• Korean Journal of Construction Engineering and Management
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• v.5 no.3 s.19
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• pp.166-173
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• 2004

This research develops a preliminary system prototype of construction data warehouse of a construction industry application to provide the construction manager with electronic decision supporting information. Construction data warehouse technology is a contractor-focused concept that provides electronic information analyzed from the separately stored database by management dimension, management subject, and data warehouse technology modules. First the authors reviews the characteristics of data warehouse technology and reviewed the conceptually designed architecture of previous study, then propose the architecture of construction data warehouse system and real application alternatives of each technology module to confirm the construction adaptability of the data warehouse technology through a case study.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.831-838
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• 2022

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.212-213
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• 2020

In this study, the market growth potential for the construction sector and the company's ease of entry were analyzed by using construction scale and risk data among global insight data. The survey was conducted in 74 countries. The purpose is to provide basic data whose result can be used as policy-based data for the overseas construction industry.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.205-209
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• 2015

A significant amount of data including ongoing construction activities, work quantities, resources utilized by contractors, and site conditions is collected in highway construction sites on a daily basis by resident engineers. This data is commonly known as daily work reports (DWRs) in the U.S. Although a lot of time and effort is invested in collecting the DWR data, its utilization has been very limited. This paper discusses current practices of collecting and utilizing DWR data among various Departments of Transportation in the U.S., and discusses the challenges and opportunities for better collection and utilization of the data. An extensive literature review and two nationwide surveys in the U.S. were conducted as a part of this study. Finally, it provides a set of recommendations to effectively address the challenges identified and maximize the benefits of utilizing DWR data such as supporting various decisions for highway project development process. The findings of this study are implementable ideas that can aid DOTs in making data-driven decisions throughout the project development processes in the future.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.359-360
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• 2023

As data management and analysis technology advances, there is active discussion on how to utilize data generated in construction projects. Among them, the materials produced during the supervision work are highly useful because their generation cycle and format are regulated according to relevant laws. In this study, we analyzed whether the data produced during the supervision work in the construction phase of apartment housing can be utilized for project performance management. First, this study identified key data necessary for performance management through FGI with experts in the field of apartment housing. Next, we collected supervisory data from the case project and identified whether the data was generated, its cycle, and storage format. As a result of the analysis, the supervisory data contained various information that could measure the performance of construction projects and had the advantage of standardized data. In the future, utilizing supervisory data is expected to enable effective performance management of apartment housing construction projects.

• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.77-85
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• 2015

The data are often missed and many errors of the data are generated in the input process for the post evaluation system on the construction projects, and the reliability of the data falls down much. Accordingly, the detailed analysis about missing and error of data was conducted to ensure reliability of the analysis results about post evaluation on the construction projects. As results in this study, a lot of input data were missed at the initial construction phase, and the data errors were found in the inaccuracy of reference reports, the lack of understanding about input data, and the failure of KRW unit.

• Journal of KIBIM
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• v.12 no.4
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• pp.1-18
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• 2022

New Fabrication Facility (FAB) construction is performed with Building Information Modeling (BIM) based design. The BIM design data keep updated during the FAB construction. To improve fast-track construction management, a Fabrication Facility Augmented Reality (FABAR) was developed. This study introduces a FABAR system development process and shows performance evaluation results of the FABAR prototype system. The FABAR is implemented with three major modules: Augmented Reality (AR) visualization unit (Room-box) to transfer big BIM data to AR data, AR registration and tracking unit to match AR with real scape and to keep AR coordination in real, and AR data management unit to enhance usability. The prototype performance results were as follows: visualization of design BIM data via AR within 24 hours, precise AR registration and tracking registration, and appropriate usability to support FAB construction management at site. The results indicate that the FABAR is applicable for FAB construction management. Especially, the BIM data transformation method using Room-box in this study signifies a new construction management approach using fluctuating BIM design data in the fast track construction condition.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1100-1105
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• 2022

Denoising, registering, and detecting changes of 3D digital map are generally conducted by skilled technicians, which leads to inefficiency and the intervention of individual judgment. The manual post-processing for analyzing 3D point cloud data of construction sites requires a long time and sufficient resources. This study develops automation technology for analyzing 3D point cloud data for construction sites. Scanned data are automatically denoised, and the denoised data are stored in a specific storage. The stored data set is automatically registrated when the data set to be registrated is prepared. In addition, regions with non-homogeneous densities will be converted into homogeneous data. The change detection function is developed to automatically analyze the degree of terrain change occurred between time series data.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.121-123
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• 2017

The widespread dissemination of the green building certification system has led to the ongoing development of information management technologies with the aim to effectively utilize construction product information. Among them, a data crawling technology enables to collect the data conveniently and to manage large volumes of construction product information in Korea and overseas. However, without a standardized classification system, it is difficult to efficiently utilize information, and problems such as an additional work for classifying information or information-sharing errors. Therefore, this study suggests to present a necessity for the standardization of the information classification system through expert interviews, and to compare construction product classification systems in Korea and overseas. This study is expected to present a necessity for the effective management of construction product information and the standardization of information-sharing with regard to various construction certifications.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1243-1249
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• 2023

The Public Data Act mandates that every public institution must make efforts to facilitate convenient access to public data for all and take necessary measures to universally expand the right to use such data. Furthermore, it specifies that the head of a public institution must provide the data held and managed by that institution to the citizens. The Construction Project Information Disclosure Service aims to increase the utilization of data within the Construction CALS System, creating new added value by disclosing information and fostering communication. This service seeks to enhance public interest and transparency, support the creation of new businesses based on construction project data, and stimulate related industries. Since 2019, a total of 26 types of information have been disclosed through this service. As the volume of disclosed information continues to increase, there has been a consistent demand for reducing burden on data providers and enhancing user functionality. This study analyzes the current status of the Construction CALS Information Disclosure System to identify its existing challenges. Subsequently, it establishes a systematic approach to the data opening process and proposes enhancements to information disclosure and search functionalities for addressing these challenges.