LAUSR

This paper deals with the dynamic increase factor (DIF) to consider the dynamic effects in the nonlinear static analysis of RC frame buildings subjected to sudden loss of a first-storey column. The study applies nonlinear static and dynamic analyses and focuses on typical seismically designed reinforced concrete buildings. The analysis of these structures until failure requires considering both the geometric and material nonlinearities since the behaviour following sudden column loss is inelastic and possibly implicate catenary effects. Moreover, quantifying the robustness of this type of structure requires the implementation of detailed three-dimensional models. This paper investigates the effects of building properties including the number of storeys, the number of bays and the location of the removed column. The results show that the buildings designed for seismic loads reveal enough capacity to avoid the global collapse, both considering and neglecting the contribution of RC floor slab. The progressive collapse resistance is greater for the external column removal scenario than for the internal column removal scenario. Both the number of floors and the number of bays are not very sensitive parameters for the progressive collapse resistance. The nonlinear static approach leads to a conservative estimation of the collapse resistance when a DIF of 2 is used as the dynamic amplification factor. For the study cases, the response of the building subjected to column loss never involves the hardening phenomenon associated with the catenary action. In this situation, the DIF decreases monotonically with increasing vertical deflection. The lower bound value of DIF is very close to 1, which is the typical value of structures that fully develop their inelastic behaviour after column removal. The simulation results show that the floor slabs can greatly improve the progressive collapse resistance, but have a minor influence on the dynamic increase factor. Thus, the simplification of the problem into 3D bare frames can lead to an accurate estimation of DIF with less computationally intensive analyses.