A parametric computational study of rc building structures under corner-column removal situations

Copyright © 2020 by the authors. Building progressive collapse is currently one of the hottest topics in the structural engineering field. Most of the research carried out to date on this topic has been focused on the structural analysis of the failure of one or more columns in a building to determine the Alternative Load Paths (ALPs) the structure can activate. Past research was mainly focused on extreme situations with high loads and large structural deformations and, to a lesser extent, research looked at lower loads used in design accidental situations, which requires a different set of assumptions in the analysis. This paper describes a study aimed at analysing accidental design situations in corner-column removal scenarios in reinforced concrete (RC) building structures and evaluating the available real ALPs in order to establish practical recommendations for design situations that could be taken into account in future design codes. A wide parametric computational analysis was carried out with advanced Finite Element (FE) models which the authors validated by full-scale tests on a purpose-built building structure. The findings allowed us to: (i) establish design recommendations, (ii) demonstrate the importance of Vierendeel action and (iii) recommend Dynamic Amplification Factors (DAFs) for design situations.

This research was funded by Fundación BBVA—Becas Leonardo a Investigadores y Creadores Culturales 2017; the Spanish Ministry of Economy, Industry and Competitiveness, grant number BIA2017-88322-R-AR; Generalitat Valenciana/Fons Social Europeu, grant number APOSTD/2019/101 and Universitat Politècnica de València, grant number PAID-10-17. This work is also part of the project “Extension of theoretical models against progressive collapse for tall and supertall concrete buildings”, funded by the Engineering Physical Science Research Council (EPSRC) of the UK as part of an Impact Acceleration Account (IAA) held at the University of Surrey (grant number EP/K008153/1), and a continuation of two research projects also funded by EPSRC of the UK (grant ref: EP/K503939 and grant ref: EP/K008153/1).