Linking Nonlinear System Identification with Nonlinear Dynamic Simulation under OpenSees: Some Justifications and Implementations

This study seeks to bridge the gap between nonlinear system identification and nonlinear dynamic finite-element analysis. Motivated by the needs in earthquake simulation, it is first investigated under which conditions and to what degree the prediction of maximum lateral drift and base shear require...

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Bibliographic Details
Published in:Journal of engineering mechanics 2009-11, Vol.135 (11), p.1213-1226
Main Authors: Piyawat, Krisda, Pei, Jin-Song
Format: Article
Language:English
Subjects:
Applied sciences
Building structure
Buildings. Public works
Computer simulation
Construction (buildings and works)
Drift
Dynamical systems
Earthquake engineering
Exact sciences and technology
Experimentation
Feedforward
Finite element method
Frames
Fundamental areas of phenomenology (including applications)
Grounds
Hysteresis
Implementation
Joining
Linking
Mathematical analysis
Mathematical models
Matlab
Measurement and testing methods
Metal structure
Methodology
Neural networks
Nonlinear dynamics
Nonlinear systems
Nonlinearity
Parametrization
Physics
Seismic phenomena
Shear
Simulation
Solid mechanics
Structural and continuum mechanics
Structural steels
TECHNICAL PAPERS
Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...)
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Summary:This study seeks to bridge the gap between nonlinear system identification and nonlinear dynamic finite-element analysis. Motivated by the needs in earthquake simulation, it is first investigated under which conditions and to what degree the prediction of maximum lateral drift and base shear requires accurate nonlinear hysteretic moment-rotation joint models. A series of simulations is carried out using a simple but typical steel frame under two different earthquake ground motion time histories scaled up to various levels. As one of the two major classes of models in nonlinear system identification, nonparametric models are proposed to be implemented into OpenSees. A methodology with details is provided to effectively implement feedforward neural networks with one hidden layer as a new one-dimensional nonlinear smooth material model directly from a MATLAB environment to OpenSees. The same methodology can be applied to benefit the implementation of other parametric and nonparametric models with linear parameterization. Numerical examples are provided. Challenges are discussed and future work is identified.
ISSN:0733-9399
1943-7889
DOI:10.1061/(ASCE)0733-9399(2009)135:11(1213)