• Computers and Concrete
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• v.27 no.4
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• pp.385-393
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• 2021

Reinforcement corrosion is the main cause of the durability failure of reinforced concrete (RC) structure. In this paper, a three-dimensional (3D) numerical model of macro-cell corrosion is established to reveal the corrosion mechanisms of steel reinforcement in RC structure. Modified Direct Iteration Method (MDIM) is employed to solve the system of partial differential equations for reinforcement corrosion. Through the sensitivity analysis of electrochemical parameters, it is found that the average corrosion current density is more sensitive to the change of cathodic Tafel slope and anodic equilibrium potential, compared with the other electrochemical parameters. Furthermore, both the anode-to-cathode (A/C) ratio and the anodic length have significant influences on the average corrosion current density, especially when A/C ratio is less than 0.5 and anodic length is less than 35 mm. More importantly, it is demonstrated that the corrosion rate of semi-circumferential corrosion is much larger than that of circumferential corrosion for the same A/C ratio value. The simulation results can give a unique insight into understanding the detailed electrochemical corrosion processes of steel reinforcement in RC structure for application in service life prediction of RC structures in actual civil engineer.

• Structural Engineering and Mechanics
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• v.72 no.4
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• pp.479-489
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• 2019

The corrosion of reinforcement leads to a gradual decay of structural strength and durability. Several models for crack occurrence prediction and crack width propagation are investigated in this paper. Analytical and experimental models were used to predict the bond strength in the period of corrosion propagation. The manner of flexural strength loss is calculated by application of these models for different scenarios. As a new approach, the variation of the concrete beam neutral axis height has been evaluated, which shows a reduction in the neutral axis height for the scenarios without loss of bond. Alternatively, an increase of the neutral axis height was observed for the scenarios including bond and concrete section loss. The statistical properties of the parameters influencing the strength have been deliberated associated with obtaining the time-dependent bending strength during corrosion propagation, using Monte Carlo (MC) random sampling method. Results showed that the ultimate strain in concrete decreases significantly as a consequence of the bond strength reduction during the corrosion process, when the section reaches to its final limit. Therefore, such sections are likely to show brittle behavior.

• Computers and Concrete
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• v.27 no.3
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• pp.225-239
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• 2021

Carbon fiber reinforced polymer (CFRP) has extensive use in strengthening reinforced concrete structures due to its high strength and elastic modulus, low weight, fast and easy application, and excellent durability performance. Many studies have been carried out to determine the performance of the CFRP confined concrete cylinder. Although studies about the prediction of confined compressive strength using ANN are in the literature, the insufficiency of the studies to predict the strain of confined concrete cylinder using ANN, which is the most appropriate analysis method for nonlinear and complex problems, draws attention. Therefore, to predict both strengths and also strain values, two different ANNs were created using an extensive experimental database. The strength and strain networks were evaluated with the statistical parameters of correlation coefficients (R2), root mean square error (RMSE), and mean absolute error (MAE). The estimated values were found to be close to the experimental results. Mathematical equations to predict the strength and strain values were derived using networks prepared for convenience in engineering applications. The sensitivity analysis of mathematical models was performed by considering the inputs with the highest importance factors. Considering the limit values obtained from the sensitivity analysis of the parameters, the performances of the proposed models were evaluated by using the test data determined from the experimental database. Model performances were evaluated comparatively with other analytical models most commonly used in the literature, and it was found that the closest results to experimental data were obtained from the proposed strength and strain models.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.245-257
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• 2022

As the main lateral load resisting system in high-rise reinforced concrete structures, the mechanical performance of shear wall has a significant impact on the structure, especially for high-rise buildings. Steel corrosion has been recognized as an important factor affecting the mechanical performance and durability of the reinforced concrete structures. To investigate the effect on the seismic behaviour of corroded reinforced concrete shear wall induced by corrosion, analytical investigations and simulations were done to observe the effect of corrosion on the ultimate seismic capacity and drift capacity of shear walls. To ensure the accuracy of the simulation software, several validations were made using both non-corroded and corroded reinforced concrete shear walls based on some test results in previous literature. Thereafter, a parametric study, including 200 FE models, was done to study the influence of some critical parameters on corroded structural shear walls with boundary element. These parameters include corrosion levels, axial force ratio, aspect ratio, and concrete compressive strength. The results obtained would then be used to propose equations to predict the seismic resistance and drift capacity of shear walls with various corrosion levels.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1199-1209
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• 2023

