Probabilistic demand model and performance-based fragility estimates for RC column subject to vehicle collision Academic Article uri icon

abstract

  • Vehicle collision is one of the most likely causes of structural failure. The infrastructure facilities, service industry, housing and office spaces, power distribution facilities have increased and spread at a high pace. The mechanical and human interactions also involve errors from both sides. As such, the number of collision of vehicles with structures has also risen over the years. As a result, vehicle collision with structures has become a critical problem and one of the leading causes of structural failure. Bridge columns, building columns, and electric poles are often made of reinforced concrete (RC). Therefore, RC columns have to be properly designed accounting for vehicle impact loading. The current bridge code AASHTO-LRFD (2007) [1] provisions assume a constant value for the shear force demand on a column subject to vehicle impact. However, the actual shear force demand imposed on an RC column is typically larger than the AASHTO-LRFD prediction and is not a constant value but rather depends on a number of variables including the vehicle velocity and mass.This paper develops a framework for the performance-based analysis and design of RC columns subject to vehicle impact. A probabilistic model is proposed to accurately estimate the dynamic shear force demand on the RC columns subject to vehicle impact. In addition, a framework to estimate the fragility of the RC column subject to a vehicle collision is also developed. The proposed framework makes use of the developed probabilistic demand model. This work can be used to develop load and resistance factors for the bridge system and help achieve the goal of a reliability-based design. The developed model can be used to design safer columns and the work can be extended to other structures under similar loading conditions. © 2014 Elsevier Ltd.

altmetric score

  • 0.5

author list (cited authors)

  • Sharma, H., Gardoni, P., & Hurlebaus, S.

citation count

  • 35

publication date

  • September 2014