• Steel and Composite Structures
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• v.51 no.5
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• pp.509-527
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• 2024

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (P_U) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the P_U challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the P_U of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was t_c> L_c > E_A > t_A > E_c > b_c > f_c > f_A from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R² and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R² equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.43-50
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• 2024

Large planar military antenna boasts a range of electrical components, including TRA(Transmit-Receive Assembly), signal processors, etc. which engage in computations and calculations. These processes generate a significant amount of heat, leading to unforeseen consequences for the equipment. To mitigate these adverse effects, it's imperative to implement a cooling system that can effectively reduce heat-related issues. Given the antenna's intricate nature and the multitude of components it houses, a two-step estimation process is necessary. The first step involves a comprehensive model calculation to determine the total flow characteristics, while the second step entails a thermal analysis of individual TRA set. In this study, we depicted an antenna set using simplified 3D models of its components, considering their material and thermal properties. The sequential analysis process facilitated the calculation of branched flow rates, providing insights into the individual TRA. This approach also allowed us to design a cooling system for the TRA set, assessing its thermal stability in high-temperature environments. To ensure the optimal performance of TRA, breaking down the analysis into stages based on the cooling system's structure can assist operators in predicting numerical results more effectively.

• Steel and Composite Structures
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• v.51 no.4
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• pp.441-456
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• 2024

This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flange-restrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flange-restrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications.

• Journal of Powder Materials
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• v.31 no.3
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• pp.220-225
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• 2024

Molten salt reactors represent a promising advancement in nuclear technology due to their potential for enhanced safety, higher efficiency, and reduced nuclear waste. However, the development of structural materials that can survive under severe corrosion environments is crucial. In the present work, pure Ni was deposited on the surface of 316H stainless steel using a directed energy deposition (DED) process. This study aimed to fabricate pure Ni alloy layers on an STS316H alloy substrate. It was observed that low laser power during the deposition of pure Ni on the STS316H substrate could induce stacking defects such as surface irregularities and internal voids, which were confirmed through photographic and SEM analyses. Additionally, the diffusion of Fe and Cr elements from the STS316H substrate into the Ni layers was observed to decrease with increasing Ni deposition height. Analysis of the composition of Cr and Fe components within the Ni deposition structures allows for the prediction of properties such as the corrosion resistance of Ni.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.141-147
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• 2024

In this study, micro CT images were used to confirm the tensile fracture strength according to the pore distribution characteristics of 3D printed concrete. Unlike general specimens, concrete structures printed by 3D printing techniques have the direction of pores (voids) depending on the stacking direction and the presence of filaments contact surfaces. Accordingly, the pore distribution of 3D printed concrete specimens was analyzed through quantitative and qualitative methods, and the tensile strength by direction was analyzed through a finite element technique. It was confirmed that the pores inside the 3D printed specimen had directionality, resulting in their anisotropic behavior. This study aims to analyze the characteristics of 3D concrete printing specimen and correlate them with simulation-based mechanical properties to improve performance of 3D printed material and structure.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1747-1753
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• 2024

The accurate determination of formation density and the physical properties of rocks is the most critical logging tasks which can be obtained using gamma-ray transport and detection tools. Though the simulation works published so far have considerably improved the knowledge of the parameters that govern the responses of the detectors in these tools, recent studies have found considerable differences between the results of using a conventional model of a homogeneous mixture of formation and fluid and an inhomogeneous fractured medium. It has increased concerns about the importance of the complexity of the model used for the medium in simulation works. In the present study, we have suggested two various models for the flow of the fluid in porous media and fractured rock to be used for logging purposes. For a typical gamma-gamma logging tool containing a ¹³⁷Cs source and two NaI detectors, simulated by using the MCNPX code, a simplified porous (SP) model in which the formation is filled with elongated rectangular cubes loaded with either mineral material or oil was investigated. In this model, the oil directly reaches the top of the medium and the connection between the pores is not guaranteed. In the other model, the medium is a large 3-D matrix of 1 cm³ randomly filled cubes. The designed algorithm to fill the matrix sites is so that this realistic random (RR) model provides the continuum growth of oil flow in various disordered directions and, therefore, fulfills the concerns about modeling the rock textures consist of extremely complex pore structures. For an arbitrary set of oil concentrations and various formation materials, the response of the detectors in the logging tool has been considered as a criterion to assess the effect of modeling for the distribution of pores in the formation on simulation studies. The results show that defining a RR model for describing heterogeneities of a porous medium does not effectively improve the prediction of the responses of logging tools. Taking into account the computational cost of the particle transport in the complex geometries in the Monte Carlo method, the SP model can be satisfactory for gamma-gamma logging purposes.

