• Advances in concrete construction
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• v.14 no.3
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• pp.195-204
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• 2022

As a brittle failure mode, punching-shear failure can be widely found in traditional RC slab-column connections, which may lead to the entire collapse of a flat plate structure. In this paper, a novel RC slab-column connection with inner steel truss was proposed to enhance the punching strength. In the proposed connection, steel trusses, each of which was composed of four steel angles and a series of steel strips, were pre-assembled at the periphery of the column capital and behaved as transverse reinforcements. With the aim of exploring the punching behavior of this novel RC slab-column connection, a static punching test was conducted on two full-scaled RC slab specimens, and the crack patterns, failure modes, load-deflection and load-strain responses were thoroughly analyzed to explore the contribution of the applied inner steel trusses to the overall punching behavior. The test results indicated that all the test specimens suffered the typical punching-shear failure, and the higher punching strength and initial stiffness could be found in the specimen with inner steel trusses. The numerical models of tested specimens were analyzed in ABAQUS. These models were verified by comparing the results of the tests with the results of the analyzes, and subsequently the sensitivity of the punching capacity to different parameters was studied. Based on the test results, a modified critical shear crack theory, which could take the contribution of the steel trusses into account, was put forward to predict the punching strength of this novel RC slab-column connection, and the calculated results agreed well with the test results.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.45-56
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• 2024

Faced with aging populations, declining resources, and limited agricultural productivity, rural areas in South Korea require innovative solutions. This study investigated the potential of drone-based vegetation indices (VIs) to analyze soybean growth patterns in open-field smart agriculture in Goesan-gun, Chungbuk Province, South Korea. We monitored multi-seasonal normalized difference vegetation index (NDVI) and the normalized difference red edge (NDRE) data for three soybean lots with different irrigation methods (subsurface drainage, conventional, subsurface drip irrigation) using drone remote sensing. Combining NDVI (photosynthetically active biomass, PAB) and NDRE (chlorophyll) offered a comprehensive analysis of soybean growth, capturing both overall health and stress responses. Our analysis revealed distinct growth patterns for each lot. LotA(subsurface drainage) displayed early vigor and efficient resource utilization (peaking at NDVI 0.971 and NDRE 0.686), likely due to the drainage system. Lot B (conventional cultivation) showed slower growth and potential limitations (peaking at NDVI 0.963 and NDRE 0.681), suggesting resource constraints or stress. Lot C (subsurface drip irrigation) exhibited rapid initial growth but faced later resource limitations(peaking at NDVI 0.970 and NDRE 0.695). By monitoring NDVI and NDRE variations, farmers can gain valuable insights to optimize resource allocation (reducing costs and environmental impact), improve crop yield and quality (maximizing yield potential), and address rural challenges in South Korea. This study demonstrates the promise of drone-based VIs for revitalizing open-field agriculture, boosting farm income, and attracting young talent, ultimately contributing to a more sustainable and prosperous future for rural communities. Further research integrating additional data and investigating physiological mechanisms can lead to even more effective management strategies and a deeper understanding of VI variations for optimized crop performance.

• Journal of the Korean Geosynthetics Society
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• v.22 no.4
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• pp.1-8
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• 2023

Space exploration is at the forefront of human scientific endeavors, and planetary exploration rovers play a critical role in studying planetary surfaces. Rover performance is especially vital for safely navigating steep terrain and delicate landscapes found on planets like Mars and the Moon. This paper offers a comprehensive overview of a landing testbed designed to simulate challenging extraterrestrial terrain and loose regolith. The paper briefly outlines lunar crater region topographical features and highlights the importance of these simulations in rover testing. It then explores previous landing testbed developments and describes the design process for a landing testbed to be installed in the dirty thermal vacuum chamber at the Korea Institute of Civil Engineering and Building Technology. Once realized, this proposed landing testbed will enable precise evaluations of rover mobility and exploration capabilities under lunar-like conditions, including high vacuum and extreme temperatures.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.295-305
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• 2023

This study introduces a smoothed finite-element implementation into the phase-field framework. In recent years, the phase-field method has recieved considerable attention in crack initiation and propagation since the method needs no further treatment to express the crack growth path. In the phase-field method, high strain-energy accuracy is needed to capture the complex crack growth path; thus, it is obtained in the framework of the smoothed finite-element method. The salient feature of the smoothed finite-element method is that the finite element cells are divided into sub-cells and each sub-cell is rebuilt as a smoothing domain where smoothed strain energy is calculated. An adaptive quadtree refinement is also employed in the present framework to avoid the computational burden. Numerical experiments are performed to investigate the performance of the proposed approach, compared with that of the finite-element method and the reference solutions.

