Nonlinear system identification of smart reinforced concrete structures under impact loads
- Authors
- Arsava, K. Sarp; Nam, Yunyoung; Kim, Yeesock
- Issue Date
- Sep-2016
- Publisher
- SAGE Publications
- Keywords
- Impact load; magnetorheological (MR) damper; smart structures; system identification; wavelet-based time delayed adaptive neuro-fuzzy inference system (W-TANFIS); wavelet transform
- Citation
- JVC/Journal of Vibration and Control, v.22, no.16, pp 3576 - 3600
- Pages
- 25
- Journal Title
- JVC/Journal of Vibration and Control
- Volume
- 22
- Number
- 16
- Start Page
- 3576
- End Page
- 3600
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/8816
- DOI
- 10.1177/1077546314563966
- ISSN
- 1077-5463
1741-2986
- 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.
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Collections - College of Engineering > Department of Computer Science and Engineering > 1. Journal Articles
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