Department of Civil Engineering,Shahid Bahonar University of Kerman
Abstract
Abstract. An ecient methodology is proposed to optimize space trusses considering geometric
nonlinearity. The optimization task is performed by a continuous Particle Swarm Optimization (PSO).
Design variables are cross sectional areas of the trusses and their weights are also taken as the objective
function. Design constraints are de ned to restrict nodal displacements and element stresses and
prevent the overall elastic instability of the structures during the optimization procedure. In order to
reduce the computational e ort of the optimization process, an Adaptive Neuro Fuzzy Inference System
(ANFIS) is employed to approximate the nonlinear analysis of the structures instead of performing
via a time consuming Finite Element Analysis (FEA). The presented ANFIS is compared with a
Back Propagation Neural Network (BPNN) and appears to produce a better performance generality for
evaluating structure design values. Test example results demonstrate the computational advantages of the
suggested methodology for optimum design of geometrically nonlinear space trusses.
Salajegheh, E., Salajegheh, J., Seyedpoor, S., & Khatibinia, M. (2009). Optimal Design of Geometrically Nonlinear Space Trusses Using an Adaptive Neuro-Fuzzy Inference System. Scientia Iranica, 16(5), -.
MLA
E. Salajegheh; J. Salajegheh; S.M. Seyedpoor; M. Khatibinia. "Optimal Design of Geometrically Nonlinear Space Trusses Using an Adaptive Neuro-Fuzzy Inference System". Scientia Iranica, 16, 5, 2009, -.
HARVARD
Salajegheh, E., Salajegheh, J., Seyedpoor, S., Khatibinia, M. (2009). 'Optimal Design of Geometrically Nonlinear Space Trusses Using an Adaptive Neuro-Fuzzy Inference System', Scientia Iranica, 16(5), pp. -.
VANCOUVER
Salajegheh, E., Salajegheh, J., Seyedpoor, S., Khatibinia, M. Optimal Design of Geometrically Nonlinear Space Trusses Using an Adaptive Neuro-Fuzzy Inference System. Scientia Iranica, 2009; 16(5): -.
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