• Advances in concrete construction
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• v.13 no.1
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• pp.45-69
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• 2022

This paper investigates the shear behavior of reinforced concrete haunched beams (RCHBs) without stirrups. The research objective is to study the effectiveness of the ideal steel fiber (SF) ratio, which is used to resist shear strength, besides the influence of main steel reinforcement, compressive strength, and inclination angles of the haunched beam. The modeling and analysis were carried out by Finite Element Method (FE) based on a software package, called Atena-GiD 3D. The program of this study comprises two-part. One of them consists of nine results of experimental SF RCHBs which are used to identify the accuracy of FE models. The other part comprises 81 FE models, which are divided into three groups. Each group differed from another group by the area of main steel reinforcement (A_s) which are 226, 339, and 509 mm². The other parameters which are considered in each group in the same quantities to study the effectiveness of them, were steel fiber volumetric ratios (0.0, 0.5, and 1.0)%, compressive strength (20.0, 40.0, 60.0) MPa, and the inclination angle of haunched beam (0.0°, 10.0°, and 15.0°). Moreover, the parametric analysis was carried out on SF RCHBs to clarify the effectiveness of each parameter on the mechanical behavior of SF RCHBs. The results show that the correlation coefficient (R²) between shear load capacities of FE proposed models and shear load capacities of experimental SF RCHBs is 0.9793, while the effective inclination angle of the haunched beam is 10° which contributes to resisting shear strength, besides the ideal ratio of steel fibers is 1% when the compressive strength of SF RCHBs is more than 20 MPa.

• Journal of Digital Policy
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• v.1 no.2
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• pp.61-69
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• 2022

The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R²) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

• Journal of Wetlands Research
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• v.24 no.1
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• pp.52-58
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• 2022

Recently, various efforts and research are being conducted to integrated management of the estuary in Nakdong River. As one of such studies, measurement of opening discharge amount for each floodgate using a three-dimensional fixed ultrasonic flow meter is being conducted, but studies on hydraulic and statistical processing procedures and techniques using actual measurement results for calculating discharge amount by opening remain at the basic level. Therefore, in this study, a data processing technique using three-dimensional fixed ultrasonic flow meter measurement data was developed, the flow coefficient was calculated based on the measured data, and the applicability of the discharge amount calculation formula development was reviewed.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.1
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• pp.80-91
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• 2022

This study is to provide an prediction model of setting time of super retarding mortar based on equivalent age method under various super retarding agent contents, curing temperature, and water-binder ratio (W/B). An equation for predicting setting time using maturity was proposed. Test results indicated that the setting time can be predicted by determining the curing temperature, W/B, and super retarding agent contents and substituting it into the equation proposed in this study. The coefficient of determination of the equation is 0.9 or more, and the reliability was confirmed through the F-test. Finally, using the equation proposed in this paper, reasonable quality control is possible regarding the setting of super retarding concrete in practice.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.239-248
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• 2023

This study is a descriptive correlation study to identify the relationship among online collaborating learning attitudes, empathy, critical thinking disposition, and sense of community in nursing students in the online learning environment during COVID-19. Data were collected from 129 nursing students. The SPSS/28.0 program was used to analyze the data using descriptive statistics, Pearson's correlation coefficient, independent t-test, one-way ANOVA, Scheffé test, Pearson's correlation analysis, and hierarchical multiple regression. Factors that significantly affect sense of community in nursing students were online collaborating learning attitudes and critical thinking disposition, and the explanatory power was about 42.2%. Based on these results, in order to increase sense of community of nursing students in non-face-to-face learning environment, it is required to strengthen the problem-solving-centered learning method cultivating the online collaborating learning attitude and critical thinking disposition.

• Structural Engineering and Mechanics
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• v.86 no.2
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• pp.167-180
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• 2023

This work applies a four-known quasi-3D shear deformation theory to investigate the bending behavior of a functionally graded plate resting on a viscoelastic foundation and subjected to hygro-thermo-mechanical loading. The theory utilizes a hyperbolic shape function to predict the transverse shear stress, and the transverse stretching effect of the plate is considered. The principle of virtual displacement is applied to obtain the governing differential equations, and the Navier method, which comprises an exponential term, is used to obtain the solution. Novel to the current study, the impact of the viscoelastic foundation model, which includes a time-dependent viscosity parameter in addition to Winkler's and Pasternak parameters, is carefully investigated. Numerical examples are presented to validate the theory. A parametric study is conducted to study the effect of the damping coefficient, the linear and nonlinear loadings, the power-law index, and the plate width-tothickness ratio on the plate bending response. The results show that the presence of the viscoelastic foundation causes an 18% decrease in the plate deflection and about a 10% increase in transverse shear stresses under both linear and nonlinear loading conditions. Additionally, nonlinear loading causes a one-and-a-half times increase in horizontal stresses and a nearly two-times increase in normal transverse stresses compared to linear loading. Based on the article's findings, it can be concluded that the viscosity effect plays a significant role in the bending response of plates in hygrothermal environments. Hence it shall be considered in the design.

• Animal Bioscience
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• v.36 no.7
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• pp.1003-1009
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• 2023

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. Results: The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. Conclusion: The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

• Journal of the Korean Geotechnical Society
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• v.25 no.11
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• pp.87-103
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• 2009

To determine the reliable probabilistic distribution model of geotechnical properties, outlier and randomness test for analysis data, parameter estimation of probabilistic distribution model, and goodness-of-fit test for model parameter and probabilistic distribution model have to be performed in sequence. In this paper, the probabilistic distribution model's geotechnical properties of Songdo area in Incheon are estimated by the above proposed procedure. Also, the coefficient of variation (COV) representing the variability of geotechnical properties is determined for several geotechnical properties. Reliable probabilistic distribution model and COV of geotechnical properties can be used for probability-based design procedure and reasonable choice of design value in deterministic design method.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.2-41.2
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• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.