TY - JOUR

T1 - Interval analysis for system identification of linear mdof structures in the presence of modeling errors

AU - Zhang, M. Q.

AU - Beer, M.

AU - Koh, C. G.

PY - 2012/11/1

Y1 - 2012/11/1

N2 - Modeling errors, represented as uncertainty associated with the parameters of a mathematical model, inevitably exist in the process of constructing a theoretical model of real structures and limit the practical application of system identification. They are usually represented either in a deterministic manner or in a probabilistic way. However, if the available information is uncertain but of a nonprobabilistic nature, as it may emerge from a lack of knowledge about the sources and characteristics of model uncertainties, a third type of approach may be useful. Presented in this paperis an approach to treat modeling errors with the aid of intervals, resulting in bounded values for the identified parameters. Compared with the traditional identification procedures where model-based forward dynamic analysis is often involved, computing bounded time history responses from a computational model with interval parameters is avoided. Two required submatrices are firstly extracted from identified state-space models by applying a subspace identification method to the measurements, and then interval analysis is performed upon these two matrices to estimate the bounded uncertainty in the identified parameters. The effectiveness of the proposed methodology is evaluated through numerical simulationof a linear multipleedegree-of-freedom (MDOF) system when modeling errors in the mass and damping parameters are taken into account. The results show the ability of the proposed method to maintain sharp enclosures of the identified stiffness parameters.

AB - Modeling errors, represented as uncertainty associated with the parameters of a mathematical model, inevitably exist in the process of constructing a theoretical model of real structures and limit the practical application of system identification. They are usually represented either in a deterministic manner or in a probabilistic way. However, if the available information is uncertain but of a nonprobabilistic nature, as it may emerge from a lack of knowledge about the sources and characteristics of model uncertainties, a third type of approach may be useful. Presented in this paperis an approach to treat modeling errors with the aid of intervals, resulting in bounded values for the identified parameters. Compared with the traditional identification procedures where model-based forward dynamic analysis is often involved, computing bounded time history responses from a computational model with interval parameters is avoided. Two required submatrices are firstly extracted from identified state-space models by applying a subspace identification method to the measurements, and then interval analysis is performed upon these two matrices to estimate the bounded uncertainty in the identified parameters. The effectiveness of the proposed methodology is evaluated through numerical simulationof a linear multipleedegree-of-freedom (MDOF) system when modeling errors in the mass and damping parameters are taken into account. The results show the ability of the proposed method to maintain sharp enclosures of the identified stiffness parameters.

KW - Interval analysis

KW - Modeling errors

KW - State space

KW - Structural dynamics

KW - Subspace identification method

KW - System identification

KW - Uncertainty estimation

UR - http://www.scopus.com/inward/record.url?scp=84876896176&partnerID=8YFLogxK

U2 - 10.1061/(ASCE)EM.1943-7889.0000433

DO - 10.1061/(ASCE)EM.1943-7889.0000433

M3 - Article

AN - SCOPUS:84876896176

VL - 138

SP - 1326

EP - 1338

JO - Journal of Engineering Mechanics - ASCE

JF - Journal of Engineering Mechanics - ASCE

SN - 0733-9399

IS - 11

ER -