• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.169-172
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• 2013

Building Information Modeling (BIM) is an effective decision-making platform that helps to save project cost and enhance quality of construction. By generating and linking a wide variety of objects data, BIM can be effectively utilized, and it should be ensured that object properties maintain consistency throughout the project period of design, estimates, construction, maintenance and repair. This study examined how to utilize BIM data in a construction project, by linking cost and schedule data in web environment, to better utilize the information and maintain consistency of the BIM information. To do so, the model integrated WBS data and CBS data, linked them with BIM model to realize 5D simulation in web environment. As a result, cost and schedule data could be simultaneously acquired, and object properties-cost, schedule, location-as well. These are expected to contribute to developing a BIM-based automatic data-processing system in web environment.

• Smart Structures and Systems
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• 제33권5호
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• pp.349-358
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• 2024

Structural digital models can be effectively established by spatially obtaining digital images using an unmanned aerial vehicle (UAV). One of the main purposes of the structural digital modeling is computer vision-based exterior damage detection of a target structure. To investigate micro-scale damage from the digital model, high-resolution digital images obtained with a close-up vision survey is typically required. However, serial image synthesis such as image stitching may cumulate stitching errors as the number of scanned images increases. Therefore, in this paper, a novel loop closure-based digital image stitching technique is proposed and experimentally validated using the close-up surveyed digital images acquired from an in-situ dam structure located in South Korea. The test results reveal that the proposed technique outperforms a non-loop closure-based image stitching technique, which can cause serious distortions, such as ghosting and vanishing phenomena.

• 한국물환경학회지
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• 제24권2호
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• pp.221-229
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• 2008

This study revised a model for hydrologically analyzing rainwater harvesting facilities considering their rainfall-runoff properties and the data available. This model has only a few parameters, which can be estimated with rather poor measurements available. The model has a non-linear module for rainfall loss, and the remaining rainfall excess (effective rainfall) is assumed to be inflow to the storage tank. This model has been applied for the rainwater harvesting facilities in Seoul National University, Korea Institute of Construction Technology, and the Daejon World Cup Stadium. As a result, the runoff coefficients estimated were about 0.9 for the building roof as a rainwater collecting surface and about 0.18 for the playground. This result is coincident with that for designing the rainwater harvesting facilities to show the accuracy of model and the simulation results.

• Structural Engineering and Mechanics
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• 제7권1호
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• pp.69-80
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• 1999

The purpose of this study is to propose an analytical model for the simulation of the hysteretic behavior of RC (reinforced concrete) beam-column subassemblages under various loading histories. The discrete line element with inelastic rotational springs is adopted to model the different locations of the plastic hinging zone. The hysteresis model can be adopted for a dynamic two-dimensional inelastic analysis of RC frame structures. From the analysis of test results it is found that the stiffness deterioration caused by inelastic loading can be simulated with a function of basic pinching coefficients, ductility ratio and yield strength ratio of members. A new strength degradation coefficient is proposed to simulate the inelastic behavior of members as a function of the transverse steel spacing and section aspect ratio. The energy dissipation capacities calculated using the proposed model show a good agreement with test results within errors of 27%.

• Computers and Concrete
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• 제14권3호
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• pp.247-262
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• 2014

Partial replacement of Portland cement by slag can reduce the energy consumption and $CO_2$ emission therefore is beneficial to circular economy and sustainable development. Compressive strength is the most important engineering property of concrete. This paper presents a numerical procedure to predict the development of compressive strength of slag blended concrete. This numerical procedure starts with a kinetic hydration model for cement-slag blends by considering the production of calcium hydroxide in cement hydration and its consumption in slag reactions. Reaction degrees of cement slag are obtained as accompanied results from the hydration model. Gel-space ratio of hardening slag blended concrete is determined using reaction degrees of cement and slag, mixing proportions of concrete, and volume stoichiometries of cement hydration and slag reaction. Furthermore, the development of compressive strength is evaluated through Powers' gel-space ratio theory considering the contributions of cement hydration and slag reaction. The proposed model is verified through experimental data on concrete with different water-to-binder ratios and slag substitution ratios.

• Architectural research
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• 제13권3호
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• pp.49-56
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• 2011

Competitive bidding in construction is concerned with contractors making strategic decisions in respect of determination of bid price if contractors opt to bid. This study presents a strategy model for deciding optimum tender price with reflecting appropriate profit in competitive bidding using Bayesian regression analysis (BRA). The purpose of the developed model is to help contractors to secure suitable profitability by predicting the actual profit based on key variables. They may affect construction cost at bidding phase, ultimately which help contractors to secure high quality output. The model was tested empirically by application to a bidding dataset collected from a large South Korea contractor. BRA allows contractors to estimate more accurate actual profit by reflecting not only objective information but also subjective experiences and judgments. Consequently, the model can contribute to improvement of decision-making process for setting an optimum tender price.

• Architectural research
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• 제8권2호
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• pp.43-48
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• 2006

The appropriate fleet estimation of the excavation equipment is a major factor in the determination of the cost and time requirements of a project. But the decision of what kind of equipment selected is often based on heuristic methods or trial and error in Korea. Thus, this study proposes a prototype model that uses genetic algorithms to select fleet estimation of loaders (backhoe) and trucks used in excavation work. To verify the applicability of this model, the case study was performed. And the result of the genetic model was compared with that of the trial & error method. The use of the genetic model suggested this study required 44days, 2 units of backhoes, 7 units of trucks, and a total cost of 171,839,756 won. With the estimated fleet number of equipment, the minimum cost of excavation work can be calculated, taking account of the time-cost trade-off. By utilizing this prototype model, the efficiency of excavation work can be improved.

• Nuclear Engineering and Technology
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• 제55권11호
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• pp.3956-3972
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• 2023

The floor response spectrum (FRS) is used to evaluate the seismic demand of equipment installed in nuclear power plants. In the conventional design practice of NPP structure, the FRS is simplified using the lumped-mass stick model (LMSM), assuming the floor slab as a rigid diaphragm. In the present study, to study the variation of seismic response in a floor, the FRSs at different locations were generated by 3-D finite element model, and the response was compared to that of the rigid diaphragm model. The result showed that the FRS significantly varied due to the large opening in a floor, which was not captured by the rigid diaphragm model. Based on the result, seismic fragility analysis was performed for the anchorage of a heat exchanger, to investigate the effect of location-dependent FRS disparity on the high confidence low probability of failure (HCLPF).

• 한국공간구조학회논문집
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• 제20권4호
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• pp.53-62
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• 2020

This study examines the optimum shape of a trolley, the driving device of the retractable membrane roof. The closed-type trolley was determined as the model of the study, and a trolley composed of cylindrical-shaped inner and outer holders was selected as the basic model. Based on this model, a cylindrical-based optimal trolley model was proposed. In the basic trolley model, steel was used for the outer holder, and steel, titanium, and aluminum were used for the inner holder. In each case, the most economical shape for the external load of the basic model was newly proposed through the topology optimization process, and the finite element analysis results of the proposed model were compared to define the durability and economics. Here, topology optimization analysis and finite element analysis used the commercial software ANSYS. As a result of optimization, the volume of the outer holder of the trolley was reduced by 58.2% and the volume of the inner holder was reduced by 25.0% compared to the basic model. In the case of stress, a stress increase of 43.2 to 79.2% occurred depending on the material of the inner holder, but it was found to be significantly lower than the yield strength, thereby ensuring safety.

• 대한건축학회논문집:구조계
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• 제34권6호
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• pp.63-69
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• 2018

The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction ($5.4%{\rightarrow}1.3%$).