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.

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