• Journal of Practical Engineering Education
• /
• v.13 no.2
• /
• pp.221-232
• /
• 2021

The purpose of this study is to explore the design direction of artificial intelligence convergence liberal arts subjects based on flip learning and maker education through analysis of learner needs in a non-face-to-face classroom environment caused by COVID-19. To this end, we analyzed the priorities of subject content elements by using the Borich needs assessment and The Locus for Focus model along with students' perceptions of flip learning for students who took and did not take maker education-based liberal arts courses. Based on this, it was used as basic data for designing the curriculum. The study results are as follows. First, the content elements of the artificial intelligence liberal arts curriculum based on maker education consisted of a total of 9 areas and were designed as a class using flip learning. Second, the areas with the highest demand for education are 'Artificial Intelligence Theory', 'Artificial Intelligence Programming Practice', 'Physical Computing Theory', 'Physical Computing Practice', followed by 'Convergence Project', '3D Printing Theory', '3D Printing practice' was decided. Third, most of the questionnaires regarding the application of flip learning in maker education-based artificial intelligence liberal arts subjects showed positive responses regardless of whether they took the course, and the satisfaction of the students was very high. Based on this, an artificial intelligence-based convergence liberal arts curriculum using flip learning and maker education was designed. This is meaningful in that it provides an opportunity to cultivate artificial intelligence literacy for college students by preparing the foundation for artificial intelligence convergence education in liberal arts education by reflecting the needs of students.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.34 no.3
• /
• pp.82-92
• /
• 2022

In recent years, coastal and port structures have attempted to prevent wave-overtopping or provide waterfront areas by installing superstructures on the structural crowns. In general, in the design stage, the Goda formula acting on the front the structure is applied to calculate the wave pressure acting on the superstructure in consideration of the wave-runup of the design wave. However, the wave pressure exceeding the Goda wave pressure could generate depending on the installation location of the superstructure where the wave-overtopping occurs. This study analyzed the applicability of the Goda formula to the wave pressure calculation for the superstructure of the vertical structures through hydraulic model experiments and numerical simulations. Furthermore, this study investigated the magnitude of the wave pressure acting on the superstructure based on detailed numerical results. As a result, the wave pressure acting on the superstructure was up to 120% higher than the maximum wave pressure on the still water surface. In addition, the wave pressure increases exponentially with the Froude number computed by the overtopping water depth at the crown of the structure, and we proposed an empirical formula for predicting the wave pressure based on the Froude number.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.6
• /
• pp.617-628
• /
• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.5
• /
• pp.431-449
• /
• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.5
• /
• pp.63-73
• /
• 2008

Recent years have seen an explosion of interest in interactive agents motivating human learners to engage in edutainment systems which are designed to be entertaining and educational at the same time. Especially, work on socio-emotional processes has focus on understanding of human's social behavior in training and entertainment a applications. In contrast with work on social emotion, where research groups have developed detailed models of emotional processes, models of personality have emphasized shallow surface behavior. Here, we build on computational appraisal models of emotion to better characterize dispositional differences in how people come to understand social situations. Known as explanatory style, this dispositional factor plays a key role in social interactions and certain socio-emotional disorders, such as depression. Building on appraisal and attribution theories, we model key conceptual variables underlying the explanatory style, and enable agents to exhibit different explanatory tendencies with respect to their personalities. Furthermore, we developed an interactive AR agent based on our framework and applied it into an interactive teaming system that allows participants to explore individual differences in the explanation of social events, with the goal of encouraging the development of perspective laking and emotion-regulatory skills.

• 전자공학회논문지 IE
• /
• v.48 no.4
• /
• pp.69-77
• /
• 2011

Recently, the public sector information projects have bebn larger in size, sophisticated, and diversified, which make the performance management model be studied continuously. According to the research and understanding on this, there are discontents resulted from poor requirements managements, schedule delays, budget overruns, failure to achieve expected goals, although there are increase the scale of projects and the number of information systems than ever before. it makes the requirement for the plan to manage significant risk factors systematically. So in this study, we will understand the risks which have been recognized by professionals and managers in public sector projects for enhancing the success rate. And the surveys will be conducted for the risk factors in each fields by gathering the opinions of the hands on workers in public sector project support team such as S company, L company. Finally, the correlation index between the risks and the other project fields would be clear by this survey.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.7
• /
• pp.430-439
• /
• 2019

