• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2000.10a
• /
• pp.32-39
• /
• 2000

Probabilistic structural design optimization, which is characterized by the so-called probabilistic. constraints which introduce permissible probability of violation, is preferred to deterministic design optimization since unpredictable inherent uncertainties and randomness in structural and environmental properties are to be taken quantitatively into account by probabilistic design optimization. In this paper, the well-known reliability index based MPFP(Most Probable Failure Point) search approach and the newly introduced target performance measure based MPTP(Minimum Performance Target Point) search approach are summarized and compared. The present comparison focuses on the number of iterations required for the estimation of probabilistic constraints and a technique for improvement which removes exhaustive iterations is presented as well. A 10 bar truss problem is examined for this.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.3
• /
• pp.217-224
• /
• 2024

This paper proposes an efficient dimension reduction method (DRM) that considers the nonlinearity of the performance functions in reliability-based design optimization (RBDO). The dimension reduction method evaluates the reliability more accurately than the first-order reliability method (FORM) using integration quadrature points and weights. However, its efficiency is hindered as the number of quadrature points increases owing to the need for an additional evaluation of the performance function. In this study, we assessed the nonlinearity of the performance function in RBDO and proposed criteria for determining the number of quadrature points based on the degree of nonlinearity. This approach suggests adjusting the number of quadrature points during each iteration of the RBDO process while maintaining the accuracy of theDRM while improving the computational efficiency. The nonlinearity of the performance function was evaluated using the angle between the vectors used in the maximum probable target point (MPTP) search. Numerical tests were conducted to determine the appropriate number of quadrature points according to the degree of nonlinearity. Through a 2D numerical example, it is confirmed that the proposed method improves the efficiency while maintaining the accuracy of the dimension reduction method or Monte Carlo Simulation (MCS).