The design of structures must satisfy a number of different requirements, such as cost, safety and
performance. The reliability of a structural system is often to be very sensitive to uncertainties
encountered in the material properties, manufacturing conditions or external loading conditions.
Therefore the assessment of uncertainty factors is a fundamental component of engineering design.
Reliability-based design optimization (RBDO) is concerned with designing an engineering system to
minimize a cost function subject to the reliability requirement that failure probability should not exceed
a required criteria. In this study, system reliability is estimated using Monte Carlo simulation (MCS)
which allows explicit consideration of nonlinear and non-differential of the limit state function.
Traditional double-loop methods use MCS to evaluate the reliability of RBDO solutions. The process is
highly inefficient as the number of components increases. Therefore, an efficient framework is
developed to combine Support Vector Machine (SVM), Particle Swarm Optimization (PSO) and
parallel programming. The interactive cooperation n between PSO ,SVM and parallel computing
contributes greatly to the success of the RBDO framework. It is shown in an illustrative example the
proposed framework showed great improvement in terms of computational efficiency, solution quality,
and model flexibility.
Key

The design of structures must satisfy a number of different requirements, such as cost, safety and
performance. The reliability of a structural system is often to be very sensitive to uncertainties
encountered in the material properties, manufacturing conditions or external loading conditions.
Therefore the assessment of uncertainty factors is a fundamental component of engineering design.
Reliability-based design optimization (RBDO) is concerned with designing an engineering system to
minimize a cost function subject to the reliability requirement that failure probability should not exceed
a required criteria. In this study, system reliability is estimated using Monte Carlo simulation (MCS)
which allows explicit consideration of nonlinear and non-differential of the limit state function.
Traditional double-loop methods use MCS to evaluate the reliability of RBDO solutions. The process is
highly inefficient as the number of components increases. Therefore, an efficient framework is
developed to combine Support Vector Machine (SVM), Particle Swarm Optimization (PSO) and
parallel programming. The interactive cooperation n between PSO ,SVM and parallel computing
contributes greatly to the success of the RBDO framework. It is shown in an illustrative example the
proposed framework showed great improvement in terms of computational efficiency, solution quality,
and model flexibility.
Key

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