• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.481-482
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• 2007

건설분야에서 표준화된 정보체계에 따라 XML 전자문서를 개발할 수 있도록 UN/CEFACT의 핵심요소 개발 지침을 기반으로 XML 라이브러리를 개발하였고 XML 라이브러리에 핵심요소, 비즈니스 정보체계, 핵심요소 유형의 기초가 되는 한정어 데이터 유형(Unqualified Data Type)을 포함하고 있다. 본 연구는 건설정보의 특성을 반영한 16종의 한정어 데이터 유형과 속성정보를 정의하고 XML 스키마를 개발하였다.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.143-150
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• 2003

Using integrated construction project data is necessary for efficient management of construction projects. Recently IFC(Industry Foundation Classes) are proposed as integration method based on interoperability and there have been several cases of system integration based on IFC. The concept of interoperability proposes the construction object as data sharing protocol between systems for system integration and independence of each system. This study proposes system development strategy by phases considering work scope and procedure of processes based on the concept of interoperability. This is proposed to overcome tendency of developing system which doesn't support real work processes as result of inclining to the format of IFC instead of properly using IFC's interoperability concept to real work scope and procedure.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.153-161
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• 2004

The systematic material and labor management planning should be established on accomplished EVM data. The matrix method of integrated cost and schedule was used with common category concept according to the construction project control system. The construction project control system was suggested through analyzing process and data modeling based on integrated cost, schedule and material. Information of construction project can be developed the relationship between the field data and the integrated cost, schedule database. Process and data modelling is provide a standard data format which are related to the material, labor management based on integrated cost, schedule database.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.841-849
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• 2023

Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.813-818
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• 2007

Construction productivity is a key factor for efficiency evaluation of construction work process, project performance measurement, and basic data of work plan in construction industry. However, although construction productivity is important in construction industry, gathering methodology and analyzing methodology of productivity data are not well-organized therefore productivity data is not utilized in the construction industry The purpose of this study is to develop productivity prediction system using data mining technology based on activities and to suggest frameworks about productivity data collection, accumulation.

• Journal of KIBIM
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• v.12 no.1
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• pp.72-80
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• 2022

It has become mandatory to designs by BIM in construction. It is urgent to make accurate decisions through the linkage between complex and various types of data in projects. In particular, block-chain based data sharing process (using BIM files, general construction submitted files) is essential to support reliable decision making in complex data flood systems. Prior to developing data sharing system based on block-chain, in this paper, a data linkage method is proposed so that practitioners can simultaneously utilize existing construction information and BIM data. Examples are shown based on the construction classification system and file expression, and incentive strategies are explored through a survey so that heterogeneous information can be used at the same time in overall projects.

• Construction Engineering and Management
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• v.22 no.4
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• pp.12-17
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• 2021

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.437-444
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• 2008

최근 유비쿼터스 시대를 맞아 건설 분야에서 이용되는 CAD 자료에서 GIS 자료로의 변환 및 융복합에 대한 요구가 증대되면서 상호변환을 위한 연구가 활발하게 진행되고 있다. 본 연구에서는 건설도면 CAD 데이터를 활용하여 수치지도의 건물데이터를 수정, 갱신하기 위한 방법론의 일환으로 건설도면의 건물외곽선을 추출하여 수치지도의 건물데이터 수준으로 일반화하는 선형 단순화 알고리즘을 비교 분석하였다. 선형 단순화 알고리즘은 Douglas-Peucker 알고리즘, Lang 알고리즘, Reumann-Witkam, Opheim 알고리즘을 적용하였으며 분석방법으로는 시각적 분석, 절점 수, 총길이, 면적 변화율 분석 그리고 각 절점이 수치지도 작성내규를 만족하는 비율을 이용하였다. 분석 결과 Douglas-Peycker 알고리즘이 시각적 측면과 절점 수 감소율 측면에서 상대적으로 우수한 단순화 결과를 보여주었으나 수치지도 작성내규 만족도 측면에서는 공통적으로 $50{\sim}60%$ 수준의 만족도를 보이고 있어 국내의 수치지도의 건물데이터를 작성하기 위한 단순화 기법으로는 한계가 있는 것으로 나타났으며 이를 만족시키기 위한 일반화 알고리즘의 개발이 필요하다고 판단된다.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.686-689
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• 2008

In the past decade, various data mining techniques have been used in construction engineering as a means to make informed decisions through the aid of useful knowledge discovered from historical data. Researchers in the construction domain are often confronted with a challenge to derive a meaningful conclusion with a limited sample of data. However, when the data size is small, the proposed results are often illogical. Even if the derived results are technically flawless, sometimes it is difficult to reproduce these results by using the same analysis method when a different set of data is used. This paper reviews some problems that stem from limited data size, and discusses several recommendations for dealing with these problems.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.