Probabilistic Characterization of Glass under Different Type of Testing

Lamela, María Jesús; Ramos, Alberto; Pelayo, F.; Fernández‐Canteli, Alfonso; Przybilla, Constanze; Huerta, Consuelo; Pacios, A.

doi:10.1016/j.mspro.2014.06.341

Cited by 13 publications

(3 citation statements)

References 7 publications

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“…Based on Table 1, the major design issue for glass can be thus expected in the well-known, high natural variability of prestress and strength parameters for such a vulnerable constructional material. Several studies of literature (Nurhuda et al, 2010;Lamela et al, 2014;Veer et al, 2018;Pisano and Carfagni, 2017;Mognato et al, 2017;Kinsella et al, 2018) have been dedicated to the measurement and assessment of these nondeterministic and size-dependent material parameters, hence resulting in specific recommendations and limitation for structural design.…”

Section: Existing Design Methods For Glass Columnsmentioning

confidence: 99%

Uncertainty Assessment for the Buckling Analysis of Glass Columns with Random Parameters

Momeni¹,

Bedon²

2020

International Journal of Structural Glass and Advanced Material

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Load-bearing elements composed of glass, as known, are often susceptible to buckling collapse mechanisms. This intrinsic characteristic (and thus potential limitation for design) typically derives from the use of relatively small thicknesses to cover large spans and surfaces, thus resulting in a multitude of columns, beams, or plates that are characterized by high slenderness. In the literature, accordingly, several and design propositions support of the definition of efficient calculation models to capture the typical buckling response of glass elements variably shaped, sized, restrained and loaded. In this study, the attention is focused on the buckling analysis of glass columns and on the assessment of uncertainties due to input random parameters. With the support of finite element numerical models, a total of 800 glass columns are investigated, by accounting for stochastic variations in the geometry (size and thickness), modulus of elasticity and density of glass, maximum amplitude of the imposed initial imperfection, material type. Based on the Monte Carlo simulation method, the final result takes the form of 2400 simulations, where the postprocessing analysis is spent on the derivation of empirical formulations for the correlation of the relevant buckling capacity indicators. From the global out-of-plane bending analysis, the input random parameters are observed to affect severely both long and short columns with different flexural stiffness. Besides, a stable linear correlation is found for some influencing indicators. The attention is thus focused on the sensitivity analysis of critical buckling load, ultimate failure configuration, deflection at collapse, buckling reduction coefficient.

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Section: Existing Design Methods For Glass Columnsmentioning

confidence: 99%

Uncertainty Assessment for the Buckling Analysis of Glass Columns with Random Parameters

Momeni¹,

Bedon²

2020

International Journal of Structural Glass and Advanced Material

View full text Add to dashboard Cite

show abstract

“…For this reason, in case of large size fragile materials samples there is a higher scattering of experimental data in tensile strength measurement than in case of smaller samples [6,8,9]. This phenomena (sample volume contribution in data scattering) is shown also in case of high isotropic materials such as stainless steel (Fig.1).…”

Section: Introductionmentioning

confidence: 90%

“…The variability of a material characteristic it can be described by using Weibull distribution function [1] which cover a large scale of application, such as: fatigue performance [2,3,4], scatter of fracture toughness [5], probabilistic characterization in different static tests up to failure (four-point bending [6] or tensile test [7]). …”

Section: Introductionmentioning

confidence: 99%