• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.94-95
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• 2020

This paper introduces the mix design and performance evaluation of Ultra-High Performance Concrete (UHPC). The concrete mixture is designed to achieve a densely compacted cementitious matrix via the modified Andreasen & Andersen particle packing model. The compressive strengths of UHPC designed by this method reached 154MPa. The relationship between packing theory and compressive strength of UHPC is discussed in this paper.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.4
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• pp.223-230
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• 2014

In this paper, the wind-induced response characteristics of freeform shaped tall building is studied by using FSI analysis. The analytical models are twist shaped ones at representing type of atypical tall building, and this study focused on the relationship between twist angle and wind acceleration. Firstly, 1-way FSI analysis is performed, so maximum lateral displacement of the analytical model for 100 years return period wind speed is calculated, then the elastic modulus of a structure that satisfies the constraints condition is evaluated. And 2-way FSI analysis is carried out. so acceleration of the analytical model for the evaluated modulus of elasticity and arbitrary density is predicted through time history analysis. The basic model is a set of a square shape, height is 400m, slenderness ratio is 8, and twist model is rotated at square model from 0 to 90 degrees at intervals of 15 degrees and from 90 to 360 degrees at intervals of 90 degrees. According to the result of predicting wind acceleration by the shape of each model, the wind vibration effect of square shape model is confirmed to be sensitive more than a twist shape ones.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.103-111
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• 2023

Accurate cost estimation in the early stages of a construction project is critical to the successful execution of the project. In this study, an ANFIS model was presented to predict construction costs in the early stages of a construction project. To increase the usability of the model, open construction cost data was used, and a model using limited information in the early stage of the project was presented. We analyzed existing studies related to ANFIS to identify recent trends, and after reviewing the basic structure of ANFIS, presented an ANFIS model for predicting conceptual construction costs. The variation in prediction performance depending on the type and number of membership functions of the ANFIS model was analyzed, the model with the best performance was presented, and the prediction accuracy of representative machine learning models was compared and analyzed. Through comparing the ANFIS model with other machine learning models, it was found to show equal or better performance, and it is concluded that it can be applied to predicting construction costs in the early stage of a project.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.1
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• pp.115-127
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• 2000

The purpose of this study is to propose the Design Object Model for implementation of an integrated structural design system for building structures. This study outlines the step-by-step development methodologies of the Design Object Model, which covers classification and modeling of the building design information. The Design Object Model has been efficiently developed through the proposed development methodologies. As a result, the Design Object Model has been proved to be efficient in design information management by representing the information from planning perspective, in recognition of structural member in space by the topology design object, and in representation of analysis s design information.

• Structural Engineering and Mechanics
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• v.84 no.3
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• pp.323-335
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• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.1-6
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• 2011

As large-scale building projects have recently increased for the residential, commercial and office facilities, construction costs for these projects have become a matter of great concern, due to their significant construction cost implications, as well as unpredictable market conditions and fluctuations in the rate of inflation during the projects' long-term construction periods. In particular, recent volatile fluctuations of construction material prices fueled such problems as cost forecasting. This research develops a time series model using the Box-Jenkins approach and material price time series data in Korea in order to forecast trends in the unit prices of required materials. Building information modeling (BIM) approaches are also used to analyze injection times of construction resources and to conduct quantity take-off so that total material prices can be forecast. To determine an optimal time series model for forecasting price trends, comparative analysis of predictability of tentative autoregressive integrated moving average (ARIMA) models is conducted. The proposed BIM-based time series forecasting model can help to deal with sudden changes in economic conditions by estimating material prices that correspond to resource injection times.

