• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.689-692
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• 2006

This paper presents the developments of Grid computing architecture which is use many data and resources from distributed and parallel system for construction test equipments, i.e., large scale computer networks meant to provide access to massive computational facilities for very large communities of users, drawing upon experiences of existing Grids architecture. In this paper, we present an efficient way to construct a construction test equipment infrastructure.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.167-167
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• 2022

최근 빅데이터에 관련된 연구에 있어 데이터의 품질관리에 대한 논의가 꾸준히 이뤄져 오고 있다. 특히 이미지 처리 및 분석에 활용되어온 딥러닝 기술의 경우, 분류 작업 및 패턴인식 등으로부터 데이터의 특징을 추출함으로써 비지도학습(Unsupervised Learning)을 가능하게 한다는 장점이 있음에도 불구하고 빅데이터를 다루는 과정에 있어 용량, 다양성, 속도 및 신뢰성 측면에서의 한계가 있었다. 본 연구에서는 CCTV 영상을 활용한 강수량 산정 모델 개발에 있어 예측 정확도 향상 및 성능 개선을 도모할 수 있는 데이터 전처리 방법을 제안하였다. 서울 근린 AWS 4개소 지역(김포장기, 하남덕풍, 강동, 성남) 및 중앙대학교 지점 내 CCTV를 설치한 후, 최대 9개월의 영상을 확보하여 강수량 산정을 위한 딥러닝 모델을 개발하였다. 배경분리, 조도조정, 영역설정, 데이터증진, 이상데이터 분류 등이 가능한 알고리즘을 개발함으로써 데이터셋 자체에 대한 전처리 작업을 수행한 후, 이에 대한 결과를 기존 관측자료와 비교·분석하였다. 본 연구에서 제안한 전처리 방법들을 적용한 결과, 강수량 산정 모델의 예측 정확도를 평가하는 지표로 선정한 평균 제곱근 편차(Root Mean Square Error; RMSE)가 약 30% 감소함을 확인하였다. 본 연구의 결과로부터 CCTV 영상 데이터를 활용한 강수량 산정의 가능성을 확인할 수 있었으며 특히, 딥러닝 모델 개발시 필요한 적정 전처리 방법들에 대한 기준을 제시할 수 있을 것으로 판단된다.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.145-152
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• 2004

Many construction companies have developed many kinds of information system to integrate the information created in the phase of planning, design, procurement, construction, and maintenance of construction project. However, the previous researches for the state of applying information technologies and the efforts of the analysis of the present information system launched in domestic construction industry is very rare. There also has been short of research on the construction decision-making support theory in construction business and industry. Hence this paper would contribute in identifying the state of information technologies and the theory of decision-making support of the information system in general construction company. and suggest the characteristic model on the information system for the construction decision-making support. The model consists of the two dimensions; (1) organizational hierarchy (2) data analyzing technology. This research, especially, can be expected to initiate the discussion on framework for understanding the construction decision-making support system in construction industry. The model is nut a practical methodology, but a window that offers a new perspective un sources of information system in each construction company, and thus can provide a clue of the useful guide to construction information system development.

• Journal of the Korea Institute of Building Construction
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• v.11 no.2
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• pp.100-107
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• 2011

As construction duration significantly influences the performance and the success of construction projects, it is necessary to appropriately manage the impact factors affecting construction duration. Recently, interest in the construction industry has been rising due to the recent change in the construction legal system, and the competition among the construction companies on construction time. However, the impact factors are extremely diverse. The existing productivity data on impact factors is not sufficient to properly identify the impact factor and measure the productivity from various perspectives, such as subcontractor, time, crew, work and so on. In this respect, a multidimensional analysis by a data warehouse is very helpful in order to view the manner in which productivity is affected by impact factors from various perspectives. Therefore, this research proposes a method that effectively takes the diverse productivity data of impact factors, and generates a multidimensional analysis. Decision tree analysis, a data mining technique, is also applied in this research in order to supply construction managers with appropriate productivity data on impact factors during the construction management process.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.89-96
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• 2016

Most data generated in construction projects is unstructured text data. Unstructured data analysis is very needed in order for effective analysis on large amounts of text-based documents, such as contracts, specifications, and RFI. This study analysed previously performed project's bid related documents (bidder inquiry) in overseas construction projects; as a results of the analysis frequent words in documents, association rules among the words, and various document topics were derived. This study suggests effective text analysis approach for massive documents with short time using text mining technique, and this approach is expected to extend the unstructured text data analysis in construction industry.

• Korean Journal of Construction Engineering and Management
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• v.18 no.5
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• pp.20-31
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• 2017

As global interests in the 4th Industrial Revolution have recently increased, it becomes critical for the construction industry to pro-actively cope with it. For effective actions, the construction industry needs to make active use of 4th Industrial Revolution technologies based on the up-to-date understanding of the trends of construction technology development employing the 4th Industrial Revolution technologies. The objective of the study is to investigate and identify key trends of ICT construction technology development over the last ten years based on Big Data Analytics. The study identifies eleven key trends and discusses that ICT construction technology development has not been as active as expected and software technologies have been less developed compared to hardware technologies.

• Construction Engineering and Management
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• v.12 no.5
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• pp.55-59
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• 2011

• Construction Engineering and Management
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• v.17 no.5
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• pp.29-33
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• 2016

• Construction Engineering and Management
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• v.20 no.2
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• pp.62-66
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• 2019

• Water for future
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• v.55 no.3
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• pp.54-63
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• 2022