• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.2
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• pp.141-147
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• 2019

This research focuses on developing the Smart material with Grease adopted as a base oil to overcome a particle deposition caused by the MR fluid consisting of a silicon, which maximizing the characteristics and advantage of the MR fluid. By adopting the SMG fluid to a shear damper, this paper aimed to evaluate the control performance of it according to the variation of intensity of electric current(0 A, 0.5 A, 1.0 A, 1.5 A, 2.0 A, 2.5 A) and frequency(0.5 Hz, 1 Hz, 2 Hz). Subsequently, the usability of the SMG damper was analyzed by comparing the dynamic model of it to that of the other types of dampers(Power(Involution) Model, Bingham Model). As a result, DR, the performance indicator of semi-active damper, shows approximately 5 in a condition of 2 Hz. Also while confirming the excellent performance like the Power and the Bingham model, it raises the possibility to exploit it as the semi-active damper.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.4
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• pp.211-219
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• 2019

In this study, SMG(Smart Material with Grease) was developed, which was improved the precipitation minute particle in grease during long term standstill. Also, small-sized cylinder damper equipped with an electromagnet in a piston was developed for using a performance evaluation of the damper with SMG and the dynamic load test, and damping force using Power model and Bingham model was derived in order to compare to the result of that of the damper. The data obtained from the dynamic load test were analyzed and plotted, and then a dynamic range was calculated to evaluate the usability of the damper with SMG. The performance of the damper with SMG was compared to the damping forse derived from the Power and Bingham model. The result of this evaluation shown that the usability of SMG damper was demonstrated by this test as a semi-active controlling equipment of small-sized damper.

• Smart Structures and Systems
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• v.28 no.6
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• pp.827-838
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• 2021

The dynamic displacement is considered to be an important indicator of structural safety, and becomes an indispensable part of Structural Health Monitoring (SHM) system for high-speed railway bridges. This paper proposes an indirect strain based dynamic displacement reconstruction methodology for high-speed railway box girders. For the typical box girders under eccentric train load, the plane section assumption and elementary beam theory is no longer applicable due to the bend-torsion coupling effects. The monitored strain was decoupled into bend and torsion induced strain, pre-trained multi-output support vector regression (M-SVR) model was employed for such decoupling process considering the sensor layout cost and reconstruction accuracy. The decoupled strained based displacement could be reconstructed respectively using box girder plate element analysis and mode superposition principle. For the transformation modal matrix has a significant impact on the reconstructed displacement accuracy, the modal order would be optimized using particle swarm algorithm (PSO), aiming to minimize the ill conditioned degree of transformation modal matrix and the displacement reconstruction error. Numerical simulation and dynamic load testing results show that the reconstructed displacement was in good agreement with the simulated or measured results, which verifies the validity and accuracy of the algorithm proposed in this paper.

• Smart Structures and Systems
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• v.29 no.3
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• pp.433-444
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• 2022

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.

• Smart Structures and Systems
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• v.31 no.2
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• pp.155-169
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• 2023

The quasi-static component of the moving vehicle-induced dynamic response is promising in damage detection as it is sensitive to bridge damage but insensitive to environmental changes. However, accurate extraction of quasi-static component from the dynamic response is challenging especially when the vehicle velocity is high. This paper proposes an adaptive quasi-static component extraction method based on the modified variational mode decomposition (VMD) algorithm. Firstly the analytical solutions of the frequency components caused by road surface roughness, high-frequency dynamic components controlled by bridge natural frequency and quasi-static components in the vehicle-induced bridge response are derived. Then a modified VMD algorithm based on particle swarm algorithm (PSO) and mutual information entropy (MIE) criterion is proposed to adaptively extract the quasi-static components from the vehicle-induced bridge dynamic response. Numerical simulations and real bridge tests are conducted to demonstrate the feasibility of the proposed extraction method. The results indicate that the improved VMD algorithm could extract the quasi-static component of the vehicle-induced bridge dynamic response with high accuracy in the presence of the road surface roughness and measurement noise.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.693-699
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• 2024

The main cause of scattering dust generated by transportation equipment such as automobiles was largely due to exhaust gas from internal combustion engines in the past, but it was generally recognized that non-exhaust causes such as abrasion of the tires or brake pads were low. Accordingly, scattering dust generated by exhaust gas has consistently existed in many studies, such as technological progress and related regulations, but research on non-exhaust is relatively insignificant, and the need for research on scattering dust generated by non-exhaust is emerging. In this study, a dust collector that can be easily mounted on a caliper to collect scattering dust generated by pad wear during the brake operation of an automobile was manufactured. In this study, we developed a dust collector that is easy to mount on calipers to collect scattering dust caused by pad wear during brake operation of automobiles. According to the installation of the manufactured dust collector, the performance of scattering dust by brake operation and the temperature change characteristics of calipers according to the structure of the dust collector were evaluated.

