Probabilistic Models for Temperature-Dependent Strength of Steel and Concrete

Qureshi, Ramla; Ni, Shuna; Khorasani, Negar Elhami; Coile, Ruben Van; Hopkin, Danny; Gernay, Thomas

doi:10.1061/(asce)st.1943-541x.0002621

Cited by 58 publications

(45 citation statements)

References 36 publications

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“…For that purpose, (i) the models have to be continuous functions across the appropriate temperature range, and (ii) there should be a closed-form function for each specific quantile value to facilitate implementation in the numerical simulations. The methodology for the model development is based on the one presented by Qureshi et al [3], but with few modifications that are highlighted in the further text.…”

Section: Methodsmentioning

confidence: 99%

“…It should be highlighted that even though the methodology described here is based on the work presented in [3] it has a couple of distinctive differences. First, the choice of adequate theoretical distribution is done more holistically, using the AIC for every data point based on the fitted curves for the governing parameters, instead of fitting the distribution for each bin separately, summing the AIC for each bin and then fitting a curve on the parameters.…”

Section: Methodsmentioning

confidence: 99%

“…As the probabilistic approach is much more developed in classical structural engineering, the stochastic description of a large number of input variables needed for the structural analysis is well defined. This involves the geometry [1], loads [2] and material strength at ambient and elevated temperatures [3]. However, concerning the heat transfer part of the calculation, there is not much information available on the stochastic description of needed inputs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic models for thermal properties of concrete

Jovanović¹,

Khorasani²,

Thienpont³

et al. 2020

Proceedings of the 11th International Conference on Structures in Fire (SiF2020)

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Thermal conductivity and specific heat of concrete are highly influential parameters for the heat transfer into the material during fire exposure. Reviewing the available literature has shown that there is a large scatter in the data for these thermal parameters. To quantify that uncertainty, novel probabilistic models for thermal conductivity and specific heat of concrete at elevated temperatures are developed.Analysis of available experimental data indicates that a temperature-dependent Gamma distribution can be recommended for both thermal properties. Closed-form equations for the temperature-dependent mean and standard deviation are derived. Thus, for both the thermal conductivity and the specific heat, a continuous probability distribution as a function of temperature is obtained, which can be easily implemented in numerical simulations. Using the example of the probabilistic analysis of a simply supported concrete slab exposed to the standard fire, the models are compared with the commonly used deterministic representation of the thermal properties. It is shown that the calculated probabilities of failure using the deterministic models are an order of magnitude lower and therefore unconservative. This analysis suggests that accounting for the uncertainty in thermal properties for concrete slabs can have a significant effect on evaluating the safety and therefore should not be ignored in cases of high importance.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic models for thermal properties of concrete

Jovanović¹,

Khorasani²,

Thienpont³

et al. 2020

Proceedings of the 11th International Conference on Structures in Fire (SiF2020)

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“…The variables kfc and kfy define the quantile of the retention factors at elevated temperature based on the strength retention models by Qureshi et al [20]. The model variables e, and Φ are the three eccentricities of the column, as listed in the JCSS probabilistic model code [21].…”

Section: Development Of Surrogate Modelmentioning

confidence: 99%

Generalized fragility curves for concrete columns exposed to fire through surrogate modelling

Chaudhary¹,

Jovanović²,

Gernay³

et al. 2020

Proceedings of the 11th International Conference on Structures in Fire (SiF2020)

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Common structural fire design relies on recommendations from design codes, or (a single or small set of) more advanced numerical analyses. When applying such procedures to the design of structures under normal loading conditions, adequate safety is ensured through calibrated safety factors and ample experience with structural failures. This is however not the case when considering accidental fire loading, where the stochasticity in the structural fire behaviour is rarely fully acknowledged. Therefore, a significant interest in the use of probabilistic approaches to evaluate structural fire performance, which take into account the uncertainty associated with model parameters, can be observed among researchers, with a special focus on the development of fragility curves. The calculation of fragility curves is, however, a laborious task, demanding huge computational expense, mainly attributed to the adoption of advanced calculation procedures and the need for a large number of model evaluations. The present study contributes to addressing the limitations imposed by these computational requirements through the development of surrogate models for fire exposed structural members. To achieve this, a framework for carrying out probabilistic studies of structures under fire through the use of surrogate modelling is presented. The framework is applied to a concrete column subjected to a standard fire and proves efficiency and accurateness for the selected simple example. Future studies will investigate the applicability of the framework to structural assemblies under physically-based fires.

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“…The two columns are assumed to be located at the ground floor of a five story building. Column A is subject to a gravity load from a tributary area of 7 m x 3.5 m while Column B is subject to a gravity load from a tributary area of 7 m x 7 m. The retention factor of concrete compressive strength at high temperatures, kc,T, follows a weibull distribution, while the retention factor of steel yield strength at high temperatures, ky,T, follows a lognormal distribution [20]. The parameters for the two distributions are temperature-dependent variables.…”

Section: Identification Of Probabilistic Parametersmentioning

confidence: 99%

Lifetime economically optimum position of steel reinforcement in a concrete column exposed to natural fire

Ni¹,

Coile²,

Khorasani³

et al. 2020

Proceedings of the 11th International Conference on Structures in Fire (SiF2020)

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This paper incorporates a probabilistic fire loss assessment method for reinforced concrete structures into a cost-benefit analysis to optimize a structural fire design. Economic losses in case of failure and survival of the structure are both quantified with, in the latter case, an estimate of the damage and repairs costs. As a case study, a cost-benefit optimization of the position of rebars in a concrete column is investigated. The column response in fire is evaluated using finite element simulations in SAFIR. Variations in cover thickness result in variations in failure probabilities and, for cases where no failure occurs, variations in repair costs due to heat penetration and residual out-of-plane deformation of the column. The optimum cover thickness is the one that offers the best trade-off between the various repair costs across the range of likely fire intensity levels. This optimum is sensitive to repair decisions such as the tolerance on the acceptable residual out-of-plane deformation after a fire. For the studied cases, the optimum cover thickness is smaller in slender columns than in stocky columns due to greater out-of-plane deformations in the former.

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Probabilistic Models for Temperature-Dependent Strength of Steel and Concrete

Cited by 58 publications

References 36 publications

Probabilistic models for thermal properties of concrete

Probabilistic models for thermal properties of concrete

Generalized fragility curves for concrete columns exposed to fire through surrogate modelling

Lifetime economically optimum position of steel reinforcement in a concrete column exposed to natural fire

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