Modelling Vehicular Impact for Threat-Dependent Progressive Collapse Assessments
Year: 2025
Type: conference-paper
Abstract: Structural robustness is vital to prevent disproportionate damage in a progressive collapse of a structure. Current assessment methods in building structures, such as the notional removal of structural members, are unable to capture cascading effects from debris impact loading and chained hazard scenarios. Vehicular impact with penetration into the building volume is an example of a plausible chained hazard scenario. In this study, the adequacy of notional damage methods was assessed for vehicular impact loading on building structures and a comprehensive framework was developed to address key limitations in conventional design methods against vehicular impact. The conventional approaches have relied on prescriptive forces and simplified models and have over-looked important phenomena, such as variable vehicle stiffness and energy dissipation mechanisms. The use of the vehicular impact framework has been exemplified using a model for progressive collapse to assess the consequences of successive time-delayed column loss on the structural response from the penetration of a lorry. The time-delay of successive column loss from vehicular impact was determined by considering the kinetic energy and energy dissipation mechanisms. Higher initial velocities lead to higher post-impact velocities after the initial impact on a column and to a reduction in time between successive failures, thus heightening the likelihood of cascading failures in structural columns and progressive collapse. Progressive collapse simulations revealed that a building structure may have sufficient robustness to prevent progressive collapse when subjected to the loss of single columns. However, successive column losses triggered an extensive progressive collapse.
DOI: 10.4028/p-o4q45b
Institution: Institute of Structural Engineering
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4417450993