Interval Identification of Structural Parameters Using Interval Deviation Degree and Monte Carlo Simulation

This paper is devoted to the interval identification of structural parameters in the aspect of uncertainty propagation and uncertainty quantification. The accurate interval estimation of structural responses can be efficiently obtained by application of Monte Carlo (MC) simulation combined with surrogate models. By means of the concept of interval length, a novel quantitative metric named as interval deviation degree (IDD) is constructed to characterize the disagreement of interval distributions between analytical modal data and measured modal data. The nominal values and interval radii of the system parameters are well estimated by solving two optimization problems. Finally, numerical and experimental case studies are given to illustrate the feasibility of the proposed method in the interval identification of structural parameters.