• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2001년도 학술대회지
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• pp.468-471
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• 2001

건설공사를 수행하는데 있어 초기 프로젝트 계획에 대한 중요성이 널리 인식되어 지고 있다. 프로젝트와 관련된 방대한 양의 실적자료들이 초기 계획단계에서부터 활용될 수 있다면, 프로젝트의 전체 범위와 비용견적을 비교적 정확하게 예측할 수 있는 중요한 원천이 될 것이다. 그러나, 공사 실적자료들의 축적, 분석, 활용의 정도가 미비하여 상당한 양의 유용한 정보들이 쉽게 사장되거나 적용되지 못한 채 보유되고 있다. 이런 문제점을 개선하기 위해서는 건설사업 참여자들이 획득한 수많은 양의 실적자료들을 유용한 지식으로 획득, 저장, 공유, 활용할 수 있는 체계적, 종합적인 실적자료관리시스템을 개발하여 관리할 필요성이 있다. 본 연구에서는 이러한 기술적 흐름과 병행하여, 현재 건설공사의 실적자료들을 효과적으로 관리하여 유사프로젝트에 유용하게 이용하고자 한다

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.393-399
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• 2009

This paper introduces an automated tool named Advanced Stochastic Schedule Simulation System (AS4). The system automatically integrates CPM schedule data exported from Primavera Project Planner (P3) and historical activity duration data obtained from a project data warehouse, computes the best fit probability distribution functions (PDFs) of historical activity durations, assigns the PDFs identified to respective activities, computes the optimum number of simulation runs, simulates the schedule network for the optimum number of simulation runs, and estimates the best fit PDF of project completion times (PCTs). AS4 improves the reliability of simulation-based scheduling by effectively dealing with the uncertainties of the activities' durations, increases the usability of the schedule data obtained from commercial CPM software, and effectively handles the variability of the PCTs by finding the best fit PDF of PCTs. It is designed as an easy-to-use computer tool programmed in MATLAB. AS4 encourages the use of simulation-based scheduling because it is simple to use, it simplifies the tedious and burdensome process involved in finding the PDFs of the many activities' durations and in assigning the PDFs to the many activities of a new network under modeling, and it does away with the normality assumptions used by most simulation-based scheduling systems in modeling PCTs.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2002년도 학술대회지
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• pp.123-130
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• 2002

건설공사 관리프로세스 및 관리체계는 건설공사를 성공적으로 수행하는데 필수적인 요소이다. 이러한 공사관리체계는 건설공사를 계획${\cdot}$관리함에 있어서 일정, 비용, 성과측정, 현금흐름(cash flow)예측 등의 업무체계가 통합 또는 연계될 때, 효과적인 관리도구로 활용할 수 있으며, 이는 건설공사에 대한 정확하고 신속한 의사결정을 도모하고 관리의 효율성을 증대시킬 수 있다. 이를 위해 우리나라 건설업계에서는 건설공사의 수행과정을 효과적으로 계획하고 통제, 관리한 수 있도록 업무프로세스를 혁신하고, 이를 지원하는 공사관리시스템의 구축에 많은 시간과 노력을 기울이고 있다. 본 연구에서는 건설공사를 효과적으로 계획, 관리하여 업무프로세스의 투명성을 확보하고 비용효과를 증대시키기 위한 방안의 일환으로 구축되고 있는 공사관리시스템의 개발 동향과 기능적 특징을 비교 분석하고, 구축된 공사관리시스템이 유효하게 기능할 수 있도록 하기위한 현안과제와 대응방안을 제시하고 있다.

• 한국건설관리학회논문집
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• 제5권1호
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• pp.168-174
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• 2004

건설공사 관리프로세스 및 관리체계는 건설공사를 성공적으로 수행하는데 필수적인 요소이다. 이러한 공사관리체계는 건설공사를 계획$\cdot$관리함에 있어서 일정, 비용, 성과측정, 현금흐름 예측 등의 업무체계가 통합 또는 연계될 때, 효과적인 관리도구로 활용할 수 있으며, 이는 건설공사에 대한 정확하고 신속한 의사결정을 도모하고 관리의 효율성을 증대시킬 수 있다. 이를 위해 우리나라 건설업계에서는 건설공사의 수행과정을 효과적으로 계획하고 통제, 관리할 수 있도록 업무프로세스를 혁신하고, 이를 지원하는 공사관리시스템의 구축에 많은 시간과 노력을 기울이고 있다. 본 연구에서는 건설공사를 효과적으로 계획, 관리하여 업무프로세스의 투명성을 확보하고 비용효과를 증대시키기 위한 방안의 일환으로 구축되고 있는 공사관리시스템의 개발 동향과 기능적 특징을 비교 분석하고, 구축된 공사관리시스템이 유효하게 기능할 수 있도록 하기위한 현안과제와 대응방안을 제시하고 있다.

• Journal of Construction Engineering and Project Management
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• 제7권1호
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• pp.30-36
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• 2017

Most local agencies such as counties and small cities continuously express difficulties in making technically and financially defensible decisions on their pavement infrastructure maintenance and rehabilitation. Unlike pavement systems managed by state highway agencies, the total lane-miles of many local pavements are significantly short and they are managed by a limited number of staff who typically have multiple responsibilities. Most local agencies also do not have historical pavement performance data and the lack of a systematic decision making framework exacerbates the problem. A structured framework and an easily accessible decision support tool that reflects their local requirements, practices and operational conditions would greatly assist them in making consistent and defensible decisions. This study fills this gap by developing a systematic pavement treatment selection framework and a spreadsheet based tool for local agencies. It is expected that the proposed framework will significantly help local agencies to improve their pavement asset management practices at the project level.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.533-538
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• 2005

An outline of an Artificial Neural Network (ANN) model for bridge condition rating and the results of a pilot study are presented in this paper. Most BMS implementation systems involve an extensive range of data collection to operate accurately. It takes many years to effectively implement a BMS using existing methodologies. This is due to unmatched data requirements. Such problems can be overcome by adopting the ANN model presented in this paper. The objective of the proposed model is to predict bridge condition ratings using historical bridge inspection data for effective BMS operation.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.