TY - GEN
T1 - Fuzzy Finite Element Model Updating Using Metaheuristic Optimization Algorithms
AU - Boulkaibet, I.
AU - Marwala, T.
AU - Friswell, M. I.
AU - Khodaparast, H. H.
AU - Adhikari, S.
N1 - Publisher Copyright:
© The Society for Experimental Mechanics, Inc. 2017.
PY - 2017
Y1 - 2017
N2 - In this paper, a non-probabilistic method based on fuzzy logic is used to update finite element models (FEMs). Model updating techniques use the measured data to improve the accuracy of numerical models of structures. However, the measured data are contaminated with experimental noise and the models are inaccurate due to randomness in the parameters. This kind of aleatory uncertainty is irreducible, and may decrease the accuracy of the finite element model updating process. However, uncertainty quantification methods can be used to identify the uncertainty in the updating parameters. In this paper, the uncertainties associated with the modal parameters are defined as fuzzy membership functions, while the model updating procedure is defined as an optimization problem at each ’-cut level. To determine the membership functions of the updated parameters, an objective function is defined and minimized using two metaheuristic optimization algorithms: ant colony optimization (ACO) and particle swarm optimization (PSO). A structural example is used to investigate the accuracy of the fuzzy model updating strategy using the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy finite element model updating are compared with the Bayesian model updating results.
AB - In this paper, a non-probabilistic method based on fuzzy logic is used to update finite element models (FEMs). Model updating techniques use the measured data to improve the accuracy of numerical models of structures. However, the measured data are contaminated with experimental noise and the models are inaccurate due to randomness in the parameters. This kind of aleatory uncertainty is irreducible, and may decrease the accuracy of the finite element model updating process. However, uncertainty quantification methods can be used to identify the uncertainty in the updating parameters. In this paper, the uncertainties associated with the modal parameters are defined as fuzzy membership functions, while the model updating procedure is defined as an optimization problem at each ’-cut level. To determine the membership functions of the updated parameters, an objective function is defined and minimized using two metaheuristic optimization algorithms: ant colony optimization (ACO) and particle swarm optimization (PSO). A structural example is used to investigate the accuracy of the fuzzy model updating strategy using the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy finite element model updating are compared with the Bayesian model updating results.
KW - Ant colony optimization
KW - Bayesian
KW - Finite Element Model updating
KW - Fuzzy logic
KW - Fuzzy membership function
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=85138998919&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-53841-9_8
DO - 10.1007/978-3-319-53841-9_8
M3 - Conference contribution
AN - SCOPUS:85138998919
SN - 9783319538402
T3 - Conference Proceedings of the Society for Experimental Mechanics Series
SP - 91
EP - 101
BT - Special Topics in Structural Dynamics - Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics 2017
A2 - Dervilis, Nikolaos
PB - Springer
T2 - 35th IMAC, A Conference and Exposition on Structural Dynamics, 2017
Y2 - 30 January 2017 through 2 February 2017
ER -