Mohsen Askari
University of Sydney
30 Papers
109 Citations
Mohsen Askari is an academic researcher from University of Sydney. The author has contributed to research in topics: Damper & Control theory. The author has an hindex of 9, co-authored 29 publications. Previous affiliations of Mohsen Askari include University of Technology, Sydney & University of Western Sydney.
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Papers
Experimental study of semi-active magnetorheological elastomer base isolation system using optimal neuro fuzzy logic control
TL;DR: It is proven that the smart MRE base isolation system is able to provide satisfactory protection for both structural and non-structural elements of the system over a wide range of hazard dynamic loadings.
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Experimental Investigation of a Base Isolation System Incorporating MR Dampers with the High-Order Single Step Control Algorithm
TL;DR: In this article, a structural vibration control system where the base isolation system is composed of rubber bearings with magnetorheological (MR) damper and are regulated using the innovative control strategy is presented.
Frequency control of smart base isolation system employing a novel adaptive magneto-rheological elastomer base isolator:
TL;DR: In the past decades, base isolation techniques have become increasingly popular for seismic protection of civil structures owing to its capability of decoupling buildings from harmful ground motion as discussed by the authors, which can decouple buildings from dangerous ground motion.
Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study
TL;DR: In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure.
A new evolving compact optimised Takagi–Sugeno fuzzy model and its application to nonlinear system identification
Mohsen Askari,Amir H.D. Markazi +1 more
TL;DR: It is shown that the developed evolving Takagi–Sugeno (T–S) fuzzy model can identify and grasp the nonlinear dynamics of both systems very well, while a small number of inputs and fuzzy rules are required for this purpose.
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