Tae-Yeon Kim’s research while affiliated with Seoul National University and other places

This study investigates the impact of a sustainable graphene derivative, designated as D-GSH, on the compressive strength development of cement mortars. The research evaluates the effects of D-GSH addition by 0.25%, by weight of cement, and dune sand-to-cement ratios on compressive strength at 1, 7, and 28 days. Compressive strength tests reveal that the addition of 0.25% D-GSH significantly enhances strength across all age intervals, with increases of 13%, 55%, and nearly 50% at days 1, 7, and 28, respectively. Conversely, increasing the dune sand-to-cement ratio from 1:1 to 1:3 adversely affects early strength development. While the higher sand content initially boosts strength, its long-term performance is hindered, highlighting the necessity for optimal dune sand content in cement mortars. The findings demonstrate that D-GSH effectively accelerates early strength gain, making it a viable alternative for improving the performance of cement-based composites.

This study investigates the mechanical properties of geopolymer concrete made with ground granulated blast furnace slag (GGBS) and silica fume (SF) as binders. The influence of varying binder proportions and sodium silicate-to-sodium hydroxide (SS-to-SH) ratios of 1.5 and 2.0 in the alkali-activated solution was examined. Experimental tests evaluated slump, compressive strength, modulus of elasticity, and splitting tensile strength at 1, 7, and 28 days. Increasing SF content up to 50% in the binder with a solution ratio of 1.5 improved the 28-day compressive strength by 50% compared to mixes made solely with slag. However, further increase in SF reduced splitting tensile strength and compressive strength by 79 and 56%, respectively, at 28 days. Increasing the solution ratio from 1.5 to 2.0 enhanced compressive strength for slag-dominant mixes by up to 63% but reduced strength for SF-rich mixes by up to 87%. The highest modulus of elasticity, 18.7 GPa, was achieved with slag-only binders and a solution ratio of 2.0, marking a 240% increase over its counterpart mix with a lower solution ratio. Equal GGBS and SF blends improved splitting tensile strength compared to SF-rich mixes but were surpassed by GGBS-rich mixes in terms of overall structural performance.

This study aims to predict the flexural response through a 3D non-linear finite element model of plain and modified cement mortar incorporating a sustainable graphene derivative, denoted as D-GSH, synthesized from dune sand and date syrup. The addition of D-GSH in cement mortars was considered by dune sand replacement. Preliminary experimental compressive strength and modulus of elasticity yielded 53 and 45% enhancements upon the addition of 0.3% D-GSH, as a replacement of dune sand. ABAQUS software was employed to simulate a three-point load test on mortar prism specimens, encompassing both tensile cracking and compressive crushing mechanisms. Numerical simulation of three-point bending tests revealed a notable 27% increase in peak load and a 16% larger mid-span deflection upon the addition of 0.3% D-GSH, replaced by dune sand in cement mortars, demonstrating improved resistance and deformability.

This study presents finite element (FE) modelling of tire pavement interaction based on a micromechanical pavement surface. The FE model of the pavement surface is created using CT scan images of an actual pavement specimen to accurately represent the micromechanical surface morphology of the pavement. The micromechanical FE model of the pavement consists of aggregate, binder, and air voids. Using the micromechanical pavement model, tire pavement interaction simulations are performed using ABAQUS by setting binder and rubber as viscoelastic materials and aggregate as an elastic material. Moreover, the FE model includes layers of asphalt, base course, and subgrade to mimic a real pavement. The interaction between tire and the pavement surface is modelled via the surface-to-surface contact.

The BAPS Hindu Mandir, recently constructed in Abu Dhabi, UAE, is a complex unreinforced stone masonry structure built from thousands of sculpted sandstone and marble pieces employing ancient Indian techniques called Shilp Shastra. The entire structure is substantially large with a footprint size of 5,100 m 2 and unique so that it is not covered by modern seismic design standards. Its performance was verified by conducting dynamic field tests presented herein. The most vulnerable substructure was identified based on both engineering judgement and modal analysis of the entire structure employing a detailed 3D finite element model, which was validated via the field experiments. A "local" model was developed for the identified vulnerable substructure which significantly reduced model complexity and allowed to overcome computational limitations. Based on the response of the local model, the relative importance of the sensor locations was determined via a Displacement Index method in addition to a reduction of the total variance of spectral accelerations using conditional probability theory. Through this approach, a methodology for selecting the optimal sensor placement with application on the complex unreinforced stone masonry Hindu Mandir is proposed. ARTICLE HISTORY

Effectively detecting defects in wafer buffer zones is crucial in semiconductor manufacturing processes. If defects are not detected in the middle of the semiconductor manufacturing process, the semiconductor eventually becomes a defective product after all processes are completed. To solve this problem, rule-based vision algorithms have been used to identify defects in the wafer buffer zone. After photographing the wafer buffer zone using a high-speed camera, defects are detected using the pixel values of the images. However, because of the resin bleed in the wafer, which is an epoxy compound, it is difficult to detect defects. Therefore, we introduced a deep learning method for semiconductor inspection and created a high-performance semiconductor inspection algorithm. The defects in the wafer buffer zone should be detected accurately and quickly. Furthermore, the approximate size of the defects must be extracted. We modified the Xception model to fit the wafer data characteristics considering both accuracy and speed. We proposed to extract the size of the defect using class activation mapping (CAM). We obtained an accuracy of 96.9% from the actual wafer dataset through this framework, and then managed to extract the size of the defect through CAM.