• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Journal of Korean Association for Spatial Structures
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• v.22 no.1
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• pp.41-49
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• 2022

In the case of columns in buildings with soft story, the concentration of stress due to the difference in stiffness can damage the columns. The irregularity of buildings including soft story requires retrofit because combined load of compression, bending, shear, and torsion acts on the structure. Concrete jacketing is advantageous in securing the strength and stiffness of existing members. However, the brittleness of concrete make it difficult to secure ductility to resist the large deformation, and the complicated construction process for integrity between the existing member and extended section reduces the constructability. In this study, two types of Steel Grid Reinforcement (SGR), which are Steel Wire Mesh (SWM) for integrity and Steel Fiber Non-Shrinkage Mortar (SFNM) for crack resistance are proposed. One reinforced concrete (RC) column with non-seismic details and two columns retrofitted with each different types of proposed method were manufactured. Seismic performance was analyzed for cyclic loading test in which a combined load of compression, bending, shear, and torsion was applied. As a result of the experiment, specimens retrofitted with proposed concrete jacketing method showed 862% of maximum load, 188% of maximum displacement and 1,324% of stiffness compared to non-retrofitted specimen.

• Journal of the Microelectronics and Packaging Society
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• v.25 no.4
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• pp.1-7
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• 2018

As the performance and density of IC (integrated circuit) devices increase, power and signal integrities in the global interconnects of advanced packaging technologies are becoming more difficult. Thus, the global interconnect technologies should be designed to accommodate increased input/output (I/O) counts, improved power grid network integrity, reduced RC delay, and improved electrical crosstalk stability. This requirement resulted in the fine-pitch interconnects with a low-k dielectric in 3D packaging or wafer level packaging structure. This paper reviews an organic-inorganic hybrid material as a potential dielectric candidate for the global interconnects. An organic-inorganic hybrid material called polysiloxane can provide spin process without high temperature curing, an excellent dielectric constant, and good mechanical properties.