A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Application of neural networks to defect detection in cantilever beams with linearized damage behavior
This paper shows the feasibility of using a very simple feed-forward backpropagation neural network for fast and accurate estimation of the location and size of a crack in a cantilever beam. The presented network is trained and tested using data generated by a linear, closed-form, one-dimensional theoretical model of the cracked beam. It is shown that the neural network is a very attractive alternative to presently used methods.
Application of neural networks to defect detection in cantilever beams with linearized damage behavior
This paper shows the feasibility of using a very simple feed-forward backpropagation neural network for fast and accurate estimation of the location and size of a crack in a cantilever beam. The presented network is trained and tested using data generated by a linear, closed-form, one-dimensional theoretical model of the cracked beam. It is shown that the neural network is a very attractive alternative to presently used methods.
Application of neural networks to defect detection in cantilever beams with linearized damage behavior
Kawiecki, G. (author)
Journal of Intelligent Material Systems and Structures ; 10 ; 797-801
2000
5 Seiten, 13 Quellen
Article (Journal)
English
| British Library Online Contents | 1999
Automated model updating of multiple cracked cantilever beams for damage detection
| British Library Online Contents | 2017
Automated model updating of multiple cracked cantilever beams for damage detection
| British Library Online Contents | 2017
Automated model updating of multiple cracked cantilever beams for damage detection
| British Library Online Contents | 2017