54,025 research outputs found
Meta-models for structural reliability and uncertainty quantification
A meta-model (or a surrogate model) is the modern name for what was
traditionally called a response surface. It is intended to mimic the behaviour
of a computational model M (e.g. a finite element model in mechanics) while
being inexpensive to evaluate, in contrast to the original model which may take
hours or even days of computer processing time. In this paper various types of
meta-models that have been used in the last decade in the context of structural
reliability are reviewed. More specifically classical polynomial response
surfaces, polynomial chaos expansions and kriging are addressed. It is shown
how the need for error estimates and adaptivity in their construction has
brought this type of approaches to a high level of efficiency. A new technique
that solves the problem of the potential biasedness in the estimation of a
probability of failure through the use of meta-models is finally presented.Comment: Keynote lecture Fifth Asian-Pacific Symposium on Structural
Reliability and its Applications (5th APSSRA) May 2012, Singapor
Efficient method for probabilistic fire safety engineering
A growing interest exists within the fire safety community for the topics of risk and reliability. However, due to the high computational requirements of most calculation models, traditional Monte Carlo methods are in general too time consuming for practical applications. In this paper a computationally very efficient methodology is for the first time applied to structural fire safety. The methodology allows estimating the probability density function which describes the uncertain response of the fire exposed structure or structural member, while requiring only a very limited number of model evaluations. The application of the method to structural fire safety is illustrated by two examples in the area of concrete elements exposed to fire
Quantile-based optimization under uncertainties using adaptive Kriging surrogate models
Uncertainties are inherent to real-world systems. Taking them into account is
crucial in industrial design problems and this might be achieved through
reliability-based design optimization (RBDO) techniques. In this paper, we
propose a quantile-based approach to solve RBDO problems. We first transform
the safety constraints usually formulated as admissible probabilities of
failure into constraints on quantiles of the performance criteria. In this
formulation, the quantile level controls the degree of conservatism of the
design. Starting with the premise that industrial applications often involve
high-fidelity and time-consuming computational models, the proposed approach
makes use of Kriging surrogate models (a.k.a. Gaussian process modeling).
Thanks to the Kriging variance (a measure of the local accuracy of the
surrogate), we derive a procedure with two stages of enrichment of the design
of computer experiments (DoE) used to construct the surrogate model. The first
stage globally reduces the Kriging epistemic uncertainty and adds points in the
vicinity of the limit-state surfaces describing the system performance to be
attained. The second stage locally checks, and if necessary, improves the
accuracy of the quantiles estimated along the optimization iterations.
Applications to three analytical examples and to the optimal design of a car
body subsystem (minimal mass under mechanical safety constraints) show the
accuracy and the remarkable efficiency brought by the proposed procedure
Reliability assessment of cutting tool life based on surrogate approximation methods
A novel reliability estimation approach to the cutting tools based on advanced approximation methods is proposed. Methods such as the stochastic response surface and surrogate modeling are tested, starting from a few sample points obtained through fundamental experiments and extending them to models able to estimate the tool wear as a function of the key process parameters. Subsequently, different reliability analysis methods are employed such as Monte Carlo simulations and first- and second-order reliability methods. In the present study, these reliability analysis methods are assessed for estimating the reliability of cutting tools. The results show that the proposed method is an efficient method for assessing the reliability of the cutting tool based on the minimum number of experimental results. Experimental verification for the case of high-speed turning confirms the findings of the present study for cutting tools under flank wear
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