• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.3
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• pp.111-119
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• 2021

A shear wall is a structural member designed to effectively resist in-plane lateral forces, such as strong winds and earthquakes. Due to its efficiency and stability, shear walls are often installed in residential buildings and essential facilities such as nuclear power plants. In this research, to predict the results of the shaking table test of the three-story shear wall RC structure hosted by the Korea Atomic Energy Research Institute, three types of numerical modeling techniques are proposed: Preliminary, Calibrated 1, and Calibrated 2 models, in order of improvement. For the proposed models, an earthquake of the 2016 Gyeongju, South Korea (peak ground acceleration of 0.28 g) and its amplified earthquake (peak ground acceleration of 0.50 g) are input. The response spectra of the measuring points are obtained by numerical analysis. Good agreement is observed in the comparisons between the experiment results and the simulation conducted on the finally adopted numerical model, Calibrated 2. In the process of improving the model, this paper investigates the influences of the mode shape, material properties, and boundary conditions on the structure's seismic behavior.

• Nuclear Engineering and Technology
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• v.51 no.5
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• pp.1444-1450
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• 2019

The fast-neutron shielding behaviour (FNSB) of two clay-materials (Ball clay and Kaolin)of Southwestern Nigeria ($7.49^{\circ}N$, $4.55^{\circ}E$) have been investigated using effective removal cross section, ${\Sigma}_R(cm^{-1})$, mass removal cross section, ${\Sigma}_{R/{\rho}}(cm^2g^{-1})$ and Mean free path, ${\lambda}$ (cm). These parameters decide neutron shielding behaviour of any material. A computer program - WinNC-Toolkit has been used for computation of these parameters. The toolkit evaluates these parameters by using elemental compositions and densities of samples. The proficiency of WinNC-Toolkit code was probe by using MCNPX and GEANT4 to model fast neutron transmission of the samples under narrow beam geometry, intending to represent the actual experimental setup. Direct calculation of effective removal cross section ($cm^{-1}$) of the samples was also carried out. The results from each of the methods for each types of the studied clay-materials (Ball clay and Kaolin) shows similar trend. The trend might be the fingerprint of water content retained in each of the samples being baked at different temperature. The compositions of each sample have been obtained by Particle-Induced X-ray Emission (PIXE) technique (Tandem Pelletron Accelerator: 1.7 MV, Model 5SDH). The FNSB of the selected clay-materials have been compared with standard concrete. The cognizance of various factors such as availability, thermo-chemical stability and water retaining ability by the clay-samples can be analyzed for efficacy of the material for their FNSB.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.78-84
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• 2019

The purpose of this study is to introduce the concept of the Smart Sewer System and to analyze the economic ripple effect when smart sewer is built all over Korea. The research method is the input-output model based on the assumption that the smart sewerage will be constructed throughout the Korea from 2021 to 2040. Estimation results show that the production-induced effect reaches 343.73 trillion Korean won, the added value-induced effect is 155.867 trillion Korean won, and the employment-induced effect is estimated by 25,118,470, indicating that the smart sewer project leads to being considerably large in the nation-wide economy. In addition, the increase of social welfare by smart sewer is expected to be realized through the improvement of both the environment improvement and the national health. Therefore, the smart sewer project should be implemented without delay by planning a concrete road map and putting it into effect with a budget.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.556-567
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• 2021

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.

• Journal of KIBIM
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• v.11 no.1
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• pp.21-30
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• 2021

Reinforcing bars, a major component of the pre-fab structure, adheres to the existing on-site assembly method and attempts to develop and commercialize the technology of the pre-assembly method, but the effect is insignificant. Welded Wire / Bar Mat (WBM) has various advantages such as commercialization of rebar through machine manufacturing to improve workability, but it is different from the existing design and the construction method is different from the previous one. Therefore, to maximize the advantages of WBM and improve productivity, manufacturing, transportation, and construction from the design stage should be considered based on BIM from the initial design stage. In this paper, the concept of the design support system for the WBM was established based on the use of BIM in concrete reinforcement and the preliminary research on the WBM. WBM conversion design was performed for the existing prefabricated members, and based on this, the exchange format and system of the master prefabricated model with the WBM design data were set up. As a result of the pilot test, it was found that the traditional reinforcing bar information extracted from the master prefab model has transmitted 100% accurately. As for the WBM information, 100% of the information on the straight reinforcement was transmitted and represented, and the information on the bent reinforcement was found to have a 90% recall in the master BIM tool.

• Journal of Creative Information Culture
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• v.6 no.3
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• pp.199-207
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• 2020

In this study, the case of capstone design course operation for the execution of SW development-oriented projects in the field of computer engineering is presented, and a 3-tier capstone design model is proposed through the case. Recently, most engineering departments operate capstone design classes, but in many cases, capstone design courses are generally operated in the graduation year. Students taking these capstone design courses are experiencing difficulties in the process of actually coding and implementing together with the collaboration process according to the team project class operation, but before these difficulties, derivation and verification of possible ideas and concrete ideas It is showing more difficulties in the process. In this study, in order to efficiently operate the capstone design and create results, in the second semester prior to graduation, students, professors, and mentors from industry mentors will work together to develop ideas through an idea tone contest, etc. Through the second semester, we propose a 3-tier capstone design model that can advance the idea and actual implementation and implementation of the derived ideas.

• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.665-675
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• 2022

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.

• Computers and Concrete
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• v.30 no.2
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• pp.99-111
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• 2022

In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze-thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.331-337
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• 2022

Smart learning is augmented with digital, context-aware, and adaptable technologies to encourage students to learn better and faster. To ensure that digital learning is successful and that implementation is efficient, it is critical that the dimensions of digital learning are arranged correctly and that interactions between the various elements are merged in an efficient and optimal manner. This paper builds and discusses a basic framework for smart learning in the digital age, aimed to improve students' abilities and performance in learning. The proposed framework consists of five dimensions: Teacher, Technology, Learner, Digital content, and Evaluation. The Teacher and Learner dimensions operate on two levels: (a) an abstract level to fit in knowledge and skills or interpersonal characteristics and (b) a concrete level in the form of digital devices used by teachers and learners. Moreover, this paper proposes asynchronous online course delivery model. An Arabic smart learning platform has been developed, based on these smart learning core dimensions and the asynchronous online course delivery model, because despite the official status of this language in many countries, there is a lack of Arabic platforms to teach Arabic. Moreover, many non-native Arabic speakers around the world have expressed an interest in learning it. The Arabic digital platform consists of over 70 lessons classified into three competence levels: beginner, intermediate, and advanced, delivered by Arabic experts and Arabic linguists from various Arab countries. The five dimensions are described for the Arabic platform in this paper. Learner dimension is the Arabic and non-Arabic speakers, Teacher dimension is Arabic experts and Arabic linguistics, Technology dimension consists of technology for Arabic platform that includes web design, cloud computing, big data, etc. The digital contents dimension consists of web-based video, records, etc. The evaluation dimension consists of Teachers rating, comments, and surveys.