• Elastomers and Composites
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• v.58 no.1
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• pp.44-56
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• 2023

Additive manufacturing (AM) or three-dimensional (3D) printing of metals has been drawing significant attention due to its reliability, usefulness, and low cost with rapid prototyping. Among the various AM technologies, fused deposition modeling (FDM) or fused filament fabrication is receiving much interest because of its simple manufacturing processing, low material waste, and cost-effective equipment. FDM technology uses metal-filled polymer filaments for 3D printing, followed by debinding and sintering to fabricate complex metal parts. An efficient binder is essential for producing polymer filaments and the thermal post-processing of printed objects. This study involved an in-depth investigation of and a fabrication route for a novel multi-component binder system with steel alloy powder (45 vol.%) ranging from filament fabrication and 3D printing to debinding and sintering. The binder system consisted of polyvinyl pyrrolidone (PVP) as a binder and thermoplastic polyurethane (TPU) and polylactic acid (PLA) as a carrier. The PVP binder held the metal components tightly by maintaining their stoichiometry, and the TPU and PLA in the ratio of 9:1 provided flexibility, stiffness, and strength to the filament for 3D printing. The efficacy of the binder system was examined by fabricating 3D-printed cubic structures. The results revealed that the thermal debinding and sintering processes effectively removed the binder/carrier from the cubic structures, resulting in isotropic shrinkage of approximately 15.8% in all directions. The scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) patterns displayed the microstructure behavior, phase transition, and elemental composition of the 3D cubic structure.

• Journal of the Korean Geotechnical Society
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• v.24 no.2
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• pp.59-65
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• 2008

Digital close-range photogrammetric technique can measure and describe 3D geometric farm from 2D image. This technique is increasingly applied in the field of sciences. In the fields of civil and mechanical engineering, which need precise measurements for design, expensive measuring equipments are widely used. In occasions where visual inspection is required in addition to other forms of measurements, appropriate measuring equipments have not been yet available. This study utilizes digital close-range photogrammetric technique to quantitatively analyze behavior patterns before and after destruction from test model of reinforced-soil wall. Then the results are compared with the measurements obtained using digital theodolite to verify the reliability of the proposed method.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.2
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• pp.223-229
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• 2023

Seawater batteries are next-generation secondary batteries that use seawater as a cathode. They utilize marine resources to provide competitive prices, high eco-friendliness, and a structure suitable for marine applications. Based on these advantages, pouch types and prismatic types have been studied and developed assuming natural seawater exposure. However, because of the electrical characteristics of the secondary battery, its capacity and internal resistance vary depending on the use environment. These characteristics are not only utilized for predicting the life of a battery but also have a direct effect on the capacity and power suitable for a specific situation. Therefore, the internal resistance was analyzed in this study by measuring the capacity depending on the seawater battery use environment and the state-of-charge-open-circuit-voltage measurement method.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.1
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• pp.37-45
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• 2009

In this study, experiments of development and application of 50 MPa high-performance concrete are performed for large dimensional tunnel linings. In order to produce 50MPa high-performance concrete, eight optimal mixtures replacing with fly ash and ground granulated blast furnace slag up to 50 percent of type I Portland cement were selected then tests for mechanical properties and simple adiabatic temperature rise tests were carried out. And in order to assess the quantitative characteristics of heat of hydrations of developed mixtures, three mixtures that the type I Portland cement (OPC) and each one mixture of binary and ternary mixtures (BS30, F15S35) were reselected, then the adiabatic temperature rise tests and mock-up tests were performed. Consequently, the comparisons between the results of mock-up tests and finite element analysis can be enhanced the reliability of analyzing routines of thermal behaviours of the developed high-performance concrete.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.831-838
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• 2022

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.

• Steel and Composite Structures
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• v.45 no.1
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• pp.67-82
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• 2022

In this paper, buckling and free vibration of imperfect, functionally graded beams, including porosities, are investigated, using a higher order shear strain theory. Due to defects during the manufacturing process, micro porosities may appear in the material, hence the appearance of this imperfection in the structure. The material properties of the beams are assumed to vary regularly, with power and sigmoid law, in the direction of thickness. A novel porosity distribution affecting the functionally graded volume fraction is presented. For the compact formulation used for cementite-based materials and already used in P-FGM, we have adapted it for the distribution of S-FGM. The equations of motion in the FG beam are derived using Hamilton's principle. The boundary conditions for beam FG are assumed to be simply supported. Navier's solution is used to obtain the closed form solutions of the FG beam. The numerical results of this work are compared with those of other published research to verify accuracy and reliability. The comparisons of different shear shape functions, the influence of porosity, thickness and inhomogeneity parameters on buckling and free vibration of the FG beam are all discussed. It is established that the present work is more precise than certain theories developed previously.

