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A platform for research: civil engineering, architecture and urbanism
Generalized Response Surface Model Updating Using Time Domain Data
In finite-element (FE) model updating using response surface (RS) models as surrogate, the procedure of finding an appropriate design to build the RS models requires a number of trial-and-error approaches with different designs and subset models. To address this issue, a procedure is proposed in this paper to design and fit proper RS models in FE model updating problems. Also, formulation of the problem in an iterative format in time domain is proposed to extract more information from measured signals and compensate for the error present in the regressed models. This procedure is applicable to both linear and nonlinear models under static or dynamic analysis. The proposed methodology is applied to a numerical case study of a steel frame with global nonlinearity. Appropriate design and model order are successfully established and optimization in time performs well in all the simulated scenarios. Finally, the performance of this method in presence of measurement noise is compared with a method based on sensitivity analysis in terms of required time and accuracy.
Generalized Response Surface Model Updating Using Time Domain Data
In finite-element (FE) model updating using response surface (RS) models as surrogate, the procedure of finding an appropriate design to build the RS models requires a number of trial-and-error approaches with different designs and subset models. To address this issue, a procedure is proposed in this paper to design and fit proper RS models in FE model updating problems. Also, formulation of the problem in an iterative format in time domain is proposed to extract more information from measured signals and compensate for the error present in the regressed models. This procedure is applicable to both linear and nonlinear models under static or dynamic analysis. The proposed methodology is applied to a numerical case study of a steel frame with global nonlinearity. Appropriate design and model order are successfully established and optimization in time performs well in all the simulated scenarios. Finally, the performance of this method in presence of measurement noise is compared with a method based on sensitivity analysis in terms of required time and accuracy.
Generalized Response Surface Model Updating Using Time Domain Data
Shahidi, S. Golnaz (author) / Pakzad, Shamim N. (author)
2013-07-18
Article (Journal)
Electronic Resource
Unknown
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