Identification of linear and non-linear physical parameters of multistory shear buildings using artificial neural network

Abstract

In this research, a novel method has been proposed for simultaneous identification of physical parameters (i.e. mass, stiffness and damping matrices) as well as for separation of linear physical parameters from non-linear ones by use of artificial neural networks (ANNs) in non-linear multi-degree-of-freedom (DOF) systems. To design an appropriate ANN in this method, the vibration equation of the lumped mass system is re-arranged into a new form. The proposed ANN has two parts; namely linear and non-linear. The outputs of the non-linear part of the proposed ANN identify the non-linear properties of the system. Moreover, the number of the employed ANNs is equal to the number of DOFs considered for the non-linear system. Initially, the first ANN is trained and the corresponding parameters of the linear part are then identified. Afterwards, the second ANN is trained using the identified mass of the first DOF. This procedure of training and identifying the parameters of each DOF is repeated in this manner until all DOF parameters are identified; then the identification of the non-linear part begins by exciting the non-linear part of ANN. Finally, the linear stiffness values and non-linear patterns are obtained from the output results of the excitations. The proposed algorithm has been verified through some examples.

Keywords:

• Mass–damping–stiffness identification
• linear and non-linear identification
• inverse problems
• shear building model
• forced vibration
• artificial neural networks

Acknowledgements

The authors would like to acknowledge and express their special gratitude to anonymous reviewers, for their constructive advice that improved the manuscript.