• Transactions of the Korean hydrogen and new energy society
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• v.34 no.6
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• pp.555-561
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• 2023

In this study, we proposed a standard model for the design, construction and demonstration of the technology development and substantiation of small hydrogen powered vessel in order to respond to the alternative fuel-using vessel market that requires the use of low-carbon/carbon-free fuel as a greenhouse gas reduction measure. The hydrogen fuel cell-based electric propulsion system developed through this is optimized through performance and durability tests on the land-based test site (LBTS), and the electric propulsion system applied to this result is mounted on a small hydrogen propulsion vessel and operated. Simultaneously, through the digital twin technology between the LBTS and the hydrogen-propelled vessel on the sea, the technology that can predict and diagnose the problems that can occur in the electric propulsion system of the vessel is applied to carry out the empirical study of the hydrogen-propelled vessel. In addition, we propose a commercialization model by analyzing the economic feasibility of the demonstration vessel.

• Advances in nano research
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• v.15 no.5
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• pp.467-484
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• 2023

Despite the significant features of fiber-reinforced cementitious composites (FRCCs), including better mechanical, fractural, and durability performance, their high content of cement has restricted their use in the construction industry. Although ground granulated blast furnace slag (GGBFS) is considered the main supplementary cementitious material, its slow pozzolanic reaction stands against its application. The addition of nano-sized mineral modifiers, including nano-silica (NS), is an alternative to address the drawbacks of using GGBFS. The main object of this empirical and numerical research is to examine the effect of NS on the strain-hardening behavior of cementitious composites; ten mixes were designed, and five levels of NS were considered. This study proposes a new method, using a four-point bending test to assess the use of nano-silica (NS) on the flexural behavior, first cracking strength, fracture energy, and micromechanical parameters including interfacial friction bond strength and maximum bridging stress. Digital image correlation (DIC) was used for monitoring the initiation and propagation of the cracks. In addition, to attain a deep comprehension of fiber/matrix interaction, scanning electron microscope (SEM) analysis was used. It was discovered that using nano-silica (NS) in cementitious materials results in an enhancement in the matrix toughness, which prevents multiple cracking and, therefore, strain-hardening. In addition, adding NS enhanced the interfacial transition zone between matrix and fiber, leading to a higher interfacial friction bond strength, which helps multiple cracking in the composite due to the hydrophobic nature of polypropylene (PP) fibers. The findings of this research provide insight into finding the optimum percent of NS in which both ductility and high tensile strength of the composites would be satisfied. As a concluding remark, a new criterion is proposed, showing that the optimum value of nano-silica is 2%. The findings and proposed method of this study can facilitate the design and utilization of green cementitious composites in structures.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Steel and Composite Structures
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• v.48 no.1
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• pp.59-71
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• 2023

Corrosion of the headed studs shear connectors is an important factor in the reduction of the durability and mechanical properties of the steel-concrete composite structure. In order to study the effect of stud corrosion on the mechanical properties in the negative moment region of steel-concrete composite beams, the corrosion of stud was carried out by accelerating corrosion method with constant current. Static monotonic loading was adopted to evaluate the cracking load, interface slip, mid-span deflection, and ultimate bearing capacity of four composite beams with varying corrosion rates of headed studs. The effect of stud corrosion on the stiffness of the composite beam's hogging moment zone during normal service stage was thoroughly examined. The results indicate that the cracking load decreased by 50% as the corrosion rate of headed studs increase to 10%. Meanwhile, due to the increase of interface slip and mid-span deflection, the bending stiffness dropped significantly with the same load. In comparison to uncorroded specimens, the secant stiffness of specimens with 0.5 times ultimate load was reduced by 25.9%. However, corrosion of shear studs had no obvious effect on ultimate bending capacity. Based on the experimental results and the theory of steel-concrete interface slip, a method was developed to calculate the bending stiffness in the negative bending moment region of composite beams during normal service stage while taking corrosion of headed studs into account. The validity of the calculation method was demonstrated by data analysis.

• Explosives and Blasting
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• v.41 no.4
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• pp.29-40
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• 2023

Rock blasting is utilized in various fields such as mining, tunneling, and the construction of underground structures. The role of rock blasting technology has became increasingly significant with the growing utilization of underground cavity. Blast hole pressure, generated during rock blasting, is a critical variable directly impacting factors such as crushing and blast vibration. It stands out as one of the most important parameters for assessing explosive performance and predicting blasting effects. While blast hole pressure has been studied by several researches, comparisons are challenging due to variations in experimental conditions such as explosive type, charge, and blasting conditions. In this study, blast hole pressure sensors and observation hole pressure sensors were developed to measure pressure during single-hole blasting, The experimental results were then used to discuss the propagation characteristics of pressure around the blast hole and the corresponding blast vibration.