The statistical interpretation of the strength of float glass for structural applications

Pisano, Gabriele; Royer-Carfagni, Gianni

doi:10.1016/j.conbuildmat.2015.08.073

Cited by 40 publications

(34 citation statements)

References 20 publications

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“…For each type of glass, ten 300 mm 9 300 mm 9 6 mm specimens were tested with the tin side under tension, while the residual compression stresses from the heat treatment were measured by SCALP-04 and SCALP-05 devices. For the pristine annealed glass strength, the authors record a value of the 50% fractile of the order of 101 MPa, indeed a value much lower than the one (161 MPa) that can be calculated from the data analyzed in a previous work, 22 corresponding to a standard commercial glass. We do not know if this datum has been erroneously reported in Zaccaria and Overend, 38 but in any case, although the average of the surface residual stresses for the fully tempered glass was 117 MPa with standard deviation 7 MPa, the mean failure stress value was found to be 288 MPa, a value much higher than what should be expected from a formula of the type (7).…”

Section: Zaccaria and Overendmentioning

confidence: 64%

“…ie, m =7.3 and

{normalη}_{0} = 406

Mpa mm

^{2 false/ m}

. On the other hand, the dashed and the dotted curves are associated with the experimental data from the campaign by the CEN TC129/WG8 group, for which the statistical parameters were estimated in both graphically ( m =6.46 and

{normalη}_{0} = 419.6

Mpa mm

^{2 false/ m}

) and analytically through the Maximum Likelihood Estimation (MLE) method ( m =5.62 and

{normalη}_{0} = 589

Mpa mm

^{2 false/ m}

). Such parameters take into account the effective area

A_{eff}

of the specimen according to the expressions (3) and (4).…”

Section: Comparison With Experimentsmentioning

confidence: 99%

“…25 Alternatively, Schula et al 26 suggested the use of the log-normal distribution. Questions have been raised about the existence of a threshold for the glass strength, 22,27 presenting arguments in support of this conjecture. 28 Following this rationale, the use of a threeparameter 22,27 or left-truncated Weibull distributions 28 have been indicated as a possibility.…”

Section: The Assumed Statistical Distributionsmentioning

confidence: 99%

“…During last years, researchers have questioned about its applicability . In particular, we have shown that this statistical model fails to interpret the left‐hand‐side tail of the strength distribution. In any case, the two‐parameter Weibull distribution provides results on the safe side, and it is rather accurate if one is not interested in extremely low probabilities of failure, but rather in estimating the values corresponding to the 50% and 5% fractiles.…”

Section: Introductionmentioning

confidence: 99%

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Statistical interference of material strength and surface prestress in heat‐treated glass

Pisano

Royer-Carfagni

2016

J Am Ceram Soc

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It is customary to assume that the characteristic design value for heat‐treated glass is represented by the sum of the characteristic values of the annealed glass strength and of the heat‐induced surface prestress, even though experiments have provided evidence that the resulting strength may be much higher. Here, we investigate the statistical interference between an assumed two‐parameter Weibull distribution for the annealed glass strength and a Gaussian distribution for the surface prestress. We show how the compound distribution confirms the experimental findings and, in particular, that the type of stress induced by the applied loads, ie, uniaxial vs biaxial, has an important role. This effect, which is more relevant for “weak” than for “strong” glasses, is the mechanical counterpart of the well‐known principle of diversification of investments in economy, and leads to a critical consideration of the design approach commonly suggested by structural standards.

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Section: Zaccaria and Overendmentioning

confidence: 64%

“…ie, m =7.3 and

{normalη}_{0} = 406

Mpa mm

^{2 false/ m}

{normalη}_{0} = 419.6

Mpa mm

^{2 false/ m}

) and analytically through the Maximum Likelihood Estimation (MLE) method ( m =5.62 and

{normalη}_{0} = 589

Mpa mm

^{2 false/ m}

). Such parameters take into account the effective area

A_{eff}

of the specimen according to the expressions (3) and (4).…”

Section: Comparison With Experimentsmentioning

confidence: 99%

Section: The Assumed Statistical Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical interference of material strength and surface prestress in heat‐treated glass

Pisano

Royer-Carfagni

2016

J Am Ceram Soc

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“…The probability density function for the action is given by Equation (15). Since two concurrent actions exist, from Equations (16) and (20), the verification formula for the case at hand can be expressed in the form with k mod,per = 0.26, k mod,s = 0.39, f g,k = 45 MPa, n = 16, and A = 1.36. Then, the characteristic value of the snow load q s is obtained from the expression recorded in Ref.…”

Section: Snow Loadmentioning

confidence: 99%

Partial safety factors for laminated glass

2019

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Glass plies can be bonded together by polymeric interlayers to form laminated glass. Thanks to the capacity in finding alternative stress paths after partial breakage, it is commonly accepted that the more the layers are, the higher the safety level is. However, a negative aspect is that the tensile strength of glass carries a size effect in terms of surface area, which increases with lamination. Here we evaluate these competing factors by calibrating the partial safety factors to be used in semi‐probabilistic (level‐I) design, through comparison with probabilistic (level‐III) methods in paradigmatic case‐studies under wind, snow, and dead weight. Starting from a two‐parameter Weibull distribution for glass strength, the “failure modes” approach determines the statistical distribution of strength for a multi‐laminate as a function of the number of plies, which interferes with the statistics for actions. As a function of the target probability of failure for the assigned class of consequence, we introduce a new coefficient in the verification formula of level‐I, to account for the effects of lamination with dependence upon the number of plies. We find that there is strong gain when passing from a monolith to a two‐ply laminate, but the advantage fades by increasing the number of layers. Verification formulas of this type could avoid overconservative design.

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A novel industrial process situation awareness model based on multi‐time scale dynamic feature fusion with applications to float glass manufacturing

Peng,

2024

Can J Chem Eng

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Modern industrial manufacturing processes often focus on the changes of performance indicators and the evolution of operation conditions. However, the dynamics of variables are vague within processes and even more difficult to be characterised across different processes. The time‐series dynamic characteristics of the different scales among variables, processes, and production cycles develop and transfer with changes in parameters, equipment, and processes. It is difficult to accurately show the quality index and operating condition trend at the same time. To solve these problems, a situation awareness (SA) framework integrating multi‐time scale dynamic features for manufacturing processes is proposed. First, data are condensed and reconstructed through the denoising long short term memory autoencoder to reveal the time series dynamic characteristics to get neighbourhood features which contain features among neighbourhood samples. Second, the neighbourhood features divided into window blocks are fused into the stage features by statistical analysis. Finally, a multi‐scale isometric convolution network is designed to extract the local and global features, which can effectively show the development of dynamic features on a long time scale, and profoundly describe the influence of full cycle features on variables and operating conditions. The proposed model is verified on the real data set of a float glass manufacturing process, and the SA model can well predict the future trend of long time series.

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The statistical interpretation of the strength of float glass for structural applications

Cited by 40 publications

References 20 publications

Statistical interference of material strength and surface prestress in heat‐treated glass

Statistical interference of material strength and surface prestress in heat‐treated glass

Partial safety factors for laminated glass

A novel industrial process situation awareness model based on multi‐time scale dynamic feature fusion with applications to float glass manufacturing

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