“…Such a feature could help to characterize the uncertainties that could not be captured by a probabilistic approach. In this paper, robust assessment of buried pipelines is proposed based on fuzzy alpha-level set, interval analysis and evaluation of fuzziness using an analogy to Shannon's entropy as defined in [4,10,13], and [19]. According to the authors' understanding, there is no such work found in the literature on robust assessment of buried pipeline using fuzzy alpha-level sets.…”
Section: Introductionmentioning
confidence: 85%
“…Concrete pipes are made from materials composed of cement, water, sharp sand and stone. The damage of concrete pipe are often caused by biogenous sulphuric acid attack [27], while plastic pipes are not. According to [14] and [17], corrosion pit depth of a metal pipe can be modelled as a time function as in eqn (13).…”
Section: Application To Buried Pipesmentioning
confidence: 99%
“…This entropy measurement equation evaluates the 'steepness' of the membership function μ x of . H 0 for a crisp set and H is maximum if μ x 0.5 [27]. Following the approach proposed by [13], entropy based robustness measure can be assessed at various membership levels with respect to the degree of imprecision in the fuzzy inputs and the associated imprecision in the fuzzy outputs.…”
This paper presents the study of the negative effects of corrosion on the robustness of buried pipeline based on deflection and bending strain failure modes. The fuzzy variable is used in the assessment to take into account the subjective character of the corrosion-induced failure. The approach gains its efficiency by scrutinizing the structural robustness at every membership level with respect to various degrees of imprecision. It is shown that the use of alpha-level discretization in the assessment of an entropy based robustness measure could produce credible results with better understanding of uncertainties associated with the failure of buried pipelines.
“…Such a feature could help to characterize the uncertainties that could not be captured by a probabilistic approach. In this paper, robust assessment of buried pipelines is proposed based on fuzzy alpha-level set, interval analysis and evaluation of fuzziness using an analogy to Shannon's entropy as defined in [4,10,13], and [19]. According to the authors' understanding, there is no such work found in the literature on robust assessment of buried pipeline using fuzzy alpha-level sets.…”
Section: Introductionmentioning
confidence: 85%
“…Concrete pipes are made from materials composed of cement, water, sharp sand and stone. The damage of concrete pipe are often caused by biogenous sulphuric acid attack [27], while plastic pipes are not. According to [14] and [17], corrosion pit depth of a metal pipe can be modelled as a time function as in eqn (13).…”
Section: Application To Buried Pipesmentioning
confidence: 99%
“…This entropy measurement equation evaluates the 'steepness' of the membership function μ x of . H 0 for a crisp set and H is maximum if μ x 0.5 [27]. Following the approach proposed by [13], entropy based robustness measure can be assessed at various membership levels with respect to the degree of imprecision in the fuzzy inputs and the associated imprecision in the fuzzy outputs.…”
This paper presents the study of the negative effects of corrosion on the robustness of buried pipeline based on deflection and bending strain failure modes. The fuzzy variable is used in the assessment to take into account the subjective character of the corrosion-induced failure. The approach gains its efficiency by scrutinizing the structural robustness at every membership level with respect to various degrees of imprecision. It is shown that the use of alpha-level discretization in the assessment of an entropy based robustness measure could produce credible results with better understanding of uncertainties associated with the failure of buried pipelines.
“…Let U obs (s, δ) be the random observations with which the parameter δ will be identified. There exists several methodologies adapted to the identification of stochastic computational models, see for instance [46,47,48]. In the present research, the identification procedure is achieved using the maximum likelihood method associated with a statistical reduction of the information [49].…”
Section: Identification Of the Stochastic Nonlinear Static Computatiomentioning
The paper presents a complete experimental validation of an advanced computational methodology adapted to the nonlinear post-buckling analysis of geometrically nonlinear structures in presence of uncertainty. A mean nonlinear reduced-order computational model is first obtained using an adapted projection basis. The stochastic nonlinear computational model is then constructed as a function of a scalar dispersion parameter, which has to be identified with respect to the nonlinear static experimental response of a very thin cylindrical shell submitted to a static shear load. The identified stochastic computational model is finally used for predicting the nonlinear dynamical post-buckling behavior of the structure submitted to a stochastic ground motion.
“…In other approaches, admissible design domains are identified with the aid of cluster analysis and fuzzy set theory (see [3,22]). Nevertheless, fuzzy set theory needs additional information like the membership function of the parameters which is typically not available in the engineering design development.…”
Section: Motivation and Problem Statementmentioning
Abstract. A stochastic algorithm that computes box-shaped solution spaces for nonlinear, high-dimensional and noisy problems with uncertain input parameters has been proposed in [35]. This paper studies in detail the quality of the results and the e ciency of the algorithm. Appropriate benchmark problems are specified and compared with exact solutions that were derived analytically. The speed of convergence decreases as the number of dimensions increases. Relevant mechanisms are identified that explain how the number of dimensions a↵ects the performance. The optimal number of sample points per iteration is determined in dependence of the preference for fast convergence or a large volume.
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