• Earthquakes and Structures
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• v.24 no.2
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• pp.127-140
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• 2023

The vibration of microtubule in human cells is the source of electrical field around it and inside cell structure. The induction of electrical field is a direct result of the existence of dipoles on the surface of the microtubules. Measuring the electrical fields could be performed using nano-scale sensors and the data could be transformed to other computers using internet of things (IoT) technology. Processing these data is feasible by artificial intelligence-based methods. However, the first step in analyzing the vibrational behavior is to study the mechanics of microtubules. In this regard, the vibrational behavior of the microtubules is investigated in the present study. A shell model is utilized to represent the microtubules' structure. The displacement field is assumed to obey first order shear deformation theory and classical theory of elasticity for anisotropic homogenous materials is utilized. The governing equations obtained by Hamilton's principle are further solved using analytical method engaging Navier's solution procedure. The results of the analytical solution are used to train, validate and test of the deep neural network. The results of the present study are validated by comparing to other results in the literature. The results indicate that several geometrical and material factors affect the vibrational behavior of microtubules.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.1258-1270
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• 2009

Recently, the earthquakes activities are more of frequency occurred in the country. In case of nomal or large magnitude earthquakes, which cause a rising number of life loss or widespread loss of property. It must be considered how to cope with the situperty of dpmage in the country ty account of ay earthquake. Consequently, the public works have currently ensured against a lot of risk about seismism not only on large scale structures but also relatively small structures. Therefore, in this study, in order to make the seismic stability safe, it has been evaluated by the seismic performance for caisson-type breakwater. The seismic response analyses have conducted on the caisson-type breaker under long-period, short-period and artificial seismic wave. The liquefaction potential of the foundation, which is caisson-type, is also estimated by using the simplified assessment method. Finally, the result of the numerical analysis by PENTAGON 2D finite element method(FEM) program are presented for 3 cases with time-history seismic analysis under the seismic load.

• Earthquakes and Structures
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• v.24 no.1
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• pp.1-9
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• 2023

A model for calculating structure interacted mechanics is proposed. A structural interaction model and controller design based on tuned mass damping (TMD) was developed to control the induced vibration. A key point is to introduce a new analytical model to evaluate the properties of the TMD that recognizes the motion-dependent nonlinear response observed in the simulations. Aiming at the problem of increased current harmonics and low efficiency of permanent magnet synchronous motors for electric vehicles due to dead time effect, a dead time compensation method based on neural network filter and current polarity detection is proposed. Firstly, the DC components and the higher harmonic components of the motor currents are obtained by virtue of what the neural network filters and the extracted harmonic currents are adjusted to the required compensation voltages by virtue of what the neural network filters. Then, the extracted DC components are used for current polarity dead time compensation control to avert the false compensation when currents approach zero. The neural network filter method extracts the required compensation voltages from the speed component and the current polarity detection compensation method obtains the required compensation voltages by discriminating the current polarity. The combination of the two methods can more precisely compensate the dead time effect of the control system to improve the control performance. Furthermore, based on the relaxed method, the intelligent approach of stability criterion can be regulated appropriately and the artificial TMD was found to be effective in reducing cross-wind vibrations.

• Journal of the Earthquake Engineering Society of Korea
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• v.2 no.4
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• pp.1-10
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• 1998

The magnitudes of historical earthquake records related with house collapses are estimated considering the magnitude, epicentral distance, soil condition and aging of a house. Eighteen artificial time histories for magnitudes 6-8, epicentral distances 5 km-350 km and hard and soft soil condition were generated. Nonlinear dynamic analyses were performed for a traditional three-bay-straw-roof house. The aging effect of the house was modeled as such that the lateral loading capacity of wooden frames represented by hysteretic stiffness was decreased linearly with time. The house was idealized by one degree-of-freedom lumped mass model and the nonlinear characteristics of wooden frames were modeled by the Modified Double-Target mode. For far field earthquakes, minor damages were identified regardless of magnitude, soil condition and aging of the house. For intermediate field earthquake, earthquake magnitude greater than 6.5 caused severe damages in soil sites. For near field earthquake, severe damages occurred for magnitude greater than 6.5 regardless of soil condition and aging of the house. It is estimated that the magnitude of historical earthquakes is about 6.2. An empirical equation of magnitude-intensity relationship suitable to Korea is suggested.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.592-599
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• 2020

This paper proposes a Convolutional Recurrent Neural Net (CRNN) structure that can simultaneously reflect both static and dynamic characteristics of seismic waveforms for various earthquake events classification. Addressing various earthquake events, including not only micro-earthquakes and artificial-earthquakes but also macro-earthquakes, requires both effective feature extraction and a classifier that can discriminate seismic waveform under noisy environment. First, we extract the static characteristics of seismic waveform through an attention-based convolution layer. Then, the extracted feature-map is sequentially injected as input to a multi-input single-output Long Short-Term Memory (LSTM) network structure to extract the dynamic characteristic for various seismic event classifications. Subsequently, we perform earthquake events classification through two fully connected layers and softmax function. Representative experimental results using domestic and foreign earthquake database show that the proposed model provides an effective structure for various earthquake events classification.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.5
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• pp.413-419
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• 2016

