• 인터넷정보학회논문지
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• 제24권4호
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• pp.137-144
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• 2023

The purpose of this study is to examine consumers' perceptions of domestic infertility support policies based on infertility-related keywords and the trends of their changes. To this end, Momsholic, a mom cafe which has the most active infertility-related bulletin boards on Naver, was selected as the analysis target, and 'infertility' was selected as a keyword for data search. The data was collected for three months. In addition, network analysis and visualization were performed using R for data collection and analysis, and cross-validation was attempted using the NetDraw function of 'textom 1.0' and the UCINET6 program. As a result of the analysis, the main keywords were cost, artificial insemination, in vitro fertilization, freezing, harvest, ovulation, and how much. Next, looking at the central value of the degree of connection, it was found that the degree of connection between the words cost, cost, how much, problem, public health center, and artificial insemination was high. According to the results of this study, women who visit mom cafes due to infertility in Korea are more interested in the cost. It is believed to be closely related to infertility treatment as well as in vitro fertilization and egg freezing. Therefore, by examining keywords related toinfertility, it has academic significance in that it is possible to identify major factors that end users are interested in. Furthermore, it is possible to redefine the guidelines for domestic infertility support policies by presenting infertility support policies that reflect the factors of interest of end consumers.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Restorative Dentistry and Endodontics
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• 제46권3호
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• pp.40.1-40.12
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• 2021

Objectives: The purpose of this study was to critically evaluate the quality, reliability and educational content of the information of vital pulp treatment videos available on YouTube. Materials and Methods: The keywords "pulpotomy" and "pulp capping" were searched on YouTube on 5th July 2020, until 60 English language videos of each search term with a duration shorter than 15 minutes were acquired. Video characteristics were recorded and Video Power Index (VPI) was calculated. Reliability and educational quality of videos were evaluated using the Modified DISCERN score, the Journal of American Medical Association (JAMA) benchmark criteria and Global Quality Scores (GQS). Videos were categorized by uploading source. Results: Regarding pulpotomy, 31.7% of the videos were uploaded by specialists and 68.3% were directed by non-specialists. In the case of pulp capping, the corresponding percentages were 45% and 55%, respectively. Videos uploaded by specialists had significantly higher modified DISCERN, JAMA and GQS scores compared to those uploaded by non-specialists. Endodontists tended to have the highest reliability and VPI scores. Conclusions: YouTube videos on vital pulp treatment contain low educational quality or incomplete information. Low popularity of dental pulp capping and pulpotomy videos may be attributed to the specialized nature of these procedures. As YouTube represents an important source for patient information about different health topics, reliable informative videos should be uploaded by specialized dental professionals.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.206-206
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• 2021

The necessity for appropriate management of water resources infrastructures such as reservoirs, levees, and dikes is increasing due to unexpected hydro-climate irregularities and rising water demands. To meet this need, past studies have focused on advancing theoretical optimization algorithms such as nonlinear programming, dynamic programming (DP), and genetic programming. Yet, the optimally derived theoretical solutions are limited to be directly implemented in making release decisions in the real-world systems for a variety of reasons. This study first aims to comparatively analyze the two prominent optimization methods, DP and evolutionary multi-objective direct policy search (EMODPS), under historical inflow series using K-fold cross validation. A total of six optimization models are formed each with a specific formulation. Then, one of the optimization models was coupled with the actual zone-based hedging rule that has been adopted in practice. The proposed methodology was applied to Boryeong Dam located in South Korea with conflicting objectives between supply and demand. As a result, the EMODPS models demonstrated a better performance than the DP models in terms of proximity to the ideal. Moreover, the incorporation of the real-world policy with the optimal solutions improved in all indices in terms of the supply side, while widening the range of the trade-off between frequency and magnitude measured in the sides of demand. The results from this study once again highlight the necessity of closing the gap between the theoretical solutions with the real-world implementable policies.

• 한국공간구조학회논문집
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• 제23권4호
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Structural Engineering and Mechanics
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• 제90권4호
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• pp.391-401
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• 2024

The present study focuses on the effect of extension-bending coupling on the elastic stability (buckling) of laminated composite plates. These plates will be loaded under uni-axial or bi-axial in-plane mechanical loads, especially in the orthotropic or anti-symmetric cross-angle cases. The main objective is to find a limit where we can approximate the elastic stability behavior of angularly crossed anti-symmetric plates by the simple behavior of specially orthotropic plates. The contribution of my present study is to predict the explicit effect of extension-flexion coupling on the elastic stability of this type of panel. Critically, a parametric study is carried out, involving the search for the critical buckling load as a function of deformation mode, aspect ratio, plate anisotropy ratio and finally the study of the effect of lamination angle and number of layers on the contribution of extension-flexure coupling in terms of plate buckling stability. We use first-order shear deformation theory (FSDT) with a correction factor of 5/6. Simply supported conditions along the four boundaries are adopted where we can develop closed-form analytical solutions obtained by a Navier development.

• Earthquakes and Structures
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• 제27권3호
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• pp.177-189
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• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• Wind and Structures
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• 제39권3호
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• pp.175-190
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• 2024

Aerodynamic shape optimization is very useful for enhancing the performance of wind-sensitive structures. However, shape parameterization, as the first step in the pipeline of aerodynamic shape optimization, still heavily depends on empirical judgment. If not done properly, the resulting small design space may fail to cover many promising shapes, and hence hinder realizing the full potential of aerodynamic shape optimization. To this end, developing a novel shape parameterization scheme that can reflect real-world complexities while being simple enough for the subsequent optimization process is important. This study proposes a machine learning-based scheme that can automatically learn a low-dimensional latent representation of complex aerodynamic shapes for bluff-body wind-sensitive structures. The resulting latent representation (as design variables for aerodynamic shape optimization) is composed of both discrete and continuous variables, which are embedded in a hierarchy structure. In addition to being intuitive and interpretable, the mixed discrete and continuous variables with the hierarchy structure allow stakeholders to narrow the search space selectively based on their interests. As a proof-of-concept study, shape parameterization examples of tall building cross sections are used to demonstrate the promising features of the proposed scheme and guide future investigations on data-driven parameterization for aerodynamic shape optimization of wind-sensitive structures.

• Geomechanics and Engineering
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• 제38권5호
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• pp.507-515
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• 2024

An accurate estimation of the geotechnical parameters in front of tunnel faces is crucial for the safe construction of underground infrastructure using tunnel boring machines (TBMs). This study was aimed at developing a data-driven model for predicting the rock quality designation (RQD) of the ground formation ahead of tunnel faces. The dataset used for the machine learning (ML) model comprises seven geological and mechanical features and 564 RQD values, obtained from an earth pressure balance (EPB) shield TBM tunneling project beneath the Han River in the Republic of Korea. Four ML algorithms were employed in developing the RQD prediction model: k-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). The grid search and five-fold cross-validation techniques were applied to optimize the prediction performance of the developed model by identifying the optimal hyperparameter combinations. The prediction results revealed that the RF algorithm-based model exhibited superior performance, achieving a root mean square error of 7.38% and coefficient of determination of 0.81. In addition, the Shapley additive explanations (SHAP) approach was adopted to determine the most relevant features, thereby enhancing the interpretability and reliability of the developed model with the RF algorithm. It was concluded that the developed model can successfully predict the RQD of the ground formation ahead of tunnel faces, contributing to safe and efficient tunnel excavation.