Parametric sensitivity analysis of critical factors for progressive collapse of base-isolated buildings

Progressive collapse resistance design necessitates the selection of critical structural characteristic parameters, which provide directional guidance for constructing collapse resistance optimisation models. Seismic isolation structures are extensively utilised in public buildings. Consequently, their robustness requirements against progressive collapse by chance action have become heightened. This study proposes a screening framework for the critical factors in the progressive collapse of base-isolated buildings based on the theory of global sensitivity analysis. The magnitude of the effects of different input parameters on the progressive collapse performance of seismic isolation structures was investigated. The parameters used to perform the sensitivity analysis included five structural layer parameters ( DL , LL , f c,cover , f c,core , and f y ) and two seismic isolation layer parameters ( Q d and K h ). The findings of this study demonstrate that in the case of lateral progressive collapse, the horizontal stiffness of the isolation layer assumes primacy as a control index; the live load emerges as the predominant control index in the context of vertical progressive collapse. Notably, marginal discrepancies exist in the criticality rankings obtained through diverse sensitivity analysis methods. However, the primary control factors and the initial four critical factors screened demonstrated consistency. The parameter sensitivity ranking in this study can be used as a reference for selecting characteristic parameters in the optimal design of structural collapse resistance and intelligent collapse prediction. • Novel framework pinpoints key factors in structural progressive collapse. • Key parameters' impact on isolated structures' collapse resistance quantified. • Shaking table tests validate base-isolated building collapse performance. • Three sensitivity methods demonstrate consistent analytical outcomes. • Findings inform parameter optimisation for collapse-resistant structural designs.