NDLI: Damage detection on a three-storey steel frame using artificial neural networks and genetic algorithms

Content Provider	Springer Nature Link
Author	Betti, Michele Facchini, Luca Biagini, Paolo
Copyright Year	2014
Abstract	The paper, through the discussion of an experimental investigation, considers a combined approach based on artificial neural networks and genetic algorithms for structural damage identification. A reduced scale three-storey steel spatial frame was instrumented by a series of 12 accelerometers and progressively damaged by cutting one of its columns just above the first storey. Accelerations induced by ambient vibrations were recorded as the frame was progressively damaged, and the deepness of the cut was taken as the entity of the damage. At every damage level the modal properties (natural frequencies and modal shapes) of the steel frame were evaluated through a neural network based approach. Subsequently, two error functions that measure the differences between the experimental results and those calculated from a finite element model of the steel frame were defined and a genetic algorithm was employed for damage detection. Results of the experimentation (where damage was known as both location and extent) were compared with the results of the optimization algorithm in order to verify its ability to match the actual damage.
Starting Page	875
Ending Page	886
Page Count	12
File Format	PDF
ISSN	00256455
Journal	Meccanica
Volume Number	50
Issue Number	3
e-ISSN	15729648
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-12-25
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Steel-frame structures Damage detection Operational modal analysis Artificial neural networks Genetic algorithms Structural health monitoring Mechanics Civil Engineering Automotive Engineering Mechanical Engineering
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Condensed Matter Physics Mechanical Engineering

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