Fuzzy finite-element approach for the vibration analysis of imprecisely-defined systems
Finite Elements in Analysis and Design - Special issue: Robert J. Melosh medal competition
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Improving interval analysis in finite element calculations by means of affine arithmetic
Computers and Structures
Perturbation finite element method of structural analysis under fuzzy environments
Engineering Applications of Artificial Intelligence
Applied Fuzzy Arithmetic: An Introduction with Engineering Applications
Applied Fuzzy Arithmetic: An Introduction with Engineering Applications
Finite Elements in Analysis and Design
Finite Elements in Analysis and Design
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A fuzzy finite element model updating (FFEMU) method is presented in this study for the damage detection problem. The uncertainty caused by the measurement noise in modal parameters is described by fuzzy numbers. Inverse analysis is formulated as a constrained optimization problem at each @a-cut level. Membership functions of each updating parameter which correspond to reduction in bending stiffness of the finite elements is determined by minimizing an objective function using a hybrid version of genetic algorithms (GA) and particle swarm optimization method (PSO) which is very efficient in terms of accuracy and robustness. Practical evaluation of the approximate bounds of the interval modal parameters in FFEMU iterations is addressed. A probabilistic analysis is performed using Monte Carlo simulation (MCS) and the results are compared with presented FFEMU method. It is apparent from numerical simulations that the proposed method is well capable in finding the membership functions of the updating parameters within reasonable accuracy. It is also shown that the results obtained by FFEMU are in good agreement with the MCS results while FFEMU is not as computationally expensive as the MCS method. Nevertheless, the proposed FFEMU do not required derivatives of the objective function like existing methods except in the deterministic case.