The model correction factor method (MCFM) is used in conjunction with the first-order reliability method (FORM) to solve structural reliability problems involving integrals of non-Gaussian random fields. The approach replaces the limit-state function with an idealized one, in which the integrals are considered to be Gaussian. Conventional FORM analysis yields the linearization point of the idealized limit-state surface. A model correction factor is then introduced to push the idealized limit-state surface onto the actual limit-state surface. A few iterations yield a good approximation of the reliability index for the original problem. This method has application to many civil engineering problems that involve random fields of material properties or loads. An application to reliability analysis of foundation piles illustrates the proposed method. (C) 2002 Elsevier Science Ltd. All rights reserved.
Model correction factor method for reliability problems involving integrals of non-Gaussian random fields / Franchin, Paolo; Ove, Ditlevsen; Armen Der, Kiureghian. - In: PROBABILISTIC ENGINEERING MECHANICS. - ISSN 0266-8920. - STAMPA. - 17:2(2002), pp. 109-122. [10.1016/s0266-8920(01)00031-5]
Model correction factor method for reliability problems involving integrals of non-Gaussian random fields
FRANCHIN, Paolo;
2002
Abstract
The model correction factor method (MCFM) is used in conjunction with the first-order reliability method (FORM) to solve structural reliability problems involving integrals of non-Gaussian random fields. The approach replaces the limit-state function with an idealized one, in which the integrals are considered to be Gaussian. Conventional FORM analysis yields the linearization point of the idealized limit-state surface. A model correction factor is then introduced to push the idealized limit-state surface onto the actual limit-state surface. A few iterations yield a good approximation of the reliability index for the original problem. This method has application to many civil engineering problems that involve random fields of material properties or loads. An application to reliability analysis of foundation piles illustrates the proposed method. (C) 2002 Elsevier Science Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.