• Structural Engineering and Mechanics
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• 제82권5호
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• pp.571-585
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• 2022

As a vital component of power grids, long-span transmission tower-line systems are vulnerable to wind load excitation due to their high flexibility and low structural damping. Therefore, it is essential to reduce wind-induced responses of tower-line coupling systems to ensure their safe and reliable operation. To this end, a shape memory alloy-bidirectional tuned mass damper (SMA-BTMD) is proposed in this study to reduce wind-induced vibrations of long-span transmission tower-line systems. A 1220 m Songhua River long-span transmission system is selected as the primary structure and modeled using ANSYS software. The vibration suppression performance of an optimized SMA-BTMD attached to the transmission tower is evaluated and compared with the effects of a conventional bidirectional tuned mass damper. Furthermore, the impacts of frequency ratios and SMA composition on the vibration reduction performance of the SMA-BTMD are evaluated. The results show that the SMA-BTMD provides superior vibration control of the long-span transmission tower-line system. In addition, changes in frequency ratios and SMA composition have a substantial impact on the vibration suppression effects of the SMA-BTMD. This research can provide a reference for the practical engineering application of the SMA-BTMD developed in this study.

• 한국초전도ㆍ저온공학회논문지
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• 제23권4호
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• pp.35-38
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• 2021

Conventional direct current (DC) rotating machines are usually used for crane and press machine using high torque in metal and steel industries, because of a constant output power along variable rotating speed. A general DC motor with permanent field magnets could not increase a magnetic flux density at a gap between armature coils and field magnets. However, a superconducting DC motor has field magnets composed with high temperature superconducting (HTS) coils and it could increase the magnetic flux density at the gap to over 10 times than those of a general DC motor by control the excitation current into HTS coils. The superconducting DC motor could be operated with extremely high torque and constant output power at a low rotational speed. In this paper, a 500 W superconducting DC rotating machine was conceptually designed with a LN₂ (Liquid Nitrogen) cooling method and the operation characteristics results of HTS field magnets were presented. The two no-insulation HTS magnets for a 500 W superconducting DC rotating machine were fabricated. The excitation current for the HTS magnets could be controlled from 0 to 40 A. This test results will be available to design large-sized HTS magnets for a number of hundred kW class superconducting DC rotating machine under LN₂ cooling system.

• Smart Structures and Systems
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• 제29권4호
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• pp.589-598
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• 2022

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.

• Smart Structures and Systems
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• 제29권1호
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• pp.207-220
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• 2022

Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.

• Computers and Concrete
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• 제31권1호
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• pp.71-84
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• 2023

In this paper, the effects of the thickness of cubic samples on the tensile strength of concrete blocks were studied using experimental tests in the laboratory and numerical simulation by the particle flow code in three dimensions (PFC3D). Firstly, the physical concrete blocks with dimensions of 150 mm×190 mm (width×height) were prepared. Then, three specimens for each of seven different samples with various thicknesses were built in the laboratory. Simultaneously with the experimental tests, their numerical simulations were performed with PFC3D models. The widths, heights, and thicknesses of the numerical models were the same as those of the experimental samples. These samples were tested with a new tensile testing apparatus. The loading rate was kept at 1 kg/sec during the testing operation. Based on these analyses, it is concluded that when the thickness was less than 5 cm, the tensile strength decreased by increasing the sample thickness. On the other hand, the tensile strength was nearly constant when the sample thickness was raised to more than 5 cm (which can be regarded as a threshold limit for the specimens' thickness). The numerical outputs were similar to the experimental results, demonstrating the validity of the present analyses.

• Computers and Concrete
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• 제32권3호
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• pp.263-286
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• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

• 한국정밀공학회지
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• 제12권9호
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• pp.156-166
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• 1995

This paper presents a bin picking method using a slit beam laser in which a robot recognizes all of the unoccluded objects from the top of jumbled objects, and picks them up one by one. Once those unoccluded objects are removed, newly developed unoccluded objects underneath are recognized and the same process is continued until the bin gets empty. To recognize unoccluded objects, a new algotithm to link edges on slices which are generated by the orthogonally mounted laser on the xy table is proposed. The edges on slices are partitioned and classified using convex and concave function with a distance parameter. The edge types on the neighborhood slices are compared, then the hamming distances among identical kinds of edges are extracted as the features of fuzzy membership function. The sugeno fuzzy integration about features is used to determine linked edges. Finally, the pick-up sequence based on MaxMin theory is determined to cause minimal disturbance to the pile. This proposed method may provide a solution to the automation of part handling in manufacturing environments such as in punch press operation or part assembly.

