Model Updating of Complex Structures Using the Combination of Component Mode Synthesis and Kriging Predictor

Updating the structural model of complex structures is time-consuming due to the large size of the finite element model (FEM). Using conventional methods for these cases is computationally expensive or even impossible. A two-level method, which combined the Kriging predictor and the component mode synthesis (CMS) technique, was proposed to ensure the successful implementing of FEM updating of large-scale structures. In the first level, the CMS was applied to build a reasonable condensed FEM of complex structures. In the second level, the Kriging predictor that was deemed as a surrogate FEM in structural dynamics was generated based on the condensed FEM. Some key issues of the application of the metamodel (surrogate FEM) to FEM updating were also discussed. Finally, the effectiveness of the proposed method was demonstrated by updating the FEM of a real arch bridge with the measured modal parameters.

[1]  M. Bampton,et al.  Coupling of substructures for dynamic analyses. , 1968 .

[2]  J. S. Hunter,et al.  The 2 k—p Fractional Factorial Designs Part I , 2000, Technometrics.

[3]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[4]  John E. Mottershead,et al.  Model Updating In Structural Dynamics: A Survey , 1993 .

[5]  R. Fox,et al.  Rates of change of eigenvalues and eigenvectors. , 1968 .

[6]  C. F. Jeff Wu,et al.  Experiments: Planning, Analysis, and Parameter Design Optimization , 2000 .

[7]  Jer-Nan Juang,et al.  An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .

[8]  Felix Famoye,et al.  Plane Answers to Complex Questions: Theory of Linear Models , 2003, Technometrics.

[9]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[10]  A. Bahrami,et al.  Design of experiments using the Taguchi approach: Synthesis of ZnO nanoparticles , 2012 .

[11]  Henry P. Wynn,et al.  Screening, predicting, and computer experiments , 1992 .

[12]  David R. Cox,et al.  PRINCIPLES OF STATISTICAL INFERENCE , 2017 .

[13]  S. Rubin Improved Component-Mode Representation for Structural Dynamic Analysis , 1975 .

[14]  J. I The Design of Experiments , 1936, Nature.

[15]  Jerome Sacks,et al.  Designs for Computer Experiments , 1989 .

[16]  M. Friswell,et al.  Finite–element model updating using experimental test data: parametrization and regularization , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[17]  R. Dennis Cook,et al.  Cross-Validation of Regression Models , 1984 .

[18]  G. Box,et al.  On the Experimental Attainment of Optimum Conditions , 1951 .

[19]  John E. Mottershead,et al.  Finite Element Model Updating in Structural Dynamics , 1995 .

[20]  T. Simpson,et al.  Use of Kriging Models to Approximate Deterministic Computer Models , 2005 .

[21]  J. S. Hunter,et al.  The 2 k — p Fractional Factorial Designs , 1961 .

[22]  Genki Yagawa,et al.  Component mode synthesis for large-scale structural eigenanalysis , 2001 .

[23]  Robert E. Fulton,et al.  A Lanczos eigenvalue method on a parallel computer , 1987 .

[24]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .