• Journal of the Korean institute of surface engineering
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• v.41 no.3
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• pp.121-128
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• 2008

High strength 7xxx aluminum alloys have been applied to automotive bump back beam of the some limited model for light weight vehicle. The aluminum bump back beam is manufactured through extrusion, bending and welding. The residual stress given on these processes combines with the corrosive atmosphere on the road spreaded with corrosive chemicals to melt snow to occur the stress corrosion cracking. The composition of commercial 7xxx aluminum has Zn/Mg ratio about 3 and Cu over 2 wt% for better strength and stress corrosion cracking resistivity. But this composition isn't adequate for appling to the automotive bump back beam with high resistance to extrusion and bad weldability. In this study the composition of 7xxx aluminum alloy was modified to high Zn/Mg ratio and low Cu content for better extrusion and weldability. To estimate the resistivity against stress corrosion cracking of this aluminum alloy by slow strain rate test, the corrosion atmosphere and strain rate separate the stress corrosion cracking from conventional corrosion must be investigated. Using 0.6 Mol NaCl solution on slow strain rate test the stress corrosion cracking induced fracture was not observed. By adding 0.3% $H_2O_2$ and 0.6M $Na_2SO_4$ to 1M NaCl solution, the corrosion potential and current density of polarization curve moved to active potential and larger current density, and on the slow strain rate test the fracture energy in solution was lower than that in pre-exposure. These mean the stress corrosion cracking induced fracture can be estimated in this 1M NaCl + 0.3% $H_2O_2$ + 0.6M $Na_2SO_4$ solution. When the strain rate was below $2{\times}10^{-6}$, the stress corrosion cracking induced fracture start to be observed.

• Computers and Concrete
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• v.31 no.1
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• pp.33-52
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• 2023

This paper proposes and validates the extension of two models, previously formulated for the evaluation of the shear strength of reinforced concrete members with un-corroded reinforcements, to the case of beams with corroded stirrups. These extended models are based on the plasticity theory (this model has been proposed in the past by one of the authors) and on the simplified modified compression field theory. The response of these models is compared with that of the compression chord capacity model, which has recently been embedded with modifications that simulate the effects of steel corrosion. These latter modifications are first discussed and then introduced into the other two models. An existing database of slender and non-slender beams tested in laboratory by other researchers is revised and improved. Finally, all the considered models are applied to the selected specimens and a comparison is drawn between the shear strength resulting from the considered models and the shear strength resulting from the laboratory tests. The effects of corrosion on some important parameters of the ultimate shear response of the reinforced concrete beams are also discussed.

• Computers and Concrete
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• v.19 no.1
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• pp.19-31
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• 2017

This paper uses modelling and experiment to perform a quantitative analysis for the gypsum and ettringite formations in cement pastes subjected to sulfate attack. Firstly, based on Fick's law and chemical reaction kinetics, a diffusion model of sulfate ions in cement pastes is proposed, and then the model of the gypsum and ettringite formations is established to analyze its contents in cement pastes with corrosion time. Secondly, the corrosion experiment of the specimens with cement pastes immersed into 2.5%, 5.0% and 10.0% $Na_2SO_4$ solutions are carried out, and by using XRD-Rietveld method, the phases of powder samples from the specimens are quantitatively analyzed to obtain the contents of gypsum and ettringite in different surface depth, solution concentration and corrosion time. Finally, the contents of gypsum and ettringite calculated by the models are compared with the results from the XRD experiments, and then the effects of surface depth, corrosion time and solution concentration on the gypsum and ettringite formations in cement pastes are discussed.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1132-1140
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• 2007

As it recently appears that Life Cycle Cost Analysis may be considered as new methodology for economic valuation of infrastructure many researches have been made to assess LCC(Life Cycle Cost) of each facility based on a reasonable methods. In general, LCC is composed of construction cost and expected maintenance repair cost. And especially, maintenance repair cost must be estimated to enhance the reliability through systematic and reasonable methods. However in Korea, because high speed railway steel bridges are recently constructed no direct statistical data are available for the account of the maintenance cost and then their maintenance characteristics are not linear yet. Therefore, the approach proposed in the paper utilizes a theoretical determination and degradation of the corrosion and fatigue of the bridges based on Rahgozar et al.(2006)'s model on fatigue notch factor considering into the corrosion to incorporate the corrosion effect into the fatigue strength reduction model. And then, the corresponding probability of failure is calculated in terms of the reliability index using S-N curve to formulate the fatigue limit state. Therefore, this paper proposes the minimum Life Cycle Cost through optimum maintenance plan analysis of high-speed railway steel bridges under construction. Finally, this paper reviews the proposed model in oder to confirm the applicability and feasibility by appling it to high speed railway steel bridges under construction

