• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.468-471
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• 2001

The importance of early project planning is broadly recognized for construction projects. From the planning step, If extensive historical data related with the project is applied effectively, It can be major resource for estimating cost and project scope. However, The accumulation, analysis, and application of historical data is not sufficient in Korea. So useful information of construction project has disappeared. In order to solve the problems, Project Historical Data Management Systems is need to be developed. The purpose of this study is to analyze current problems and to find the method to utilize historical data in similar project.

• International conference on construction engineering and project management
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• 2009.05a
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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.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.123-130
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• 2002

Project plan and control is a highly skilled task which is vital for business success, and Project control systems are important for successful project execution. To support construction process management effectively, an integrated cost and schedule control function is required to collect quality data in a timely manner and to provide quality historical data for future planning of new projects. In Korea construction industry, the development and implementation cases of a project control system which to efficiently support construction project planning and controlling processes, have been increased to ensure a project success and the profitability of a construction company. To contribute to improving practical effectiveness of a project control system, the paper reviewed and analyzed the current trends and functional features of current project control systems. And it also described the pending issues and their solutions faced on previous project control systems.

• Korean Journal of Construction Engineering and Management
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• v.5 no.1 s.17
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• pp.168-174
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• 2004

Project plan and control is a highly skilled task which is vital for business success, and Project control systems are important for successful project execution. To support construction process management effectively, an integrated cost and schedule control function is required to collect quality data in a timely manner and to provide quality historical data for future planning of new projects. In Korea construction industry, the development and implementation cases of a project control system which to efficiently support construction project planning and controlling processes, have been increased to ensure a project success and the profitability of a construction company. To contribute to improving practical effectiveness of a project control system, the paper reviewed and analyzed the current trends and functional features of current project control systems. And it also described the pending issues and their solutions faced on previous project control systems.

• Journal of Construction Engineering and Project Management
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• v.7 no.1
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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.

• International conference on construction engineering and project management
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• 2009.05a
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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.

• International conference on construction engineering and project management
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• 2005.10a
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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.

• International conference on construction engineering and project management
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• 2022.06a
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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.