Resistivity-based chloride-induced corrosion rate prediction models and hypothetical framework for interpretation of resistivity measurements in cracked RC structures

2015 ◽  
Vol 49 (6) ◽  
pp. 2349-2366 ◽  
Author(s):  
M. Otieno ◽  
H. Beushausen ◽  
M. Alexander
Keyword(s):  
Corrosion Rate ◽  
Prediction Models ◽  
Rc Structures ◽  
Resistivity Measurements ◽  
Rate Prediction ◽  
Chloride Induced Corrosion

scholarly journals Empirical models of corrosion rate prediction of steel in reinforced concrete structures

2020 ◽  
Vol 14 (2) ◽  
pp. 98-107
Author(s):  
Nguyen Ngoc Tan ◽  
Dang Vu Hiep
Keyword(s):  
Reinforced Concrete ◽  
Corrosion Rate ◽  
Prediction Models ◽  
Concrete Structures ◽  
Steel Corrosion ◽  
Empirical Models ◽  
Chloride Ions ◽  
Reinforced Concrete Structures ◽  
Damage Prediction ◽  
Rate Prediction

Corrosion rate is one of the most important input parameters in corrosion-induced damage prediction models as well as in calculation of service-life for reinforced concrete structures. In most cases, instantaneous measurements or constant corrosion rate values used in damage prediction models is irrelevant. The new factors appearing such as corrosion-induced cover cracking, concrete quality to change the corrosion rate should be taken into consideration. This study shows several empirical models to predict the corrosion rate and their limits of application. The predicted values of steel corrosion rate using four empirical models are compared with the measured values of a series of 55 experimental samples collected from the literature. The results show that the empirical models overestimated the experimental corrosion rate. Using model proposed by Liu and Weyers provided the best agreement with the experimental data. Keywords: corrosion rate; prediction model; reinforced concrete; chloride ions; reinforcement corrosion.

Research on CO2 Corrosion Rate Prediction of Gathering Pipelines Considering the Effect of Oil

2015 ◽  
Vol 737 ◽  
pp. 788-793 ◽  
Author(s):  
Yuan Peng Cheng ◽  
Zi Li Li ◽  
Qian Qian Liu
Keyword(s):  
Corrosion Rate ◽  
Empirical Model ◽  
Prediction Models ◽  
Mechanistic Model ◽  
Experimental Studies ◽  
Research Directions ◽  
Rate Prediction ◽  
Semi Empirical ◽  
The Impact ◽  
Sweet Corrosion

Experimental studies show that under special conditions, oils in corrosion environment have some inhibiting effect on CO2corrosion behavior of gathering pipelines. Oil wetting and corrosion product film are the great difference in existent rate prediction models of sweet corrosion. The progress of CO2corrosion rate prediction including empirical model, semi-empirical model, mechanistic model and artificial neural networks model considering the impact of oil in recent years are introduced in detail, the present problems and further research directions are also discussed.

scholarly journals Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Nagoor Basha Shaik ◽  
Kedar Mallik Mantrala ◽  
Balaji Bakthavatchalam ◽  
Qandeel Fatima Gillani ◽  
M. Faisal Rehman ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Corrosion Rate ◽  
Prediction Models ◽  
Test Time ◽  
Bayesian Regularization ◽  
Cobalt Chromium ◽  
Corrosion Properties ◽  
Cocrmo Alloy ◽  
Artificial Neural

AbstractThe well-known fact of metallurgy is that the lifetime of a metal structure depends on the material's corrosion rate. Therefore, applying an appropriate prediction of corrosion process for the manufactured metals or alloys trigger an extended life of the product. At present, the current prediction models for additive manufactured alloys are either complicated or built on a restricted basis towards corrosion depletion. This paper presents a novel approach to estimate the corrosion rate and corrosion potential prediction by considering significant major parameters such as solution time, aging time, aging temperature, and corrosion test time. The Laser Engineered Net Shaping (LENS), which is an additive manufacturing process used in the manufacturing of health care equipment, was investigated in the present research. All the accumulated information used to manufacture the LENS-based Cobalt-Chromium-Molybdenum (CoCrMo) alloy was considered from previous literature. They enabled to create a robust Bayesian Regularization (BR)-based Artificial Neural Network (ANN) in order to predict with accuracy the material best corrosion properties. The achieved data were validated by investigating its experimental behavior. It was found a very good agreement between the predicted values generated with the BRANN model and experimental values. The robustness of the proposed approach allows to implement the manufactured materials successfully in the biomedical implants.

scholarly journals Experimental and Simulation Study on Diffusion Behavior of Chloride Ion in Cracking Concrete and Reinforcement Corrosion

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Yongchun Cheng ◽  
Yuwei Zhang ◽  
Chunli Wu ◽  
Yubo Jiao
Keyword(s):  
Finite Element ◽  
Prediction Models ◽  
Chloride Concentration ◽  
Chloride Ion ◽  
Corrosion Current ◽  
Chloride Diffusion ◽  
Finite Element Models ◽  
Rc Structures ◽  
Cracked Concrete ◽  
Chloride Migration

A chloride ion is a key factor affecting durability of reinforced concrete (RC) structures. In order to investigate chloride migration in cracked concrete, considering the mesoscopic heterogeneity of concrete, concrete modeled here is treated as a four-phase composite consisting aggregate, mortar, crack, and interfacial transition zone (ITZ). In this paper, two-dimensional finite element models of cracked concrete with different crack widths and crack quantity are established and the control parameters are determined based on the nonsteady-state chloride migration (NSSCM) test. In addition, based on the concrete finite element models, influences of crack width, crack quantity, and erosion time on chloride migration behaviors and characteristics are studied. Furthermore, a prediction model of chloride concentration on the simulated surface of a rebar in concrete influenced by different crack states is established. This model is used to derive the corrosion current density and corrosion depth prediction models of a rebar in this paper, which can be used by engineers to estimate the migration behaviors of chloride and rebar corrosion degree in RC structures in a short time and evaluate the duration of RC structures after knowing the status of cracks and chloride diffusion sources.

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