• Advances in concrete construction
• /
• v.10 no.3
• /
• pp.185-193
• /
• 2020

An analytical model is developed in this paper to predict the shear capacity of reinforced concrete (RC) columns with corroded transverse reinforcements. The shear strength model for corroded RC columns is proposed based on modifying the existing strut-and-tie model, which considers the deformational compatibility between truss and arch mechanisms. The contributions to the shear strength from both truss and arch mechanisms are incorporated in the proposed model. The effects of corrosion level of transverse reinforcements are considered in the proposed model through the minimum residual cross-sectional area of transverse reinforcements and the reduction of concrete compressive strength for the cover area. The shear strengths calculated from the developed model are compared with the experimental results from Vu's study (2017), which consisted of RC columns with corroded transverse reinforcements showing shear failure under the cyclic loading. The comparison results indicate satisfactory correlations. Parametric studies are conducted based on the developed shear strength model to explore the effects of column axial loading, aspect ratios, transverse reinforcements and the corrosion levels in transverse reinforcements to the shear strength of RC columns with corroded transverse reinforcements.

• Geomechanics and Engineering
• /
• v.18 no.6
• /
• pp.595-603
• /
• 2019

A modified model combining Kriging and Monte Carlo method (MC) is proposed for probabilistic estimation of tunnel face stability in this paper. In the model, a novel uniform design is adopted to train the Kriging, instead of the existing active learning function. It has advantage of avoiding addition of new training points iteratively, and greatly saves the computational time in model training. The kinematic approach of limit analysis is employed to define the deterministic computational model of face failure, in which the Hoek-Brown failure criterion is introduced to account for the nonlinear behaviors of rock mass. The trained Kriging is used as a surrogate model to perform MC with dramatic reduction of calls to actual limit state function. The parameters in Hoek-Brown failure criterion are considered as random variables in the analysis. The failure probability is estimated by direct MC to test the accuracy and efficiency of the proposed probabilistic model. The influences of uncertainty level, correlation relationship and distribution type of random variables are further discussed using the proposed approach. In summary, the probabilistic model is an accurate and economical alternative to perform probabilistic stability analysis of tunnel face excavated in spatially random Hoek- Brown media.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.4
• /
• pp.33-40
• /
• 2020

In order to satisfy customers, it is important to identify the quality elements that affect customers' satisfaction. The Kano model has been widely used in identifying multi-dimensional quality attributes in this purpose. However, the model suffers from various shortcomings and limitations, especially those related to survey practices such as the data amount, reply attitude and cost. In this research, a model based on the text sentiment analysis is proposed, which aims to substitute the survey-based data gathering process of Kano models with sentiment analysis. In this model, from the set of opinion text, quality elements for the research are extracted using the morpheme analysis. The opinions' polarity attributes are evaluated using text sentiment analysis, and those polarity text items are transformed into equivalent Kano survey questions. Replies for the transformed survey questions are generated based on the total score of the original data. Then, the question-reply set is analyzed using both the original Kano evaluation method and the satisfaction index method. The proposed research model has been tested using a large amount of data of public IT service project evaluations. The result shows that it can replace the existing practice and it promises advantages in terms of quality and cost of data gathering. The authors hope that the proposed model of this research may serve as a new quality analysis model for a wide range of areas.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.301-307
• /
• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.8
• /
• pp.2787-2800
• /
• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• Wind and Structures
• /
• v.36 no.6
• /
• pp.367-377
• /
• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.

• Journal of the Korea Concrete Institute
• /
• v.17 no.2 s.86
• /
• pp.201-212
• /
• 2005

A new strut-tie model using secant stiffness, Direct Inelastic Strut-Tie Model, was developed. Since basically the proposed design method uses linear analysis, it is convenient and stable in numerical analysis. At the same time, the proposed design method can accurately estimate the inelastic strength and ductility demands of struts and ties because it can analyzes the inelastic behavior of structure using iterative calculations for secant stiffness. In the present study, the procedure of the proposed design method was established, and a computer program incorporating the proposed method was developed. Design examples using the proposed method were presented, and its advantages were highlighted by the comparison with the traditional strut-tie model. The Direct Inelastic Strut-Tie Model, as an integrated analysis/design method, can directly address the design strategy intended by the engineer to prevent development of macro-cracks and brittle failure of struts. Since the proposed model can analyze the inelastic deformation, indeterminate strut-tie model can be used. Also, since the proposed model controls the local deformations of struts and ties, it can be used as a performance-based design method for various design criteria.

• Journal of Internet Computing and Services
• /
• v.16 no.5
• /
• pp.99-105
• /
• 2015

Currently robots are widely used in schools for educational purpose. With wide spread of robot-based education, it is known that major advantage of robot-based education is to enhance creativity and logical thinking of students. Although robots can be very useful tools for assisting students' study activities, there have not been lots of teaching-learning models for robot-based education.In this paper, a teaching-learning model is presented for robot-based education. The proposed model is designed based on constructivism. The proposed model consists of 6 stages: preparation, design, assembling, demonstration run, evaluation, and application & extension. The proposed model has the following characteristics. First, the proposed model is designed to enhance creativity and logical thinking ability of learners. Learners are supposed to be involved in self-directed activities and required to provide results based on their own ideas. Teachers are supposed to mediate students only if necessary. Second, learners are encouraged to participate in activity via diverse interaction. The interaction in this model includes learner-to-learner interaction, learner-to-teacher interaction, and learner-to-expert interaction. The proposed model encourages learners to solve the problem with cooperating each other. Also, teachers are supposed to guide students if necessary and observe and monitor behavior of students all the time. Third, motivation is provided in the beginning stage of the instruction. Fourth, in the proposed model, both study results and study process are equally important. In the model, study process is reviewed at the final stage.

• Communications for Statistical Applications and Methods
• /
• v.10 no.2
• /
• pp.361-370
• /
• 2003

Those who are interested in making inferences concerning linear combination of valiance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods. The methods are applied to a numerical example and recommendations are given for choosing a proper interval.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1998.10a
• /
• pp.32-35
• /
• 1998

Technology Acceptance Model (TAM) has been widely used for predicting behavioral processes in which information technologies were accepted, but several previous studies point out that TAM may not explain the adoption process of complex information technologies effectively. This study empirically tests the limitation of TAM, and suggests a proposed research model by incorporating the concept of the perceived behavioral control into TAM. Study findings indicate the proposed model can predict the adoption process better than TAM does.