TY - GEN
T1 - Quantification of parametric model uncertainties in finite element model updating problem via fuzzy numbers
AU - Erdogan, Yildirim Serhat
AU - Gul, Mustafa
AU - Catbas, F. Necati
AU - Bakir, Pelin Gundes
PY - 2013
Y1 - 2013
N2 - Analytical and numerical models that simulate the physical processes inevitably contain errors due to the mathematical simplifications and the lack of knowledge about the physical parameters that control the actual behavior. In this sense, parametric identification of civil engineering structures using uncertain numerical models should be subject to a particular interest in terms of accuracy and reliability of identified models. In this study, model uncertainties are modeled by fuzzy numbers and quantified using fuzzy model updating approach. In order to find the possible variation range of the response parameters (e.g. natural frequencies, mode shapes and strains) using uncertain finite element model, successive updating is employed. A simplified approach is proposed in order to facilitate the time consuming successive model updating phase. The identified variation range of the response parameters is employed to construct the fuzzy membership functions for each response parameter. Finally, fuzzy finite element model updating method (FFEMU) is used to obtain the membership functions of the model parameters. Different sets of model parameters are chosen to represent different models in terms of accuracy and these parameters are identified in the same way to investigate the model complexity. A two span laboratory grid structure developed for simulating bridge structures is used to validate and demonstrate the proposed approaches. The results show that the proposed approaches can efficiently be utilized to quantify the modeling uncertainties for more realizable and quantitative condition assessment and decision making purposes.
AB - Analytical and numerical models that simulate the physical processes inevitably contain errors due to the mathematical simplifications and the lack of knowledge about the physical parameters that control the actual behavior. In this sense, parametric identification of civil engineering structures using uncertain numerical models should be subject to a particular interest in terms of accuracy and reliability of identified models. In this study, model uncertainties are modeled by fuzzy numbers and quantified using fuzzy model updating approach. In order to find the possible variation range of the response parameters (e.g. natural frequencies, mode shapes and strains) using uncertain finite element model, successive updating is employed. A simplified approach is proposed in order to facilitate the time consuming successive model updating phase. The identified variation range of the response parameters is employed to construct the fuzzy membership functions for each response parameter. Finally, fuzzy finite element model updating method (FFEMU) is used to obtain the membership functions of the model parameters. Different sets of model parameters are chosen to represent different models in terms of accuracy and these parameters are identified in the same way to investigate the model complexity. A two span laboratory grid structure developed for simulating bridge structures is used to validate and demonstrate the proposed approaches. The results show that the proposed approaches can efficiently be utilized to quantify the modeling uncertainties for more realizable and quantitative condition assessment and decision making purposes.
KW - Finite element model updating
KW - Fuzzy numbers
KW - Inverse fuzzy problems
KW - Model uncertainties
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=84880532325&partnerID=8YFLogxK
U2 - 10.1007/978-1-4614-6564-5_7
DO - 10.1007/978-1-4614-6564-5_7
M3 - Conference contribution
AN - SCOPUS:84880532325
SN - 9781461465638
T3 - Conference Proceedings of the Society for Experimental Mechanics Series
SP - 67
EP - 74
BT - Topics in Model Validation and Uncertainty Quantification - Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013
T2 - 31st IMAC, A Conference on Structural Dynamics, 2013
Y2 - 11 February 2013 through 14 February 2013
ER -