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Computers and Concrete
  Volume 35, Number 1, January 2025 , pages 15-28
DOI: https://doi.org/10.12989/cac.2025.35.1.015
 


Failure probability of corroded RC members using a novel fiber beam-column element formulation
Behrouz Akbarian Reshvanlou, Kiarash Nasserasadi and Jamal Ahmadi

 
Abstract
    Over the past few years, there has been an increasing emphasis on assessing structures in harsh environments to guarantee their safety and longevity. Nevertheless, precisely modeling the behavior of corroded elements and estimating their capability is still a major hurdle. Current models often depend on oversimplified assumptions, such as the uniform distribution of corrosion in steel bars, which can result in inaccurate forecasts of structural behavior. Through numerical and experimental studies, this research attempts to integrate recent findings on the corrosion distribution of steel bars with structural element modeling by introducing a modified beam-column element in OpenSees. For this purpose, different stochastic distributions of corrosion associated with steel bar area reduction were incorporated into the element by modified fiber modeling formulation. This study also considered the impact of transverse bars on element modeling, which further increased the results' accuracy. The accuracy of the proposed element was evaluated by comparing the modeling results with laboratory observations. The results indicated that the proposed element with suggested area reduction models considerably increases the modeling accuracy compared to the existing techniques based on even corrosion distribution.
 
Key Words
    corroded RC members; failure probability; fiber beam-column element; OpenSees; probability distribution
 
Address
Behrouz Akbarian Reshvanlou: Structural Engineering Division, Department of Civil Engineering, University of Zanjan, Zanjan, Iran
Kiarash Nasserasadi and Jamal Ahmadi: Structural Health Monitoring Research Lab, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran
 

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