Damage of infrastructures by an earthquake causes the secondary damage through the world at large more than the damage of the structures themselves. Amomg them, underground utility tunnel structures comes under the special life line: communication, gas, electricity and etc. and it has a need to evaluate its fragility to an earthquake exactly. Therefore, the destruction ability according to peak ground acceleration of earthquakes for the underground utility tunnels is evaluated in this paper. As an input ground motion for evaluating seismic fragilities, real earthquakes and artificial seismic waves which could be generated in the Korean peninsula are used. And as a seismic analysis method, response displacement method and time history analyzing method are used. An limit state which determines whether destruction is based on the bending moment and shear deformation. A method used to deduct seismic fragility curve is method of maximum likelihood and the distribution function is assumed to the log normal distribution. It could evaluate the damage of underground utility tunnels to an earthquake and could be applied as basic data for seismic design of underground utility tunnel structures.

• Earthquakes and Structures
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• v.19 no.5
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• pp.347-359
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• 2020

The Mega-Sub Controlled Structure System (MSCSS), an innovative vibration passive control system for building structures, is improved by adding lead rubber bearings (LRBs) on top of the substructure. For the new system, a genetic algorithm is used to optimize the dynamic parameters and distributions of dampers and LRBs. The program uses various seismic performance indicators as optimization objectives, and corresponding results are compared. It is found that the optimization procedure for maximizing the energy dissipation ratio yields the best solutions, and optimized models have consistent seismic performances under different earthquakes. Seismic performances of optimized MSCSS models with and without LRBs, as well as the traditional Mega-Sub Structure model, are evaluated and compared under El Centro wave, Taft wave and 20 other artificial waves. In both elastic and plastic analysis, the model with LRBs shows significantly smaller story drift and horizontal acceleration than those of the other two models, and fewer plastic hinges are developed during severe earthquakes. Energy analysis also shows that LRBs installed in proper locations increase the deformation and energy dissipation of dampers, thereby significantly reduce the kinetic, potential, and hysteretic energy in the structure. However, LRBs do not have to be mounted on all the additional columns. It is also demonstrated that LRBs at unfavorable locations can decrease the energy dissipation for dampers. After LRBs are installed, the optimal damping coefficient and the optimal damping exponent of dampers are reduced to produce the best damping effect.

• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Structural Engineering and Mechanics
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• v.74 no.6
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• pp.789-807
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• 2020

Multiple earthquakes that occur during short seismic intervals affect the inelastic behavior of the structures. Sequential ground motions against the single earthquake event cause the building structure to face loss in stiffness and its strength. Although, numerous research studies had been conducted in this research area but still significant limitations exist such as: 1) use of traditional design procedure which usually considers single seismic excitation; 2) selecting a seismic excitation data based on earthquake events occurred at another place and time. Therefore, it is important to study the effects of successive ground motions on the framed structures. The objective of this study is to overcome the aforementioned limitations through testing a two storey RC building structural model scaled down to 1/10 ratio through a similitude relation. The scaled model is examined using a shaking table. Thereafter, the experimental model results are validated with simulated results using ETABS software. The test framed specimen is subjected to sequential five artificial and four real-time earthquake motions. Dynamic response history analysis has been conducted to investigate the i) observed response and crack pattern; ii) maximum displacement; iii) residual displacement; iv) Interstorey drift ratio and damage limitation. The results of the study conclude that the low-rise building model has ability to resist successive artificial ground motion from its strength. Sequential artificial ground motions cause the framed structure to displace each storey twice in correlation with vary first artificial seismic vibration. The displacement parameters showed that real-time successive ground motions have a limited impact on the low-rise reinforced concrete model. The finding shows that traditional seismic design EC8 requires to reconsider the traditional design procedure.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.201-207
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• 2017

Failure of the power apparatus causes many inconveniences and problems due to power outage in all places using power such as industry and home. The main causes of faults in the Power Apparatus are aging, natural disasters such as typhoons and earthquakes, and animals. At present, the long high temperature status is monitored only by the assumption that a fault occurs when the temperature of the power apparatus becomes higher. Therefore, it is difficult to cope with the failure of the power apparatus at the right time. In this paper, we propose a power apparatus monitoring system as an efficient countermeasure against general faults except for faults caused by sudden natural disasters. The proposed monitoring system monitors the power apparatus in real time by attaching a thermal sensor, collects the monitored data, and predicts the failure using the accumulated information through learning using the artificial neural network. Through the learning and experimentation of artificial neural network, it is shown that the proposed method is efficient.