In this study, a nonlinear impact analysis was performed to evaluate the safety and damage of an eco-pillar debris barrier with a hollow cross-section, which was proposed to improve constructability and economic efficiency. The construction of concrete eco-pillar debris barriers has increased recently. However, there are no design standards concerning debris barriers in Korea, and it is difficult to find a study on performance evaluations in extreme environments. Thus, an analysis of an eco-pillar debris barrier was done using the rock impact speed, which was estimated from the debris flow velocity. The diameters of rocks were determined by ETAG 27. The impact position, angles, and rock diameter were considered as variables. A concrete nonlinear material model was applied, and the estimation of damage was done by ABAQUS software. As a result, the damage ratio was found to be less than 1.0 at rock diameters of 0.3 m and 0.5 m, but it was 1.39 when the diameter was 0.7 m. This study could be used as basic data on impact force in the design of the cross section of an eco-pillar debris barrier.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.3A
• /
• pp.555-563
• /
• 2006

Recently, many bridges become not only functionally obsolete of bridge deck due to inadequate width but also structurally deficient of substructure due to erosion. In these cases, widening is almost always more economical than complete replacement, and therefore there is a need to make available the results of research and field experience pertaining to the widening of bridge substructure. But, an experimental study for the guarantee of unification between existing and new substructure with adhesive anchor is so insufficient that the development of adhesive anchor system for the unification should be settled promptly. The purpose of the present study is to explore pull out and shear characteristics of adhesive anchor system. For this purpose, several series of concrete specimens have been tested. Major test variables were the bonded length, anchor diameter and anchor slope. The pull out strength, bond stress and shear strength of adhesive faces were measured for the specimens. The present study indicates that the pull out strength increased with more bonded length and more anchor diameter, and that the bond stress decreased with more bonded length and more anchor diameter. The pull out strength and the bond stress increases with more anchor slope and it is considered that the slope of $5^{\circ}$ was more efficient. From the shear tests, it is supposed that anchor diameters more than D19 was proper to the adhesive anchor. Finally, it is expected that both experimental data in these tests and further study including mock-up tests will contribute to the establishment of the unification between existing and new substructure with adhesive anchor.

• Journal of the Korean Geotechnical Society
• /
• v.38 no.11
• /
• pp.69-86
• /
• 2022

The railway is widely used to transport passengers and freight due to its punctuality and large transport capacity. The recent remarkable development in construction technology enables various subsea railway tunnels for continent-continent or continent-island connectivity. In Korea, design and construction experience is primarily based on the successful completion of the Boryeong subsea tunnel (2021) and the Gadeok subsea tunnel (2010). However, frequent earthquakes with diverse magnitudes, globally induced and continuously increased the awareness of seismic risks and the frequency of domestic earthquakes. The effect of an earthquake on the subsea tunnel is very complicated. However, ground conditions and seismic waves are considered the main factors. This study simulated four ground types of 3-dimensional numerical models, such as soil, rock, composite, and fractured zone, to analyze the effect of ground type and seismic wave. A virtual subsea railway shield tunnel considering external water pressure was modeled. Further, three different seismic waves with long-term, short-term, and both periods were studied. The dynamic analyses by finite difference method were performed to investigate the displacement and stress characteristics. Consequently, the long-term period wave exhibited a predominant lateral displacement response in soil and the short-term period wave in rock. The artificial wave, which had both periodic characteristics, demonstrated predominant in the fractured zone. The effect of an earthquake is more noticeable in the stress of the tunnel segment than in displacement because of confining effect of ground and structural elements in the shield tunnel. 

• Industry Promotion Research
• /
• v.8 no.4
• /
• pp.47-60
• /
• 2023

The purpose of this study is to examine the causal relationship between self-efficacy and safety consciousness of health belief factors and how they affect the disaster prevention behavior of construction workers using TBM. To this end, a research model is presented that applies the main variables of the Health Belief Theory, a social psychological health behavior change model developed to predict and explain health-related behaviors. To empirically verify the research model of this study, a survey was conducted among construction workers who have experience in using TBMs for chemical plant construction. The results showed that, first, the perceived severity of construction workers utilizing chemical plant construction has a significant effect on self-efficacy and safety consciousness; second, the perceived probability of construction workers utilizing chemical plant construction has a significant effect on self-efficacy and safety consciousness. Third, the perceived obstacles of construction workers utilizing chemical plant construction have a significant effect on self-efficacy and safety consciousness. Fourth, the perceived benefits of construction workers utilizing chemical plant construction were found to have a significant effect on self-efficacy and safety awareness. The purpose of this study is to reduce critical accidents through disaster prevention behavior of chemical plant construction workers through TBM.