Where concrete structures are designed to have a service life of over 100 years, their performance must be monitored, for the prediction models available are fraught with uncertainties that need to be eliminated. The present study was conducted to meet that need by monitoring a pilot structure for low and intermediate radioactive waste storage. Long-term operation of the sensors was observed to be adequate to determine the value of the parameters that characterise structural durability, such as corrosion current density. The parameters analysed were correlated to calculate their reciprocal impact: where applied in conjunction with artificial intelligence tools, temperature, for instance, was found suitable for finding activation energy and expansion coefficients and detecting outliers. The results showed the pilot structure to perform satisfactorily.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.47-55
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• 2023

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R² score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.7
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• pp.4960-4968
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• 2015

In this study to improve the chloride durability of the shotcrete structure depending on types and contents of mineral admixture chloride resistance was evaluated by NT BUILD 492 of european test standards. It was also evaluated with the mechanical properties such as static strength and chloride penetration resistance. For shotcrete mixed crushed stone aggregate of the maximum size 10mm of coarse aggregates was produced. Based on 28days compression strength the variable mixed with 15% silica fume showed the highest strength in 67.55MPa. As the content of fly ash and blast furnace slag increased, the strength lowered. In the chloride penetration resistance test, OPC showed "high grade" and In the case of admixture, the penetration resistance tended to increase in all variables except the fly ash. In order to evaluate the service life, the accelerated chloride penetration test was conducted by the standards of KCL, ACI, FIB. Test results were obtained with the lowest spreading factor in a variable mixed with silica fume of 15%. At the KCI standards, It was found to have a service life of about 65 years and at the FIB standards, It was found to have a service life of 131 years. Among standards, the service life of KCI standard in all of the variables was evaluated as the lowest.

• Journal of the Korea Concrete Institute
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• v.15 no.5
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• pp.662-669
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• 2003

Recently, the serviceability and durability of concrete structures under thermal load have received great attention. The thermal stress and clacking behavior of concrete at early ages are one of the important factors that affect such serviceability and durability of concrete structures. Nevertheless, most studies on the behavior of early-age concrete have been confined to the temperature and strain development itself in the laboratory. The desirable efforts to explore the material properties of concrete at early-ages have not been made extensively so far. The purpose of the present study is, therefore, to identify some important material properties that affect the stress behavior of concrete at early-ages. To this end, full-scale concrete base-restrained wall members have been fabricated, and many sensors including thermocouples, strain meters and stress meters were installed inside of the wall members. These sensors were to measure the development of temperatures, strains and stresses at several location in concrete walls during the hardening and curing phase of early-age concrete. By using these measured values of strain and stress, the compliance function at early-age was identified. The basic form of compliance function derived in this study follows the double-power law. However, the results of present study indicate that the values of existing compliance functions are much lower than actual values, especially at very early-ages. It can be seen that the prediction of stresses of early-age concrete based on the proposed compliance function agrees very well with test data. The present study allows more realistic evaluation of varying stresses in early-age concrete under thermal load.

• Journal of the Korea Concrete Institute
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• v.15 no.2
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• pp.280-287
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• 2003

To evaluate the practical application of oyster shells(OS) as construction materials, an experimental study was performed. More specifically, the long-term mechanical properties and durability of concrete blended with oyster shells were investigated. Test results indicate that long-term strength of concrete blended with 10% oyster shells is almost identical to that of normal concrete. However, the long-term strength of concrete blended with 20% oyster shells is appreciably lower than that of normal concrete. Thereby, concrete with higher oyster shell blend has the possibility of negatively influencing the concrete long-term strength. Elastic modulus of concrete blended with crushed oyster shells decreases as the blending mixture rate increases. Namely, the modulus is reduced to approximately 10∼15% when oyster shells are blended up to 20% as the fine aggregate. The drying shrinkage strain increases with an increasing crushed oyster shells substitution rate. In addition, the existing model code of drying shrinkage and creep do not coincide with the test results of this study. An adequate prediction equation needs to be developed. The utilization of oyster shells as the fine aggregate in concrete has an insignificant effect on fleering and thawing resistance, carbonation and chemical attack of concrete. However, water permeability is considerably improved.