Application of Nonlinear-Autoregressive-Exogenous model to predict the hysteretic behaviour of passive control systems. (15th February 2015)

• Record Type: Journal Article
• Authors: Chan, Ricky W.K.; Yuen, Jason K.K.; Lee, Eric W.M.; Arashpour, Mehrdad
• Is Part Of: Engineering structures. Volume 85(2015:Feb. 15)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-02-15
• Subjects: Nonlinear autoregressive model -- Hysteretic behaviour -- Steel slit damper -- Yielding shear panel device; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2014.12.007
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: The NARX performed better than the BWBN in modelling the hysteretic behaviour. A hybrid model combining the NARX and GRNN models was developed. The hysteretic behaviour of a new damper was well predicted by the hybrid model. The hybrid model optimized the damper design for maximum energy dissipation. Abstract: This paper proposes to use the nonlinear-autogressive models with exogenous input (NARX) model to predict the hysteretic behaviour of passive control systems. Although existing analytical hysteresis models such as the generalized Bouc–Wen (BW) model and the Bouc–Wen–Baber–Noori (BWBN) model can be used to model the hysteretic behaviour of passive control systems, the generalized BW model fails to account the pinching or stiffness degradation of hysteretic systems and the BWBN model requires to tune considerable parameters before its application. Therefore, we propose this alternative approach to predict the hysteresis response of passive control systems. The NARX model is the branch of artificial intelligence which is a promising tool for the forecasting of time series problems. We adopted the NARX model to predict the hysteretic behaviour with experimental results conducted on yielding shear panel device (YSPD) and steel slit damper (SSD), respectively. A good agreement between the experimental results on both YSPD and SSD and the prediction results was achieved. We also combined the NARX model and the general regression neural network (GRNN) as a hybrid model to predict hysteretic behaviour of the SSD of which the damper design was hidden from the model training process. The performance of using the hybrid model to predict the hysteretic behaviour of SSD is reasonably well. Finally, the applicability of the hybrid model has been successfully demonstrated through the optimisation of the geometrical parameters of the SSD. We concluded that the proposed NARX model is capable to predict the hysteretic behaviour of passive control systems.