Probabilistic modelling of the robustness of reinforced concrete frames accounting for material property variability using a layered beam finite element approach

Abstract

In this contribution the influence of material properties variability on the structural robustness of Reinforced Concrete (RC) frames is assessed using a computational approach. A layered beam finite element model is employed in a correlation reduced Latin Hypercube Sampling-based stochastic framework in which the material parameters driving the elastic, plastic and fracture behavior of both steel and concrete constituents are considered to be Random Variables (RVs). The probability characteristics of each RV are set to match relevant experimental data available in the literature. Structural robustness of each deterministic simulation is evaluated based on the outcome of a nonlinear dynamic progressive collapse computation using the Sudden Column Loss (SCL) scenario. The sensitivity of structural robustness and residual displacement to each material parameter is established first, considering their variations separately. The robustness of a RC frame designed following the European building code, incorporating all RVs simultaneously is assessed and critically compared to the results of the sensitivity analysis. The link between the structural degradation and failure mechanism and data extracted from the cross sectional behavior is established. The identification of structural mechanisms inducing failure or survival is attempted and a set of practically relevant recommendations for progressive collapse resistance is listed.

Probabilistic investigation of reinforced concrete structures under fire loading accounting for material and geometric uncertainties

“…The constitutive relationships of concrete and steel in this work (Figure 3) were shown to perform well in Berke and Massart (2018), Iribarren et al (2011), Oliveira et al (2014, Sosso et al (2020) for the RC structures and specifically in Sosso et al (2021) for the stuctures considered here. At each layer 1D strain rate independent elasto-plastic constitutive models are used for both materials, with an ultimate strain limit, ϵ u , used to detect layer fracture (no stresses are developed in the material once this limit is reached).…”

“…Therefore LHS with correlation reduction (Olsson et al (2003); Olsson and Sandberg (2002)) was used in this work, known to significantly reduce the number of simulations, resulting in more affordable and still accurate results (De Souza et al (2019); Gernay et al (2016)). The considered RVs include material properties driving the elasto-plastic behavior and fracture of the constituents, as in Sosso et al (2020), completed with geometrical parameters, resulting in a total of 12 RVs.…”

“…For this study, the low cost thermo-mechanical multilayered beam computational tool developed in Sosso et al (2021) is used in a stochastic framework in an approach very similar to Sosso et al (2020) to investigate the influence of material and geometrical model parameters variability on the structural response of RC structures subjected to fire. To ease readability, the main concepts of the computational methodology and the parameters of the constitutive models are recalled first.…”

“…Monitoring and detailed analysis of the cross section state (i.e. stress and strain distribution, material degradation) is scarcely done, possibly because of the burden of performing such a detailed analysis in a stochastic approach based on a large number of simulations (Sosso et al (2020)). Note that such knowledge is particularly useful for the evaluation of the post-fire safety level of a structure, being an outlook of the present work.…”