• Computers and Concrete
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• v.30 no.5
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• pp.339-355
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• 2022

The high cost of ultra-high-performance engineered cementitious composite (UHP-ECC) is currently a crucial issue, especially in terms of the polyethylene (PE) fibers use. In this paper, cheap calcium carbonate whiskers (CW) were evaluated on the feasibility of hybrid with PE fibers. Diverse combinations of PE fibers and CW were employed to investigate the multi-scale enhancement on the UHP-ECC performance. A probabilistic-based UHP-ECC tensile strain reliability analysis approach was utilized, which was in general agreement with the experimental results. Furthermore, a multi-dimensional integrated representation was conducted for the comprehensive assessment of UHP-ECC. Results illustrated that CW improved the compressive strength and energy dissipation capacity of UHP-ECC owing to the microscopic strengthening mechanism. CW and PE fiber further promoted the saturated cracking of composite by multi-scale crack arresting effect. In particular, PE1.75-CW0.5 specimen possessed the best overall performance. The ultimate cracking width of PE1.75-CW0.5 group had 98 ㎛, which was 46.18% lower compared to PE2-CW0 group, the 28d compressive strength were slightly improved, the tensile strain capacity was comparable to that of PE2-CW0 group. The results above demonstrated that combinations of PE fiber and CW could significantly enhance the comprehensive performance of UHP-ECC, which was beneficial for large-scale engineering applications.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.495-507
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• 2023

The roughness of discontinuity significantly influences the mechanical characteristics of rock masses and extensively affects thermal and hydraulic behaviors. In this study, we utilized photogrammetry to generate 3D point cloud data for discontinuity and applied this data to characterize the roughness of discontinuity. The discontinuity profiles, reconstructed from the 3D point cloud data, were compared with those manually measured using a profile gauge. This comparison served to validate the accuracy and reliability of the acquired point cloud data in replicating the actual configurations of rock surfaces. Subsequent to this validation, influence of the number of profiles for representative JRC assessment was further investigated followed by suggestion of roughness anisotropy evaluation method with application of it to actual rock discontinuity surfaces.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.59-65
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• 2024

In this study, in order to improve the mechanical properties and durability of asphalt mixtures, a modifier (CSM, Cationized Silicate Modifier) was applied to asphalt to derive optimal mixing ratio conditions. Design of asphalt mixture using modified asphalt binder was conducted, and moisture resistance and dynamic stability were evaluated for optimal mixing conditions. The evaluation results showed that it exceeded the standards stipulated in the relevant guidelines, and as a result of conducting a water permeability test on the optimal mixing condition, it was confirmed that impermeable performance was secured. As a result of examining the noise reduction performance through field test, a noise reduction performance of about 10 dB was secured compared to before paving. It will be necessary to secure reliability through continuous noise generation evaluation in the future.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.20 no.1
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• pp.56-65
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• 2024

Cast austenitic stainless steels (CASS) and austenitic stainless steel weldments with a ferrite-austenite duplex structure are widely used in nuclear power plants, incorporating ferrite phase to enhance strength, stress relief, and corrosion resistance. Thermal aging at 290-325℃ can induce embrittlement, primarily due to spinodal decomposition and G-phase precipitation in the ferrite phase. This study evaluates the effects of thermal aging by collecting and analyzing various mechanical properties, such as Charpy impact energy, ferrite microhardness, and tensile strength, from various literature sources. Different model expressions, including hyperbolic tangent and phase transformation equations, are applied to calculate activation energy (Q) of room-temperature impact energies, and the results are compared. Additionally, predictive models for Q based on material composition are evaluated, and the potential of machine learning techniques for improving prediction accuracy is explored. The study also examines the use of ferrite microhardness and tensile strength in calculating Q and assessing thermal embrittlement. The findings provide insights for developing advanced prediction models for the thermal embrittlement behavior of CASS and the weldments of austenitic steels, contributing to the safety and reliability of nuclear power plant components.