• Journal of Korea Water Resources Association
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• v.50 no.12
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• pp.827-836
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• 2017

The most important parameters of the Muskingum method, widely used in hydrologic river routing, are the storage coefficient and the weighting factor. The Muskingum method does not consider the lateral inflow from the upstream to the downstream, but the lateral inflow actually occurs due to the rainfall on the watershed. As a result, it is very difficult to estimate the storage coefficient and the weighting factor by using the actual data of upstream and downstream. In this study, the flow without the lateral inflow was calculated from the river flow through the hydraulic flood routing by using the HEC-RAS one-dimensional unsteady flow model, and the method of the storage coefficient and the weighting factor calculation is presented. Considering that the storage coefficient relates to the travel time, the empirical travel time formulas used in the establishment of the domestic river basin plan were applied as the storage coefficient, and the simulation results were compared and analyzed. Finally, we have developed a formula for calculating the travel time considering the flow rate, and proposed a method to perform flood routing by updating the travel time according to the inflow change. The rise and fall process of the flow rate, the peak flow rate, and the peak time are well simulated when the travel time in consideration of the flow rate is applied as the storage coefficient.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.7
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• pp.521-530
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• 2009

This study has been made to execute a research in order to lead the improvement of indoor air quality, examining the indoor ventilation characteristics by using a numerical analysis method. To this end an extensive parametric investigation are made according to various external flow variables such as main wind direction and wind speed by season, building layout design, and location of ventilators, etc. in Daedeok Techno Valley, one of large-scaled apartment in Daejeon. It is observed there was a significant difference of main wind direction between summer and winter. The main wind direction in summer was a south wind, and on the contrary the direction in winter is northnorthwest, which is similar to the average main wind direction for 10 years. One of the important calculation results is that the change of wind direction causes a significant effect on the apartment ventilation by the change of pressure difference around each complex of apartment. In case of favorable area of ventilation, the indoor ventilation rate can meet 0.7 ACH from the standard value only with natural ventilation. On the contrary, in other area the value was much lower than the standard value. If the calculation result applies to the design of layout apartment or placement of ventilators, it will be greatly helpful to the energy saving because it can be parallel with the natural ventilation to help securing ventilation rate, not much depending on the mechanical ventilation.

• Tunnel and Underground Space
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• v.33 no.3
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• pp.189-207
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• 2023

In the present study, the thermoshearing experiment on a rough rock fracture were modeled using a three-dimensional grain-based distinct element model (GBDEM). The experiment was conducted by the Korea Institute of Construction Technology to investigate the progressive shear failure of fracture under the influence of thermal stress in a critical stress state. The numerical model employs an assembly of multiple polyhedral grains and their interfaces to represent the rock sample, and calculates the coupled thermo-mechanical behavior of the grains (blocks) and the interfaces (contacts) using 3DEC, a DEM code. The primary focus was on simulating the temperature evolution, generation of thermal stress, and shear and normal displacements of the fracture. Two fracture models, namely the mated fracture model and the unmated fracture model, were constructed based on the degree of surface matedness, and their respective behaviors were compared and analyzed. By leveraging the advantage of the DEM, the contact area between the fracture surfaces was continuously monitored during the simulation, enabling an examination of its influence on shear behavior. The numerical results demonstrated distinct differences depending on the degree of the surface matedness at the initial stage. In the mated fracture model, where the surfaces were in almost full contact, the characteristic stages of peak stress and residual stress commonly observed in shear behavior of natural rock joints were reasonably replicated, despite exhibiting discrepancies with the experimental results. The analysis of contact area variation over time confirmed that our numerical model effectively simulated the abrupt normal dilation and shear slip, stress softening phenomenon, and transition to the residual state that occur during the peak stress stage. The unmated fracture model, which closely resembled the experimental specimen, showed qualitative agreement with the experimental observations, including heat transfer characteristics, the progressive shear failure process induced by heating, and the increase in thermal stress. However, there were some mismatches between the numerical and experimental results regarding the onset of fracture slip and the magnitudes of fracture stress and displacement. This research was conducted as part of DECOVALEX-2023 Task G, and we expect the numerical model to be enhanced through continued collaboration with other research teams and validated in further studies.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.