• Earthquakes and Structures
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• v.19 no.3
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• pp.157-174
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• 2020

The in-situ pushover test differs from the shake-table test because it is performed outdoors and thus its size is not restricted by space, which allows us to test a full-size building. However, to build a new full-size building for the test is not economical, consequently scholars around the world usually make scale structures or full-scale component units to be tested in the laboratory. However, if in-situ pushover tests can be performed on full-size structures, then the seismic behaviors of buildings during earthquakes can be grasped. In view of this, this study conducts two in-situ pushover tests of reinforced concrete (RC) buildings. One is a masonry-infilled RC building with openings (the openings ratio of masonry infill wall is between 24% and 51%) and the other is an RC building without masonry infill. These two in-situ pushover tests adopt obsolescent RC buildings, which will be demolished, to conduct experiment and successfully obtain seismic capacity curves of the buildings. The test results are available for the development or verification of a seismic evaluation model. This paper uses ASCE 41-17 as the main evaluation model and is accompanied by a simplified pushover analysis, which can predict the seismic capacity curves of low-rise buildings in Taiwan. The predicted maximum base shear values for masonry-infilled RC buildings with openings and for RC buildings without masonry infill are, respectively, 69.69% and 87.33% of the test values. The predicted initial stiffness values are 41.04% and 100.49% of the test values, respectively. It can be seen that the ASCE 41-17 evaluation model is reasonable for the RC building without masonry infill walls. In contrast, the analysis result for the masonry infilled RC building with openings is more conservative than the test value because the ASCE 41-17 evaluation model is limited to masonry infill walls with an openings ratio not exceeding 40%. This study suggests using ASCE 41-17's unreinforced masonry wall evaluation model to simulate a masonry infill wall with an openings ratio greater than 40%. After correction, the predicted maximum base shear values of the masonry infilled RC building with openings is 82.60% of the test values and the predicted initial stiffness value is 67.13% of the test value. Therefore, the proposed method in this study can predict the seismic behavior of a masonry infilled RC frame with large openings.

• Journal of the Ergonomics Society of Korea
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• v.20 no.1
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• pp.45-62
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• 2001

This study suggests a genetic algorithm-based partial least squares (GA-based PLS) method to select the design variables for building a usability model. The GA-based PLS uses a genetic algorithm to minimize the root-mean-squared error of a partial least square regression model. A multiple linear regression method is applied to build a usability model that contains the variables seleded by the GA-based PLS. The performance of the usability model turned out to be generally better than that of the previous usability models using other variable selection methods such as expert rating, principal component analysis, cluster analysis, and partial least squares. Furthermore, the model performance was drastically improved by supplementing the category type variables selected by the GA-based PLS in the usability model. It is recommended that the GA-based PLS be applied to the variable selection for developing a usability model.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.331-336
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• 2015

In this study, a numerical model developed for sunshine duration based on GIS data was used. This model considers blocking caused by topography and buildings and it is properly applicable to evaluation of sunshine duration environment in urban areas. The model reasonably well predicted the solar altitude and azimuth angels, compared to those provided by Korea Astronomy and Space Science Institute (KASI). The developed model was applied to evaluation of sunshine duration environment around the Seonleung Park located near a building-congested area in Seoul. The model well reproduced shadow caused by buildings and/or topography in the numerical domain at 09:00 on August 1, 2015. In addition, the model was applied to finding a suitable measurement sites for pyrheliometer around the Seonleung Park. The model was also usefully applied to finding a suitable site for pyrheliometer in an urban area.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.3
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• pp.103-111
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• 2018

A prototypical building that represents the energy characteristics of buildings can be used as a mean of improving building energy efficiency by supporting policy makers, researchers, architects and engineers. This paper is a fundamental study for the definition of prototypical office building models of Korea. First of all, the term "prototypical buildings" was defined in the context of the representatives of building stock by examining the meanings used from many previous researches. Then, building energy related DB of Korea that is available from public and governmental organization such as MLIT (Ministry of Land, Infrastructure and Transport) and KEEI (Korea Energy Economics Institute) is analyzed in terms of materials for prototypical building definition. Finally, numerous prototypical building studies since 1990's from all of the world were classified with three criteria based on the degrees of the used DB and assumptions in defining prototypical building. The found three criteria are EDPB (Empirical Decision based Prototypical Building), HIPB (Hybrid Information based Prototypical Building) SAPB (Statistical Analysis based Prototypical Building).