• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.1
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• pp.47-54
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• 2019

This study proposes a new ERAM model with building population weighting. Previous studies of applying weightings on ERAM model on the scale of urban space were focused on the relationship between the street and the human behavior. However, this study focuses on the influences that buildings give to human behavior and develops a building population weighted ERAM model. This research starts by analyzing ERAM model to its basic compositions, which are adjacency matrix and row vector. It applies building population weighting to the row vector, while previous studies put weightings in the adjacency matrix. Building population weighted ERAM model calculates the building population weighting based on GIS data, which provides objective and massive data of buildings in the urban scale. For the verification of the model, Insa-dong and Myeong-dong were analyzed with both ERAM model and building population weighted ERAM model. The results were analyzed through the correlation test with actual pedestrian population data of the two districts. As a result, the explanation ability of building population weighted ERAM model for the pedestrian population turned out to be higher than the ERAM model. Since building population weighted ERAM model has the structure that can be combined with other weighted ERAM models, it is expected to develop a multi-weighted ERAM model with better explanation ability as a further study.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.3
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• pp.205-212
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• 2008

This paper describes the application of an inverse building model to a calibrated forward building model using EnergyPlus program. Typically, inverse models are trained using measured data. However, in this study, an inverse building model was trained using data generated by an EnergyPlus model for an actual office building. The EnergyPlus model was calibrated using field data for the building. A training data set for a month of July was generated from the EnergyPlus model to train the inverse model. Cooling load prediction of the trained inverse model was tested using another data set from the EnergyPlus model for a month of August. Predicted cooling loads showed good agreement with cooling loads from the EnergyPlus model with root-mean square errors of 4.11%. In addition, different control strategies with dynamic cooling setpoint variation were simulated using the inverse model. Peak cooling loads and daily cooling loads were compared for the dynamic simulation.

• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.290-293
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• 2006

The goal of research is to propose a framework for drawing data search system which is based on the web. The existing search systems were reviewed in the form of case studies and thereby the limitation were addressed: the unsystematic translation between the presentational building model and the discursive design criteria. besides the limited area in sharing and space. Therefore, a web-based drawing search with common structure which building representational model and building behavior model is proposed. The system contains a number of phases: firstly, a user is required to build a building model with the proposed building representational model and then this model is automatically transformed into an aspect model; secondly, a user is also required to present his query in form of the propose building behavior model by web page; finally, these two models - building representational model and building behavior model - are compared by database data so as to retrieve the proper result.

• Atmosphere
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• v.29 no.4
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• pp.417-427
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• 2019

The purpose of this study is to build urban canopy model (Met Office Reading Urban Surface Exchange Scheme, MORUSES) based to Unified Model (UM) by using urban building information data in Seoul, and then to compare the improving urban canopy model simulation result with that of Seoul Automatic Weather Station (AWS) observation site data. UM-MORUSES is based on building information database in London, we performed a sensitivity experiment of UM-MOURSES model using urban building information database in Seoul. Geographic Information System (GIS) analysis of 1.5 km resolution Seoul building data is applied instead of London building information data. Frontal-area index and planar-area index of Seoul are used to calculate building height. The height of the highest building in Seoul is 40m, showing high in Yeoido-gu, Gangnam-gu and Jamsil-gu areas. The street aspect ratio is high in Gangnam-gu, and the repetition rate of buildings is lower in Eunpyeong-gu and Gangbuk-gu. UM-MORUSES model is improved to consider the building geometry parameter in Seoul. It is noticed that the Root Mean Square Error (RMSE) of wind speed is decreases from 0.8 to 0.6 m s^-1 by 25 number AWS in Seoul. The surface air temperature forecast tends to underestimate in pre-improvement model, while it is improved at night time by UM-MORUSES model. This study shows that the post-improvement UM-MORUSES model can provide detailed Seoul building information data and accurate surface air temperature and wind speed in urban region.

