• Journal of the Korea Institute of Building Construction
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• v.22 no.5
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• pp.451-462
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• 2022

The objective of the study is to establish a BIM modeling of precast concrete(PC) shear wall with various wall-to-base connections. The family library of PC shear wall was established in BIM program using component function in a IFC(Industry foundation classes) file format and SketchUp program. From the BIM program, the amounts of concrete, reinforcing bars and steel materials as well as the interference of arranged reinforcing bars can be accurately evaluated in the PC shear walls with spliced sleeves, bolt, or welding plate connection methods. Although the additional metallic materials such as steel plates, bolts, and nuts were used in the PC shear walls with welding plate connection method, their amounts of materials, economic efficiency, and environmental impact were similar to those with spliced sleeve connection. Consequently, the bolt or welding connection is a highly applicable method as wall-to-base connection of PC shear walls, and it was a more useful method than spliced sleeve method, particularly considering the constructability.

• Journal of KIBIM
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• v.12 no.2
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• pp.12-25
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• 2022

To increase the usability of Building Information Modeling (BIM) in construction projects, it is critical to ensure the interoperability of data between heterogeneous BIM software. The Industry Foundation Classes (IFC), an international ISO format, has been established for this purpose, but due to its structural complexity, geometric information and properties are not always transmitted correctly. Recently, deep learning approaches have been used to learn the shapes of the BIM elements and thereby verify the mapping between BIM elements and IFC entities. These models performed well for elements with distinct shapes but were limited when their shapes were highly similar. This study proposed a method to improve the performance of the element type classification by using an Ensemble model that leverages not only shapes characteristics but also the relational information between individual BIM elements. The accuracy of the Ensemble model, which merges MVCNN and MLP, was improved 0.03 compared to the existing deep learning model that only learned shape information.

• Journal of the Korea Institute of Building Construction
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• v.15 no.3
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• pp.317-327
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• 2015

Many construction projects have extensively adopted building information modeling (BIM), and various institutions and standards have been developed domestically in Korea. However, the current process that is used to calculate building space area has a significant shortcoming in that there are two different laws to apply the method of measurement considering space boundaries for building element guidelines. For example, space area can be calculated by a polygon, which is modeling using a BIM-based computer aided design program, such that the space polygon is always exported as an inner-edge type. In this paper, we developed an automated algorithm to convert coordinates of space representation using industry foundation classes based BIM data. The proposed algorithm will enable engineers responsible for space management to use a BIM-based model directly in the space programming process without having to do additional work. The proposed process can help ensure that space area is more accurately and reliably.

• Korean Journal of Computational Design and Engineering
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• v.20 no.1
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• pp.1-10
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• 2015

Using BIM (Building Information Modeling)-based design information to analyze various engineering processes has been widely adopted in construction projects. However, since typical building permit processes often require traditional 2D-based design information for submission and to obtain building approval, there are some challenges in delivering such data thru BIM-based design information. This paper proposed a methodology to generate and meet information requirements for permit applications and approvals based on BIM-based design information. To that end, we analyzed the required information necessary to make submissions for building approvals using the Seumter system. We then suggested a process to collect the required information from BIM-based data, and classified this into two types: BIM-internal and BIM-external information requirements. In addition, we proposed three algorithms to extract and to match between extracted BIM data and BIM-internal information requirements using IFC(Industry Foundation Classes). The proposed methodology enables to ensure the efficiency and the accuracy when providing data for building permit review and approval.

• Journal of KIBIM
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• v.9 no.4
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• pp.31-40
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• 2019

With Building Information Modeling(BIM) becoming the de facto standard for data sharing in the AEC industry, additional needs have increased to ensure the data integrity of BIM models themselves. Although the Industry Foundation Classes provide an open and neutral data format, its generalized schema leaves it open to data loss and misclassifications This research applied deep learning to automatically classify BIM elements and thus check the integrity of BIM-to-IFC mappings. Multi-view CNN(MVCC) and PointNet, which are two deep learning models customized to learn and classify in 3 dimensional non-euclidean spaces, were used. The analysis was restricted to classifying subtypes of architectural walls. MVCNN resulted in the highest performance, with ACC and F1 score of 0.95 and 0.94. MVCNN unitizes images from multiple perspectives of an element, and was thus able to learn the nuanced differences of wall subtypes. PointNet, on the other hand, lost many of the detailed features as it uses a sample of the point clouds and perceived only the 'skeleton' of the given walls.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4176-4184
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• 2015

