• International Journal of Highway Engineering
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• v.19 no.4
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• pp.1-7
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• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.8
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• pp.627-635
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• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

• Journal of Magnetics
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• v.19 no.4
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• pp.315-322
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• 2014

In this work, Artificial Neural Network (ANN) was used to model the dynamic behavior of ferromagnetic hysteresis derived from performing the mean-field analysis on the Ising model. The effect of field parameters and system structure (via coordination number) on dynamic critical points was elucidated. The Ising magnetization equation was drawn from mean-field picture where the steady hysteresis loops were extracted, and series of the dynamic critical points for constructing dynamic phase-diagram were depicted. From the dynamic critical points, the field parameters and the coordination number were treated as inputs whereas the dynamic critical temperature was considered as the output of the ANN. The input-output datasets were divided into training, validating and testing datasets. The number of neurons in hidden layer was varied in structuring ANN network with highest accuracy. The network was then used to predict dynamic critical points of the untrained input. The predicted and the targeted outputs were found to match well over an extensive range even for systems with different structures and field parameters. This therefore confirms the ANN capabilities and indicates the ANN ability in modeling the ferromagnetic dynamic hysteresis behavior for establishing the dynamic-phase-diagram.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.987-997
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• 2008

Successful design, construction and maintenance of geotechnical structure in soft ground and marine clay demands prediction, control, stability estimation and monitoring of settlement with high accuracy. It is important to predict and to estimate the compression index of soil for predicting of ground settlement. Lab. and field tests have been and are indispensable tools to achieve this goal. In this paper, Artificial Neural Networks (ANNs) model with Levenberg-Marquardt Algorithm and field database were used to predict compression index of soil in Korea. Based on soil property database obtained from more than 1800 consolidation tests from soils samples, the ANNs model were proposed in this study to estimate the compression index, using multiple soil properties. The compression index from the proposed ANN models including multiple soil parameters were then compared with those from the existing empirical equations.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.948-955
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• 2005

In this case of study, Incheon International Airport 2nd phase site preparation 1st section estimated final settlement to improve soft ground. Final settlement is very important in preloading method. Recently, Hyperbolic method, Hoshino method and Asaoka method are used mostly in prediction of final settlement and this paper, Comparing a result of Final settlement, used to Artificial Neural Network. The structure of Dynamic Artificial Neural Network which predicted Final settlement, has application to Young_Jong Island other site, If new investigation data will be added. Also, It is expected to save measuring_system cost in soft ground.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.2
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• pp.393-406
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• 1993

In resistance spot welding process, size of molten nuggest have been utilized to assess the integrity of the weld quality. However real-time monitoring of the nugget size is an extremely difficult problem. This paper describes the design of an artificial neural networks(ANN) estimator to predict the nugget size for on-line use of weld quality monitoring. The main task of the ANN estimator is to realize the mapping characteristics from the sampled dynamic resistance signal to the actual negget size through training. The structure of the ANN estimator including the number of hidden layers and nodes in a layer is determined by an estimation error analysis. A series of welding experiments are performed to assess the performance of the ANN estimator. The results are quite promissing in that real-time estimation of the invisible nugget size can be achieved by analyzing the dynamic resistance signal without any conventional destructive testing of welds.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.5
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• pp.533-541
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• 2011

This paper presents a frequency-response test performed on an antagonistic actuation system consisting of two Mckibben pneumatic artificial muscles and a pneumatic circuit with pressure valves. Varying switching frequency to pressure valves from 0.1 Hz to 5 Hz, parameters of a linear model were estimated optimally to predict dynamic characteristics of the antagonistic actuation. A model-base control scheme with estimated parameters was built for the precise trajectory tracking of the antagonistic structure and realized on a reconfigurable embedded control system, CompactRIO. Experimental results showed that the proposed model-based control scheme gave good performance in trajectory tracking comparing with a PD control scheme when square wave and sinusoidal wave were given as references to follow.

• Earthquakes and Structures
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• v.15 no.4
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• pp.431-441
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• 2018

This study provides fragility-based assessment of seismic performance of reinforced concrete bridges. Seismic fragility curves were created using nonlinear analysis (NA) and artificial neural networks (ANNs). Nonlinear response history analyses were performed, in order to calculate the seismic performances of the bridges. To this end, 306 bridge-earthquake cases were considered. A multi-layered perceptron (MLP) neural network was implemented to predict the seismic performances of the selected bridges. The MLP neural networks considered herein consist of an input layer with four input vectors; two hidden layers and an output vector. In order to train ANNs, 70% of the numerical results were selected, and the remained 30% were employed for testing the reliability and validation of ANNs. Several structures of MLP neural networks were examined in order to obtain suitable neural networks. After achieving the most proper structure of neural network, it was used for generating new data. A total number of 600 new bridge-earthquake cases were generated based on neural simulation. Finally, probabilistic seismic safety analyses were conducted. Herein, fragility curves were developed using numerical results, neural predictions and the combination of numerical and neural data. Results of this study revealed that ANNs are suitable tools for predicting seismic performances of RC bridges. It was also shown that yield stresses of the reinforcements is one of the important sources of uncertainty in fragility analysis of RC bridges.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.10
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• pp.923-929
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• 2004

In this research, Forced Vibration Test(FVT) on a cable stayed bridge was conducted to examine the validity of the frequency domain pattern recognition method using signal anomaly index and artificial neuralnetwork. 7he considering structure, Samchunpo Bridge, located in Sachun-Shi, Kyungsangnam-Do, is a cable stayed bridge with the 436 meter span. The excitation force was induced by a sudden braking of a fully loaded truck. and vortical acceleration signals were acquired at 14 points. The initial 2-dimensional FE-model was developed from the design documents to prepare the training sets for the artificial neural network, and then the model calibration was performed with the field test data. As a result of the model calibration, we obtained the FFT spectrums from the model simulation, which was similar to those from the vibration test. These tests and the simulation data will be used for the structural identification using arbitrarily added masses to the bridge.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.619-623
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• 2003

In this research, Forced Vibration Test(FVT) on a cable stayed bridge was conducted to examine the validity of the frequency domain pattern recognition method using signal anomaly index and artificial neural network. The considering structure, Samchunpo Bridge, located in Sachun-Shi, Kyungsangnam-Do, is a cable stayed bridge with the 436 meter span. The excitation force was induced by a sudden braking of a fully loaded truck, and vertical acceleration signals were acquired at 14 points. The initial 2-dimensional FE-model was developed from the design documents to prepare the training sets for the artificial neural network, and then the model calibration was performed with the field test data. As a result of the model calibration, we obtained the FFT spectrums from the model simulation, which was similar to those from the vibration test. These tests and the simulation data will be used fur the structural identification using arbitrarily added masses to the bridge.