Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Zhenzhou Lu"'
Autor:
Zhenzhou Lu, Bofan Dong
Publikováno v:
Structural and Multidisciplinary Optimization. 63:2613-2635
In the presence of random and interval hybrid uncertainty (RI-HU), the safety degree of the structure system can be quantified by the upper and lower bounds of failure probability. However, there is a lack of efficient methods for estimating failure
Publikováno v:
Structural and Multidisciplinary Optimization. 61:1107-1121
Reliability measures the ability that the structure finishes its intended function without failures by taking uncertainties into account. Reliability sensitivity commonly is defined as the partial derivative of the failure probability with respect to
Publikováno v:
Structural and Multidisciplinary Optimization. 61:1589-1602
By the average absolute difference between the unconditional failure probability and the conditional one on fixing an input at its realization, the failure-probability-based-sensitivity (FP-S) is defined to quantify the effect of the fixed input on t
Publikováno v:
Structural and Multidisciplinary Optimization. 61:267-281
Local reliability sensitivity (RS) and global RS can provide useful information in reliability-based design optimization, but the algorithm for solving them is still a challenge, especially in case of small failure probability and high dimensionality
Publikováno v:
Structural and Multidisciplinary Optimization. 60:2325-2341
It is widely recognized that the active learning kriging (AK) combined with Monte Carlo simulation (AK-MCS) is a very efficient strategy for failure probability estimation. However, for the rare failure event, the AK-MCS would be time-consuming due t
Publikováno v:
Structural and Multidisciplinary Optimization. 60:1373-1388
The time-dependent failure probability function (TDFPF) is defined as a function of the time-dependent failure probability (TDFP) varying with the design parameters and the service time, and it is useful in the reliability-based design optimization f
Publikováno v:
Structural and Multidisciplinary Optimization. 60:1189-1207
Global sensitivity analysis (GSA) plays an important role to quantify the relative importance of uncertain parameters to the model response. However, performing quantitative GSA directly is still a challenging problem for complex models with dependen
Publikováno v:
Structural and Multidisciplinary Optimization. 59:2177-2187
Models with multivariate outputs are widely used for risk assessment and decision-making in practical applications. In this paper, multi-output support vector regression (M-SVR) is employed for global sensitivity analysis (GSA) with multivariate outp
Publikováno v:
Structural and Multidisciplinary Optimization. 59:229-247
Polynomial chaos expansion (PCE) has been proven to be a powerful tool for developing surrogate models in the field of uncertainty and global sensitivity analysis. The computational cost of classical PCE is unaffordable since the number of terms grow
Publikováno v:
Structural and Multidisciplinary Optimization. 59:263-278
Due to multiple implicit limit state functions needed to be surrogated, adaptive Kriging model for system reliability analysis with multiple failure modes meets a big challenge in accuracy and efficiency. In order to improve the accuracy of adaptive