Conference_Titel :
Intelligent Information Systems, 1997. IIS '97. Proceedings
Conference_Location :
Grand Bahama Island
Print_ISBN :
0-8186-8218-3
DOI :
10.1109/IIS.1997.645426