• Geomechanics and Engineering
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• 제33권3호
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• pp.271-277
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• 2023

This article investigates the effect of viscoelastic foundations on the waves' dispersion in a beam made of ceramic-metal functionally graded material (FGM) with microstructural defects. The beam is considered to be shear deformable, and a simple three-unknown sinusoidal integral higher-order shear deformation beam theory is applied to represent the beam's displacement field. Novel to this study is the investigation of the impact of viscosity damping on imperfect FG beams, utilizing a few-unknowns theory. The stresses and strains are obtained using the two-dimensional elasticity relations of FGM, neglecting the normal strain in the beam's depth direction. The variational operation is employed to define the dispersion relations of the FGM beam. The influences of the material gradation exponent, the beam's thickness, the porosity, and visco-Pasternak foundation parameters are represented. Results showed that phase velocity was inversely proportional to the damping and porosity of the beams. Additionally, the foundation viscous damping had a stronger influence on wave velocity when porosity volume fractions were low.

• Advances in environmental research
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• 제12권1호
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• pp.17-40
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• 2023

This article presents studies of the adsorption process in a continuous system of fluoride solutions at a concentration of 30 mg/L using a bone char packed in fixed-bed columns, as well as regeneration studies in the same system using HNO₃, HCl and NaOH at 0.01, 0.1 and 1 M. The Thomas Model, Artificial Neural Networks (ANNs), Numerical Integration and Mass Transfer Zone were used for the modeling of asyemmetrical breakthrough curves obtained from the fluoride adsorption on bone char. The maximum adsorption capacity of the breakthrough curves was estimated, and various design parameters of the columns were obtained for the different operating conditions. Results showed that an improvement in the modeling capabilities of the Thomas model can be obtained using ANNs. Moreover, ANNs are useful for determining reasonable and accurate design parameters of packed-bed adsorption columns. This modeling approach can be useful for the process system engineering of dynamic adsorption systems involved in the field of water treatment and purification. It is important to highlight that the obtained results indicate that, when using HCl or HNO₃ at a concentration of 0.1 M, a large number of adsorption-desorption cycles are obtained and, therefore, the highest values of adsorption capacity, which leads to a reduction in operation costs.

• Advances in nano research
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• 제14권4호
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• pp.355-362
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• 2023

Physical exercise, especially intense exercise and high intensity interval training (HIIT) by trampoline, can lead to muscle injuries. These effects can be reduced with intelligent products made of nanocomposite materials. Most of these nanocomposites are polymers reinforced with silicon dioxide, alumina, and titanium dioxide nanoparticles. This study presents a polymer nanocomposite reinforced with silica. As a result of the rapid reaction between tetraethyl orthosilicate and ammonia in the presence of citric acid and other agents, silica nanostructures were synthesized. By substituting bis (4-amino phenoxy) phenyl-triptycene in N, N-dimethylformamide with potassium carbonate, followed by catalytic reduction with hydrazine and Pd/C, the diamine monomer bis (4-amino phenoxy) phenyl-triptycene is prepared. We synthesized a new polyaromatic (imide) with triptycene unit by sol-gel method from aromatic diamines and dianhydride using pyridine as a condensation reagent in NMP. PI readily dissolves in solvents and forms robust and tough polymer films in situ. The FTIR and NMR techniques were used to determine the effects of SiO₂ on the sol-gel process and the structure of the synthesized nanocomposites. By using a simultaneous thermal analysis (DTA-TG) method, the appropriate thermal operation temperature was also determined. Through SEM analysis, the structure, shape, size, and specific surface area of pores were determined. Analysis of XRD results is used to determine how SiO₂ affects the crystallization of phases and the activation energy of crystallization.