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Damage detection in structural beam elements using hybrid neuro fuzzy systems
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  • Journal title : Smart Structures and Systems
  • Volume 16, Issue 6,  2015, pp.1107-1132
  • Publisher : Techno-Press
  • DOI : 10.12989/sss.2015.16.6.1107
 Title & Authors
Damage detection in structural beam elements using hybrid neuro fuzzy systems
Aydin, Kamil; Kisi, Ozgur;
 Abstract
A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.
 Keywords
neuro fuzzy system;grid partitioning;subtractive clustering;beam;damage detection;
 Language
English
 Cited by
 References
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