Probabilistic failure analysis of reinforced concrete beam-column sub-assemblage under column removal scenario
Source abstract
Abstract
Abstract Column removal failure is a common failure type of reinforced concrete (RC) structures subjected to progressive collapse. Thus, it is widely adopted to use the experimental/numerical column removal scenario to assess the progressive collapse capacity of RC structures. However, most of existing studies neglect the uncertainties involved in the structures, e.g., geometric size and material properties, while it may have great influence on the overall behavior of the structure under progressive collapse. In this paper, a probabilistic analysis of RC beam-column sub-assemblage under column removal scenario is performed. An efficient numerical model based on the finite element software OpenSEES is firstly developed for progressive collapse analysis of RC structures. The model adopts fiber beam-column element with co-rotational formulation to simulate the extreme behavior of frame members under column removal scenario, and uses a Min-Max material to apply the material failure criterion. Then the probability density evolution method (PDEM) is employed to conduct the probabilistic failure analysis. Two benchmark column removal tests of RC sub-assemblages are selected as the numerical case study examples. The deterministic analysis results indicates that the proposed finite element model is effective to reproduce the typical behavior of the sub-assemblages, while the probabilistic analysis provides mean values, standard variations and probability density functions (PDFs) of the whole progressive collapse process. Finally, the influence (or sensitivity) of the uncertain parameters on the global and local behaviors of RC beam-column sub-assemblage under column removal are quantified.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Structural Response to Dynamic Loads · Structural Behavior of Reinforced Concrete · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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