• 한국BIM학회 논문집
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• 제11권4호
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• pp.53-62
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• 2021

The successful implementation of Scan-to-BIM automation depends on the entire process from scanning of buildings, including indoor facilities and furniture, to generating BIM models. However, the conventional Scan-to-BIM process requires a lot of time, manpower, and cost for the manual generation of BIM models including indoor objects. To solve this problem, this study applied a Scan-to-BIM automation process using a deep learning model and parametric algorithm to an existing building, Kangwon Fire Service Academy. To improve the accuracy of the BIM model, after object data was extracted from the scan data, the data was corrected according to actual object-specific conditions. As a result, the accuracy of the BIM model created by the proposed Scan-to-BIM automation process was 91% compared to the actual area of the construction drawings. In addition, it was confirmed that the BIM objects were automatically generated for 10 object classes.

• 건축사
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• 8호통권126호
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• pp.27-42
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• 1979

• 전산구조공학
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• 제36권3호
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• pp.17-22
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• 2023

• 한국공간구조학회지
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• 제20권4호
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• pp.6-13
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• 2020

• 한국공간구조학회논문집
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• 제19권3호
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• pp.69-76
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• 2019

A Beam String Structure (BSS) is a type of hybrid structures, which is composed of upper structural members, lower strings, and struts. Due to the advantages that the pre-tensioned strings elicit pre-caber of the upper structural members, the deflection can be greatly reduced without increasing the structural member size. In this study, a two-way beam string structure is proposed to endure bi-directional loading. The two-way beam string structure consists of two cable parts, namely, sagging and arch-shaped cables. A parametric study is presented aimed at proposing design guide lines of the two-way beam string structures. Numerical finite element analyses through the ABAQUS package were implemented to obtain their behaviors.

• 한국공간구조학회논문집
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• 제21권1호
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• pp.61-70
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• 2021

This paper describes a novel zero-stress member selecting method for sizing optimization of truss structures. When a sizing optimization method with static constraints is implemented, the member stresses are affected sensitively with changing the variables. However, because some truss members are unaffected by specific loading cases, zero-stress states are experienced by the elements. The zero-stress members could affect the computational cost and time of sizing optimization processes. Feature selection approaches can be then used to eliminate the zero-stress member from the whole variables prior to the process of optimization. Several numerical truss examples are tested using the proposed methods.

• 한국공간구조학회논문집
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• 제21권4호
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• pp.39-48
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• 2021

In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

• 건축사
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• 통권633호
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• pp.48-49
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• 2022

• 건축사
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• 통권535호
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• pp.70-73
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• 2013

• 건축사
• /
• 6호통권112호
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• pp.36-44
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• 1978