• Smart Structures and Systems
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• v.34 no.1
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• pp.33-40
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• 2024

We aimed to assess the evolution of small-strain stiffness and relative density in non-compacted embankment layers. We developed embedded type in-situ soil stiffness measurement devices for monitoring small-strain stiffness occurring after filling at a test site and conducted comprehensive laboratory compaction tests using an oedometer cell with a bender element. However, direct comparison is extremely difficult because the shear wave velocity measured in the field and laboratory depend on depth and effective stress, respectively. Therefore, we propose a method for establishing a relationship between effective stress and depth using a compressibility model. In this study, the shear wave velocity measured in the field was compared to the estimated shear wave velocity-depth profiles for completely dry and saturated conditions with different relative densities. The relative density under saturated soil conditions may vary between 50% and 90% and tends to be closer to 95%. Under dry soil conditions, the relative density of the embankment can vary from 30% to 70% and tends to approach 76%. For model validation, the relative density estimated from shear wave velocity-depth profiles was compared to that estimated from DCPI data. In other words, the results analyzed in the context of an effective stress-depth model enable the prediction of engineering properties such as the small-strain stiffness and relative density of embankment layers. This study demonstrates that physics-based data analyses successfully capture the relative density of non-compacted embankment layers.

• Steel and Composite Structures
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• v.52 no.6
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• pp.621-626
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• 2024

The work researched the application of artificial intelligence to the design and analysis of advanced nanoplates, with a particular emphasis on size and surface effects. Employing an integrated theoretical framework, this study developed a more accurate model of complex nanoplate behavior. The following analysis considers nanoplates embedded in a Pasternak viscoelastic fractional foundation and represents the important step in understanding how nanoscale structures may respond under dynamic loads. Surface effects, significant for nanoscale, are included through the Gurtin-Murdoch theory in order to better describe the influence of surface stresses on the overall behavior of nanoplates. In the present analysis, the modified couple stress theory is utilized to capture the size-dependent behavior of nanoplates, while the Kelvin-Voigt model has been incorporated to realistically simulate the structural damping and energy dissipation. This paper will take a holistic approach in using sinusoidal shear deformation theory for the accurate replication of complex interactions within the nano-structure system. Addressing different aspectsof the dynamic behavior by considering the length scale parameter of the material, this work aims at establishing which one of the factors imposes the most influence on the nanostructure response. Besides, the surface stresses that become increasingly critical in nanoscale dimensions are considered in depth. AI algorithms subsequently improve the prediction of the mechanical response by incorporating other phenomena, including surface energy, material inhomogeneity, and size-dependent properties. In these AI- enhanced solutions, the improvement of precision becomes considerable compared to the classical solution methods and hence offers new insights into the mechanical performance of nanoplates when applied in nanotechnology and materials science.

• Journal of the Korean GEO-environmental Society
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• v.13 no.11
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• pp.51-60
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• 2012

In order to characterize the shear strength of the frozen sand for foundation design in cold region and prediction of adfreeze bond strength, many researchers developed test techniques and carried out many tests to analyze shear strength properties of the frozen sand for half a century. However, many studies for shear strength properties of the frozen sand have been carried out with limited circumstances, even though shear strength of the froze sand can be affected by various influence factors such as soil type, temperature conditions, and magnitude of normal stress. In this study, direct shear test equipment was used to analyze the shear strength characteristics of the frozen sand. Direct shear test equipment was designed for cold weather, and the direct shear tests were carried out inside of large-scaled low temperature chamber. Three soil types-two uniform sands and one well graded soil were used to analyze the shear strength of the frozen sand with three different temperature conditions and three different vertical confining pressures. In this research, a series of direct shear tests for shear strength of the frozen sand have been conducted to demonstrate the efficiency of effectiveness of the test equipment and low temperature chamber. This research also showed that shear strength of the froze sand increased with decreasing temperature condition, but the influence of vertical confining pressure was insignificant to the shear strength of the frozen sand.

• Food Science and Preservation
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• v.20 no.6
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• pp.894-898
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• 2013

This study was performed to provide the basic data for the prediction of the usefulness of onion with cold tolerance cultivated in Kangwon. The physicochemical properties and antioxidant activity of freeze-dried and hot-air-dried (40 and $60^{\circ}C$) onions were investigated. The moisture content of the raw onion was 90.55%. The crude protein and crude fat contents of the freeze-dried onions were slightly higher than those of the hot-air-dried onions ($40^{\circ}C$ and $60^{\circ}C$). As for the color values, the freeze-dried onion powder was highest in lightness (77.19), and the $60^{\circ}C$ hot air-dried onion was highest in redness (6.09) and yellowness (24.60). Moreover, the color difference (${\Delta}E$) between the freeze-dried and hot-air-dried ($40^{\circ}C$ and $60^{\circ}C$) onion powders was significant. The brown index was lower in the freeze-dried onion than in both hot-air-dried onions. The total phenol content and the 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) scavenging activity of both hot-air-dried onions were higher than those of the freeze-dried onion. These results indicate that the freeze-drying methods protected the physicochemical properties of the onion powder, whereas the hot-air-drying method enhanced the antioxidant activity and the total phenol content of the onion powder.