• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.848-852
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• 2007

Recently the domestic construction companies have been occurred to the various demand of information. Also information quantity is increasing. But using information have been stated in low why large information quantity. The other side, the various Metadata have been proposing in the many field for an effective search and an application. So the Metadata for using construction information must be built. In this study, we have founded information of construction companies, present condition of information, concept of Metadata and recent report. And we have presented the Metadata system model and building process of information system. We have analyzed the Metadata element of construction. This study is the foundation for the Metadata using system.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.99-107
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• 2023

Recently, the rate of death and safety accidents at construction sites is the highest among all kinds of industries. In order to apply artificial intelligence technology to construction sites, it is essential to secure a dataset which can be used as a basic training data. In this paper, a number of image data were collected through actual construction site, for which major construction equipment objects mainly operated in civil engineering sites were defined. The optimal training dataset construction was completed by annotation process of about 90,000 image dataset. Reliability of the dataset was verified with the mAP of over 90 % in use of YOLO, a representative model in the field of object detection. The construction equipment training dataset built in this study has been released which is currently available on the public data portal of the Ministry of Public Administration and Security. This dataset is expected to be freely used for any application of object detection technology on construction sites especially in the field of construction safety in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.661-670
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• 2006

The utilization of simulation has been limited in planning construction process because it is difficult to collect data and build a model using simulation method. This study collects construction operation data and analyzes the characteristics of its distribution. Through the statistical analysis on the empirical data, this study identifies Beta distribution functions is one of the most proper in duplicating the characteristics of construction equipment operation data into a computer simulation. The information obtained in this study can support preparing input data for another simulation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.375-378
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• 2020

본 연구는 토지 보상비의 추정 모델 개발을 위해서 건설 CALS (Continuous Acquisition & Life-cycle Support) 시스템의 내부데이터와 개별공시지가 및 표준지 공시지가 등의 외부데이터, 그리고 개발된 추정 모델의 고도화를 위한 개별공시가 데이터를 기반으로 생성된 데이터를 활용하였다. 이렇게 수집된 3가지 유형의 데이터를 분석하기 위해서 기존 선형 모델 또는 의사결정나무 (Tree) 기반의 모델상 과적합 오류를 제거할 경우 매우 유용한 알고리즘으로 Decision Tree 기반의 Xgboost 알고리즘을 데이터 분석 방법론으로 토지 보상비 추정 모델 개발에 활용하였다. Xgboost 알고리즘의 고도화를 위해 하이퍼파라미터 튜닝을 적용한 결과, 실제 보상비와 개발된 보상비 추정 모델의 MAPE(Mean Absolute Percentage Error) 범위는 19.5%로 확인하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.301-303
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• 2015

국내 건설행정 투명화 및 경쟁력 향상 목적으로 개발된 건설사업정보시스템에 정부와 운영주체는 다양한 기능개선 방안과 관련 연구를 수행하며 시스템 성능을 개선시켜왔다. 그러나 기 추진된 성능향상 방안이 공공업무 처리에 중점 되어 대국민 사용자를 위한 콘텐츠 및 기능 등의 서비스가 미흡한 상황이다. 이에 본 논문에서는 건설사업정보 건설인허가시스템의 도로점용장소별 허가현황 기능을 중심으로 빅 데이터를 이용한 허가현황 정보 제공 방안을 제안하였다. 제안한 기능개선 방안은 기 구축된 비정형 데이터를 빅 데이터 기반으로 재분석하여 구글 맵에 가시화함으로써 공공업무 데이터 처리 뿐만 아니라 대국민 서비스를 위한 콘텐츠 제공이 가능하도록 하였다. 뿐만 아니라 그동안 축적된 15TB이상의 건설관련 데이터의 재활용 가능성을 시사함으로써 시스템 활용성 증대 및 개편 방향에 도움이 될 것으로 판단된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.758-759
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• 2019

4차산업혁명 시대의 도래로 빅데이터, AI 기술 등을 이용하여 데이터 수집, 분석, 가공, 활용에 관심이 높아졌다. 정부도 공공부문 정보시스템의 보유데이터를 개방하여 산업 전반에 데이터 연계·활용을제고하고 있다. 본 연구는 도로건설사업의 업무처리를 지원하는 건설사업정보시스템(CALS)에 보유중인 건설데이터를 개방하기 위한 방안을 마련하였다. 이를 위해 건설데이터 중 개방 가능한 데이터를 선정하고 키워드를 통한 데이터 및 파일의 정보검색 환경과 데이터 공개서비스 구축방법을 제시하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.101-104
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• 2005

최근 들어 건설분야에서도 정보화를 통해 건설업무의 생산성 향상과 비용 절감을 위해 많은 시스템이 개발되고 있다. 이에 따라 건설정보시스템들에서 생성, 유통되는 정보자원이나 데이터가 다양하고 방대해지고 있다. 이를 위해 본 연구는 건설정보시스템들 간에 정보자원이나 데이터의 상호 연계성을 확보함으로써 언제, 어디서든지 필요한 정보자원이나 데이터을 추출하여 공유 및 재사용할 수 있는 메타데이터시스템 구축 방안을 마련하였고, 16종의 건설CALS 메타데이터를 개발하였다.

• Korean Journal of Construction Engineering and Management
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• v.7 no.2 s.30
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• pp.127-137
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• 2006

Productivity is important to evaluate an efficiency of performed work and organization in construction industry. The productivity should be defined as activity level rather than macro level in order to effectively use productivity data and manage a project. The primary objective of this study is to develop a construction productivity management system using data warehouse, OLAP and data mining technologies which enables to easily accumulate the construction productivity data and perform multi layer analysis. Finally, it is anticipated that the effective use of the developed system would be able to measure the result of project and make a plan of the similar project with reliability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.127-134
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• 2022

Recently, policies and research to prevent increasing construction accidents have been actively conducted in the domestic construction industry. In previous studies, the prediction model developed to prevent construction accidents mainly used only structured data, so various characteristics of construction sites are not sufficiently considered. Therefore, in this study, we developed a machine learning-based construction accident prediction model that enables the characteristics of construction sites to be considered sufficiently by using both structured and text-type unstructured data. In this study, 6,826 cases of construction accident data were collected from the Construction Safety Management Integrated Information (CSI) for machine learning. The Decision forest algorithm and the BERT language model were used to train structured and unstructured data respectively. As a result of analysis using both types of data, it was confirmed that the prediction accuracy was 95.41 %, which is improved by about 20 % compared to the case of using only structured data. Conclusively, the performance of the predictive model was effectively improved by using the unstructured data together, and construction accidents can be expected to be reduced through more accurate prediction.