• Computers and Concrete
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• v.32 no.2
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• pp.119-138
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• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

• Corrosion Science and Technology
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• v.6 no.4
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• pp.170-176
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• 2007

Iodine-induced stress corrosion cracking (ISCC) properties and the associated ISCC process of Zircaloy-4 and an Nb-containing advanced nuclear fuel cladding were evaluated. An internal pressurization test with a pre-cracked specimen was performed with a stress-relieved (SR) or recrystallized (RX) microstructure at $350^{\circ}C$, in an iodine environment. The results showed that the $K_{ISCC}$ of the SR and RX Zircaloy-4 claddings were 3.3 and 4.8MPa\;m^{0.5}, respectively. And the crack propagation rate of the RX Zircaloy-4 was 10 times lower than that of the SR one. The chemical effect of iodine on the crack propagation rate was very high, which was increased $10^4$ times by iodine addition. Main factor affecting on the micro-crack nucleation was a pitting formation and its agglomeration along the grain boundary. However, this pitting formation on the grain-boundary was suppressed in the case of an Nb addition, which resulted in an increase of the ISCC resistance when compared to Zircaloy-4. Crack initiation and propagation mechanisms of fuel claddings were proposed by a grain boundary pitting model and a pitting assisted slip cleavage model and they showed reasonable results.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.297-314
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• 2016

As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (${\theta}$) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.

• Structural Engineering and Mechanics
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• v.56 no.2
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• pp.257-273
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• 2015

Numerical value of correlation between effective parameters in the strength of a structure is as important as its stochastic properties in determining the safety of the structure. In this article investigation is made about the variation of coefficient of correlation between effective parameters in corrosion initiation time of reinforcement and the time of concrete cover cracking in reinforced concrete (RC) structures. Presence of many parameters and also error in measurement of these parameters results in uncertainty in determination of corrosion initiation and the time to crack initiation. In this paper, assuming diffusion process as chloride ingress mechanism in RC structures and considering random properties of effective parameters in this model, correlation between input parameters and predicted time to corrosion is calculated using the Monte Carlo (MC) random sampling. Results show the linear correlation between corrosion initiation time and effective input parameters increases with increasing uncertainty in the input parameters. Diffusion coefficient, concrete cover, surface chloride concentration and threshold chloride concentration have the highest correlation coefficient respectively. Also the uncertainty in the concrete cover has the greatest impact on the coefficient of correlation of corrosion initiation time and the time of crack initiation due to the corrosion phenomenon.

• Journal of the Korean Ceramic Society
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• v.38 no.9
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• pp.829-833
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• 2001

There have been many studies about the chemical corrosion of converter's refractories. However, few studies have been reported for the stress corrosion induced by the weight of the refractories and by the two bottom shape models designed for the converter. In this study, the stress distributions of the two converter models were analyzed by FEM. Model 1 has the step edge shape and model 2 has the round edge shape. Simulation results show that model 2 of round bottom edge has higher gravitational stress just below the slag line than the model 1. Model 2 is considered to suffers more serious corrosion than model 1 during the middle working stage by the thermal and chemical factors.

• Steel and Composite Structures
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• v.24 no.5
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• pp.635-641
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• 2017

Safety service improvement and development of efficient maintenance strategies for corroded steel structures are undeniably essential. Therefore, understanding the influence of damage caused by corrosion on the remaining load-carrying capacities such as tensile strength is required. In this study, artificial neural network (ANN) approach is proposed in order to produce a simple, accurate, and inexpensive method developed by using tensile test results, material properties and finite element method (FEM) results to train the ANN model. Initially in reproducing corroded model process, FEM was used to obtain tensile strength of artificial corroded plates, for which surface is developed by a spatial autocorrelation model. By using the corroded surface data and material properties as input data, with tensile strength as the output data, the ANN model could be trained. The accuracy of the ANN result was then verified by using leave-one-out cross-validation (LOOCV). As a result, it was confirmed that the accuracy of the ANN approach and the final output equation was developed for predicting tensile strength without tensile test results and FEM in further work. Though previous studies have been conducted, the accuracy results are still lower than the proposed ANN approach. Hence, the proposed ANN model now enables us to have a simple, rapid, and inexpensive method to predict residual tensile strength more accurately due to corrosion in steel structures.