Paper
20 October 2009 Nonparametric dynamic modeling of a non-linear frame structure with MR dampers
B. Xu, Z. G. Huang, S. F. Masri
Author Affiliations +
Proceedings Volume 7493, Second International Conference on Smart Materials and Nanotechnology in Engineering; 749339 (2009) https://doi.org/10.1117/12.843397
Event: Second International Conference on Smart Materials and Nanotechnology in Engineering, 2009, Weihai, China
Abstract
Identification of nonlinear dynamic systems using vibration measurements is crucial for efficient and reliable damage detection in structural health monitoring and control system design. Because of the complexity of control devices, it is usually difficult to model the nonlinear control devices with enough accuracy in a parametric form. In this study, a multi-storey steel-frame model structure equipped with magneto-rheological (MR) dampers, which were employed to introduce nonlinear phenomena to the model structure, was modeled with a neural network in a nonparametric way. Corresponding to the availability of dynamic response measurements, two different network models were proposed to predict the vibration response of the nonlinear model structure. Raw dynamic response measurements of the model structure under a certain impulse excitation was employed to train the two neural network models and the generality of the trained neural network models were validated in the form of forecasting the raw test dynamic response measurements of the model structure under other impulse excitation conditions. Results show that two neural network models provide a reliable way for the modeling of nonlinear dynamic structures and present a useful way for the control system design of engineering structures equipped with nonlinear control devices.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
B. Xu, Z. G. Huang, and S. F. Masri "Nonparametric dynamic modeling of a non-linear frame structure with MR dampers", Proc. SPIE 7493, Second International Conference on Smart Materials and Nanotechnology in Engineering, 749339 (20 October 2009); https://doi.org/10.1117/12.843397
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neural networks

Data modeling

Systems modeling

Instrument modeling

Neurons

Performance modeling

Structural engineering

Back to Top