American Journal of Engineering and Applied Sciences

Prediction of Residual Axial Load Carrying Capacity of Reinforced Concrete (RC) Columns Subjected to Extreme Dynamic Loads

Masoud Abedini, Azrul A. Mutalib, Sudharshan N. Raman, Shahrizan Baharom and J. Sima Nouri

DOI : 10.3844/ajeassp.2017.431.448

American Journal of Engineering and Applied Sciences

Volume 10, Issue 2

Pages 431-448

Abstract

The evaluation of residual axial load carrying capacity of Reinforced Concrete (RC) columns to explosion load is significant for protection of buildings. The few investigations conducted on residual axial load carrying capacity of RC columns when subjected to blast loads. Therefore, the overall aim of this research is to generate equations on the blast capacity of axially and uniaxial loaded columns. In this study, an advanced nonlinear model is developed to study the residual axial load carrying capacity (Presidual) of RC columns to explosion loads using Arbitary Lagrangian Eulerian (ALE) finite element technique in LS-DYNA 971. The ALE model represents the actual blast incident scenario and is validated with experimental study reported in the previous research. In order to derive the Presidual empirical equations, intensive parametric studies are carried out to investigate the effects of column depth (d), longitudinal reinforcement ratio (ρ), transverse reinforcement ratio (ρs), yield stress of longitudinal steel (fy), yield stress of transverse steel (fyt), column height (H), column width (w) and concrete strength (fc) on the residual axial capacity of RC columns. Based on numerical simulation data, nine empirical relations are suggested to predict residual axial capacity of RC columns. The validated equations can be used for quick assessment of existing RC columns when blast loading is required to be considered especially to evaluate the blast resistant capacity of a critical building such as military buildings, government assets and etc.

Copyright

© 2017 Masoud Abedini, Azrul A. Mutalib, Sudharshan N. Raman, Shahrizan Baharom and J. Sima Nouri. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.