Nonlinear system identification of smart reinforced concrete structures under impact loads. (September 2016)

• Record Type: Journal Article
• Authors: Sarp Arsava, K; Nam, Yunyoung; Kim, Yeesock
• Is Part Of: Journal of vibration and control. Volume 22:Number 16(2016)
• Page Start: 3576
• Page End: 3600
• Publication Date: 2016-09
• Subjects: Impact load -- magnetorheological (MR) damper -- smart structures -- system identification -- wavelet-based time delayed adaptive neuro-fuzzy inference system (W-TANFIS) -- wavelet transform; Vibration -- Periodicals; Damping (Mechanics) -- Periodicals; 620.3
• DOI: 10.1177/1077546314563966
• Languages: English
• ISSNs: 1077-5463

Abstract

This paper proposes system identification models of smart concrete structures equipped with magnetorheological (MR) dampers under a variety of high impact loads. The proposed model was used to predict and analyze the highly nonlinear behavior of integrated structure-control systems subjected to impact loading. Highly nonlinear behavior of the integrated structure-MR damper was represented by a wavelet-based time delayed adaptive neuro-fuzzy inference system (W-TANFIS). To generate sets of input and output data for training and validating the proposed W-TANFIS models, experimental studies were performed on a smart reinforced concrete beam under a variety of impact loads. The impact forces and current signals on an MR damper were used as input signals for training the W-TANFIS to predict the acceleration, deflection, and strain responses. As a benchmark, an adaptive neuro-fuzzy inference system (ANFIS) was used. It was demonstrated that the proposed W-TANFIS framework is effective in anticipating the structural responses of the reinforced concrete beam-MR damper system subjected to impact loading. In addition, the comparison of the W-TANFIS and ANFIS models demonstrated that the W-TANFIS model has better performance over the ANFIS model.