• Smart Structures and Systems
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• v.25 no.4
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• pp.487-499
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• 2020

This paper proposes a novel approach to model updating for a large-scale cable-stayed bridge based on ambient vibration tests coupled with a hybrid metaheuristic search algorithm. Vibration measurements are carried out under excitation sources of passing vehicles and wind. Based on the measured structural dynamic characteristics, a finite element (FE) model is updated. For long-span bridges, ambient vibration test (AVT) is the most effective vibration testing technique because ambient excitation is freely available, whereas a forced vibration test (FVT) requires considerable efforts to install actuators such as shakers to produce measurable responses. Particle swarm optimization (PSO) is a famous metaheuristic algorithm applied successfully in numerous fields over the last decades. However, PSO has big drawbacks that may decrease its efficiency in tackling the optimization problems. A possible drawback of PSO is premature convergence leading to low convergence level, particularly in complicated multi-peak search issues. On the other hand, PSO not only depends crucially on the quality of initial populations, but also it is impossible to improve the quality of new generations. If the positions of initial particles are far from the global best, it may be difficult to seek the best solution. To overcome the drawbacks of PSO, we propose a hybrid algorithm combining GA with an improved PSO (HGAIPSO). Two striking characteristics of HGAIPSO are briefly described as follows: (1) because of possessing crossover and mutation operators, GA is applied to generate the initial elite populations and (2) those populations are then employed to seek the best solution based on the global search capacity of IPSO that can tackle the problem of premature convergence of PSO. The results show that HGAIPSO not only identifies uncertain parameters of the considered bridge accurately, but also outperforms than PSO, improved PSO (IPSO), and a combination of GA and PSO (HGAPSO) in terms of convergence level and accuracy.

• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.3
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• pp.15-25
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• 2023

In this study, silicon sludge from semiconductor dicing process is recycled to fabricate silica nanoparticles, which are applied as dispersing materials for electro-responsive (ER) smart fluid. In specific, metal impurities are removed from silicon sludge by acid washing to obtain the high-purity silicon powder. And then, silica nanoparticles are synthesized by facile hydrothermal method employing the silicon powder as reactant material. To control the size of silica nanoparticles, the reaction time of hydrothermal method is varied as 8, 15, 20, and 30 hours are applied to control the size of silica nanoparticles. Sizes of silica nanoparticles are increased proportionally to the reaction time owing to the increased numbers of hydrolysis and condensation reactions. As-synthesized silica nanoparticles are prepared as electro-responsive smart fluids by dispersing into silicon oil. Silica nanoparticles synthesized by 30 hours of hydrothermal reaction (SiO₂-H30) exhibit the highest shear stress of 21.4 Pa under an applied electric field strength of 3.0kV mm^-1. Such enhancement in ER performance of SiO₂-H30 among various silica nanoparticles are attribute to the reinforcing effect originated from the mixed particle size, which allowing the formation of rigid chain-like structures. Accordingly, this study successfully propose a recycling method of silicon sludge to synthesize silica nanoparticles and their derived ER fluids, which may suggest new possibility to ESG management emphasizing the eco-friendliness.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.7 s.144
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• pp.918-925
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• 2005

The following are the results from the infrared body temperature image test to verify the changes in facial temperature according to call duration with a cellular phone. As for the body temperatures, it appears to be the mean value at the upper central point of phone's battery among 7 different points that are measured, and to be the highest at srernocleido-mastoid and scapular trapezius muscle triangle zone$(34.25^{\circ}C\; and\;34.05^{\circ}C\;each)$. The changes of body temperature according to the time duration shows that the body temperature rises according to the length of phone use because of the heat emitted from the battery. As for the temperature changes according to blocking materials, the one without processing appears to be higher in the mean temperature compared to the others that are processed, NSS(Nano Silver Silk) and NSG(Nano Silver Silk Gold) appear to be the lowest in the temperature to show the best blocking property. As for the temperature changes according to measuring points, it appears to be the highest at P4, P5 with all materials, and one with NSG to be the lowest at Pl, P2, P3, and one with NSS to be the lowest at P3, P4, P5, P6, which is due to the thermal conduction of Au and Ag. And the mean temperature at each point appears to be different according to the materials. Therefore, the study conducted with human participants requires a proper particle size of it which would not penetrate cellular tissues and a proper binder and binding treatment for it, to prevent the physical fatigues and the potential diseases. However, it is highly required for back-up researches to verify various aspects in applying nano silver to textile products.

• Smart Structures and Systems
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• v.22 no.5
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.