Nonlinear Dynamic Analysis of Structures Using Cellular Automata

Abstract eng:
Emerging idea of Parallel Computing makes the use of new algorithms inevitable to deal with the problem of structural optimization. Simultaneous analysis and design has been a well known optimization method whose efficiency can be increased by the use of parallel computation methods. Cellular Automata are among those algorithms which their models are composed of regular lattice of cell or automata. Each cell may change its state at discrete, fixed times (iterations) according to a local rule based on behavior of the system which should be modeled. So the behavior of a system can be modeled by simple interaction of cells. This study contributes to nonlinear dynamic analysis of multi degrees of freedom (MDF) structures by Cellular Automata. The proposed method establishes local update rules which act on the stories of the structures as system cells. Any of the stories interacts with its neighbors, while applying a ground motion record to the structure base. This leads to the global dynamic response of structure after some iteration. The analysis update rules tend to solve the global differential equation of motion by solving some local differential equations at the cell levels. An outstanding feature of the proposed method is that the analysis of the structure is performed by solving some simple equations corresponding to single degree of freedom (SDF) systems. This scheme of the method is astonishing, as previous studies on training artificial neural networks for optimizing dynamic behaviors of structures can be extended to nonlinear phase. Another aspect of Cellular Automata which makes them efficient computational tools is their capability to be utilized efficiently by parallel processors for modeling complex systems.

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