Identification of non-linear structural parameters under limited input and output measurements

• Published in: International journal of non-linear mechanics Vol. 47; no. 10; pp. 1141 - 1146
• Main Authors: Lei, Ying, Wu, Yan, Li, Tao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2012
• Subjects: Algorithms; Excitation; Extended Kalman estimator; Hysteresis; Interconnection; Least squares method; Least-squares estimation; Mathematical models; Non-linear structures; Nonlinearity; Structural parameter identification; Substructure approach; Substructures; Unknown input; Structural parameter identification; Least-squares estimation; Unknown input; Substructure approach; Non-linear structures; Extended Kalman estimator
• ISSN: 0020-7462; 1878-5638
• DOI: 10.1016/j.ijnonlinmec.2011.09.004

In this paper, an algorithm is proposed for the identification of non-linear structural parameters under limited input and output measurements. The algorithm is based on the sequential application of extended Kalman estimator for the non-linear structural parameters and the least-squares estimation for the unmeasured excitation. First, the identification of small size non-linear structural parameters is studied. Then, based on the substructure approach, the algorithm is extended to identify large size non-linear structural parameters. Interconnection effect between adjacent substructures is accounted by considering the interconnection forces at substructural interfaces as ‘additional unknown inputs’ to the substructures. The general case that measurements at the substructure interfaces are not available is considered. Two numerical examples which identify non-linear hysteretic parameters of a four-story and an eight-story hysteretic shear-beam building subject to unmeasured excitation respectively, demonstrate the efficiency of the proposed algorithm. ► Identify non-linear parameters with limited input and output measurements. ► Sequential application of extended Kalman estimator and least-squares estimation. ► Simplifies the identification problem compared with other algorithms available. ► Identify large size non-linear structural parameters. ► Identification without the measurements at the substructure interfaces.