Cost Estimate for alternative in design phase of construction become criterion for profitability and feasibility analysis of projects. Initial design phase performed schematic estimate based on similar data in the past. The quantity take-off according to estimators experience and calculation method are occurred different or missing. IT (Information Technology) technology evolution has been promoting BIM technology in construction. It is changing the paradigm of planning, design, construction and maintenance phase throughout the construction project. A number of studies have been attempted to apply BIM technology in the construction. In this paper, we propose schematic estimation framework linking standard format IFC (Industry Foundation Classes) and estimate related knowledge. As a result, it performs a cost prediction for decision-making in the design phase, and expected to overcome the limitations of previous studies. In addition, it is possible actively coping with changes.

• Korean Journal of Construction Engineering and Management
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• v.13 no.3
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• pp.43-55
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• 2012

A methodology for BIM Quality Assurance in the construction industry is becoming increasingly an important issue to determine the reliability of BIM. However, the quality assurance of BIM is currently limited to check 3D models, such as clash detection and space layout while verification methods for disciplinary BIM results from structural engineering, mechanical engineering, and estimation do not exist yet. Particularly, in the BIM-based estimation mathematical equations to take off quantities are not clearly exposed so that the results are not quite accepted at practices. With the concept of reliability engineering defined in the manufacturing industry to improve reliability of outcomes of BIM-based quantity take-off, impacting factors that affect reliability of BIM-based quantity take-off were derived. It was found that the factors also include the modeling method and the features of a BIM tool. Therefore, this research aims to propose modeling and verification methods to improve reliability of BIM-based quantity take-off through the pilot test that was performed with commercial BIM tools and IFC-based BIM data.

• Korean Journal of Construction Engineering and Management
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• v.16 no.1
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• pp.24-34
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• 2015

As recent multilateral collaboration design system has been advanced, BIM based data exchange is a key factor for successful next generation building project. Even though many studies have been trying to set up a data compatibility system for collaboration, There are still a lot of problem in data exchange between design and engineering phase. Therefore, In this study, we analysis causes of problem for information exchange and suggest a IFC based Information exchange framework for improving BIM based design collaboration environment. In order to find out problems that hinder establishment of advanced open BIM information exchange, proper analysis about transition of process from current and to-be BIM based design collaboration process is important, at first. From analysis of main obstacles to information exchange, this research suggests solution plan using open API and IFC based BIM collaboration supporting system. The suggested open API solution named Integrity feedback system perform a role making up for weak point derived from IFC based data exchange. And main system suggestion about framework for IFC based information exchange reflect technological system support, requirement of function for collaboration including API/BCF plug-in.

• Journal of the Korea Institute of Building Construction
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• v.20 no.6
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• pp.505-514
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• 2020

This research introduces the method of developing Korea BIM standard (KBIMS) architectural and structural element library and the methodology of converting KBIMS IFC property names with special characters. Diverse BIM tools are utilizing in project, however BIM library researches lack diversity on BIM tool selection. This research described the method to generate twelve categories and seven hundred and ninety-three elements library containing geometrical and numerical data in CATIA V6. KBIMS has its special property data naming systems which was the challenge inputting to ENOVIA IFC database. Three mapping methods for special naming characters had been developed and the ASCII code method was applied. In addition, the convertor prototype had been developed for searching and replacing the ASCII codes into the original KBIMS IFC property names. The methodology was verified by exporting 2,443 entities without data loss in the sample model conversion test. This research would provide a wider choice of BIM tool selection for applying KBIMS. Furthermore, the research would help on the reduction of data interoperability issues in projects. The developed library would be open to the public, however the continuous update and maintenance would be necessary.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.277-288
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• 2021

Building information modeling (BIM) element to industry foundation classes (IFC) entity mappings need to be checked to ensure the semantic integrity of BIM models. Existing studies have demonstrated that machine learning algorithms trained on geometric features are able to classify BIM elements, thereby enabling the checking of these mappings. However, reliance on geometry is limited, especially for elements with similar geometric features. This study investigated the employment of relational data between elements, with the assumption that such additions provide higher classification performance. Neural structured learning, a novel approach for combining structured graph data as features to machine learning input, was used to realize the experiment. Results demonstrated that a significant improvement was attained when trained and tested on eight BIM element types with their relational semantics explicitly represented.