Design and development of 3-stage determination of damage location using Mamdani-adaptive genetic-Sugeno model

Damage detection in structural elements like beams is one of important research areas for
health monitoring. Initiation of a fault in the form of a crack or any damage puts a limitation
on the service life of a structural member. So, in this paper, a method is proposed which
uses the advantages of soft computing techniques like Fuzzy Inference Systems (Mamdani
and Sugeno) and Adaptive Genetic Algorithm for three stage refinement of the data base
generated using dynamic responses from a cracked fixed-free aluminum alloy beam element.
For the crack element reference, a finite element model of a single transverse crack has been
considered. The proposed method describes both Mamdani and Sugeno Fuzzy Inference
Systems for training of damage parameters. In the Adaptive Genetic Algorithm, a statistics
based method has been incorporated to limit the randomness of the search process. Finally,
the results from the Mamdani-Adaptive Genetic-Sugeno model (MAS) are validated with
the results from the experimental analysis.