Abstract

Corrosion of the reinforced concrete (RC) structures has been affecting the major infrastructures in U.S. and in other continents, causing the recent several bridge collapses and incidents. While the theoretical understanding is well-established, the reliable prediction of the corrosion process in the RC structural systems has hardly been successful due to the inherent uncertainties existed in the electrochemical corrosion process and the associated material and environmental conditions. The paper proposes a computational framework to develop evidence-based probabilistic corrosion initiation models for the reinforcing steels in the RC structures, which predicts the corrosion initiation time and quantifies the inherent variances considering various acting parameters. The framework includes: probabilistic modeling with Bayesian updating based on the sets of previously generated experimental data; Bayesian model/parameter selection considering various parameters, such as material properties and environmental conditions; corrosion reliability analyses to predict the probabilities of the corrosion initiation at given time t, structural configurations, and environmental conditions; and sensitivity analyses to measure and to rank the influences of each acting parameter and its uncertainty to the probabilities of the corrosion initiation. Total of 284 sets of experimental data exposed to the coastal atmospheric environments are used for the modeling. The goal of the Bayesian model selection presented in this paper is to obtain the most accurate and unbiased model using the simplest form of expression. The developed example corrosion model is currently limited to the initiation of diffusion-induced corrosion. The model can be updated, improved, or modified upon future available sets of data. The research contributes to the decision making to improve the corrosion reliability, corrosion control, and further the structural reliability of corroding structures.

Highlights

  • Corrosion of the reinforced concrete (RC) structures has become an important threat for infrastructure reliability

  • The corrosion of RC structures has been modeled and estimated as a chemical/electrochemical reaction affected by the surrounding environment, which has been predicted by several technologies and methods [2,3,4]

  • Significant uncertainties involved in the system including material properties, environmental conditions, and chemical/electrochemical reaction play a critical role in the prediction and the estimation of reliabilities of the reinforced concrete corrosion

Read more

Summary

Introduction

Corrosion of the reinforced concrete (RC) structures has become an important threat for infrastructure reliability. Significant uncertainties involved in the system including material properties, environmental conditions, and chemical/electrochemical reaction play a critical role in the prediction and the estimation of reliabilities of the reinforced concrete corrosion. A significant amount of research has been performed on the chloride ingress into the concrete both experimentally and numerically, to predict the corrosion initiation time of the reinforcement. Arora et al [12] developed the mathematical model based on both of the finite difference method (FDM) and the probabilistic model that express the corrosion initiation time of RC columns as well as the chloride ingress with various concrete materials. Engelund and Sørensen [13] developed a probabilistic model as a stochastic process to estimate the time of corrosion initiation as well as chloride ingress in reinforced concrete structures. The developed model is used to estimate the probability of the corrosion initiation and the sensitivity analyses

Corrosion of Reinforcing Steel in Concrete Structures
Probabilistic Corrosion Initiation Model
Bayesian Methods
Experimental Data
Model Selection Criteria
Probabilistic Modeling
Uncertainties and Model Parameters
Conditional Probability of Corrosion Initiation
Importance of the Variability
Findings
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call