• Journal of Korea Planning Association
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• v.54 no.5
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• pp.129-138
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• 2019

This study aims to empirically examine the relationship between building energy consumption and building and local factors in Seoul. Building energy issue is an important topic for low carbon and eco-friendly city development. Building physical, socio-economic and environmental factors effect to increasing or decreasing energy consumption. However, there are different characteristic in each area, and this kind of variable has a hierarchical structure. The multi-level model was used to consider the hierarchical structure of the variables. In this study, a multi-level model was applied to confirm the difference between areas. Spatial area is Seoul, Korea and the temporal scope is August, summer season. As the result, in Model 1 (Null Model), ICC is 0.817. This shows that the energy consumption differs by 8.174% due to factors at the Dong level. Model 2 (Random Intercept Model) suggests that building's physical factors and Average age, Household size and Land price in Dong level have significant effects on Building energy consumption. In Model 3 (Random Coefficient Model), random effect variables have intercepts and slopes to vary across groups. This study provides a perspective for policy makers that the building energy reduction policies to be applied for buildings should be differently applied on area. Furthermore, not only physical factors but also socio-economic and environmental factors are important when making energy reduction policy.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.5
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• pp.422-431
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• 2003

Theory of the building heat transfer is generally limited to the heat flux to the surfaces of windows and walls, which influences the indoor climate of a building, in the field of architectural environmental engineering. While the heat flux from the buildings to their environment has been considered in the viewpoint of urban climate, its conventional theory have been rarely examined. The purpose of this study is to propose a building-urban heat transfer model for defining the relation between the building and the urban climate by extending the building heat transfer model. In this study, the extended building heat transfer model, where response factor method is used, is established on the urban space and the indoor space by the boundary of building envelopes. Computer simulation (HASP/ACLD) is conducted on the subjected urban area by the established building-urban heat transfer model. As a result it is logically proved that the short waves of solar radiation, which interact with long Waves of radiation from the buildings and the earth, increase the urban air temperature ana buildings largely influence on the urban climate.

• KIEAE Journal
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• v.2 no.4
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• pp.65-72
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• 2002

In this study, the evaluation of environmental performance of the building energy system of domestic commercial sector was carried out. Based on the theory of linear programming model, we established an evaluation model satisfying object functions and constraint conditions. Employing the model, the evaluation of building energy system was performed under the consideration of cost and environmental constraint conditions. As an evaluation tool, MARKAL (MARKet Allocation) known as a market distribution model was employed. We analyzed scenarios of Case I (Base Scenarios) through Case IX established by the combination of the components of building energy system such as glazing, building skin, core, and heat source system. According to the results of the evaluation, highest contribution on the useful energy demand was obtained from the building energy system combined with solar heat source system, when the total amounts of $CO_2$ exhaust as an environmental constraint condition is assumed to be the level of 1995.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.320-324
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• 2000

Nonlinear stress analysis of nuclear containment building is carried out using microscopic concrete material model. The present study mainly focuses on the evaluation of the ultimate pressure capacity of idealized containment building in nuclear power plant. For this purpose, an eight-node degenerated shell element it adopted and an imaginary opening in the apex of containment building is allowed in FE model. From numerical analysis, the adopted concrete material model performs well and has a good agreement with the result obtained by using ABAQUS. Finally, we propose the present study as a benchmark test for nonlinear analysis of containment building.

• Journal of Construction Engineering and Project Management
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• v.4 no.4
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• pp.9-16
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• 2014

The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.

• Journal of the Korea Institute of Building Construction
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• v.5 no.1 s.15
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• pp.97-103
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• 2005

Building costs means capital costs which include cost of land, costs of acquiring and preparing the site, construction costs, professional fees, furnishings, cost of financing the project. and cost of management required to run and maintenance the building for use. There are several phases that determine the building costs : design phase, construction phase, and operation & maintenance phase. So, the cost of work could be set against the examining the full range of complexities that a building program might contain. To solve this problem, it needs to compute building cost systematically. This is still in the development stage, awaiting the organization of rational cost data base. The method of cost saving by cost control could be constituted by detailed knowledge of building costs for all possible combinations of components and subsystems that can be assembled into integration model of cost factor on each phase of project development. The model of cost saving in each building phase is available for procedures of cost control of building systems.