• Journal of the military operations research society of Korea
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• v.36 no.1
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• pp.29-38
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• 2010

In Korea weapon system acquisition processes, it's required a cost estimation report obtained from a commercial cost model. The PRICE model is generally used as a cost estimation model in Korea. However, the model uses American historical R&D data and it's output cost component is different from our cost component of defense accounting system. Also, we found that estimating results show about 10% of difference when we comparing with actual costs in 44 finished weapon acquisition projects. There are some limitations in calibration to increase an accuracy of the PRICE model because it's difficult obtain good real input data, detailed cost and technical data in low level WBS. So, only 8% of the defense R&D projects are calibrated and validation of calibration results is more difficult. Therefore, we studied the standard calibration process and performed the calibration about the MCPLXS/E parameters of the PRICE model based on actual cost data. In order to obtain a good calculation result, we collected the actual material costs from the defense industry companies. Our results can be used for an reference in similar weapon system R&D and production cost estimation cases.

• Journal of the Korea Institute of Building Construction
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• v.5 no.1 s.15
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• pp.111-121
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• 2005

This study purposed to derive a model to estimate appropriate construction costs for the application of the estimation system based on historical construction data. For this purpose, it analyzed data ($1999\sim2004$) on the construction costs of reinforced concrete apartments (Long-Lamina-type apartments), which have been highly standardized and have a lot of relevant objective data using statistical analysis techniques and developed an estimation model.

• Korean Management Science Review
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• v.23 no.2
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• pp.75-91
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• 2006

The purpose of this paper is to propose a new software development cost estimation method using SVR(Support Vector Regression) SVR, one of machine learning techniques, has been attracting much attention for its theoretic clearness and food performance over other machine learning techniques. This paper may be the first study in which SVR is applied to the field of software cost estimation. To derive the new method, we analyze historical cost data including both well-known overseas and domestic software projects, and define cost drivers affecting software cost. Then, the SVR model is trained using the historical data and its estimation accuracy is compared with that of the linear regression model. Experimental results show that the SVR model produces more accurate prediction than the linear regression model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.667-676
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• 2014

This study has conducted case studies in order to suggest alternatives to the historical data-based estimate system. Price fluctuation calculation methods based on historial cost indexes, standard estimate and construction cost indexes were applied to 9 road construction sites in Jeju for an analysis. As a result, in 5 construction sites (about 56% of 9 sites), the index control rate calculated based upon historical data-based estimate system was higher than that calculated based upon standard estimate and construction cost indexes. Thus the establishment of the requirements for the adjustment of contract price due to price fluctuation delays, which leads to a significant difference in price fluctuation amount. And, in an analysis of construction cost indexes, the indexes for road construction were used for calculating index control rate which ranges from 2.0 to 9.4 percent, indicating the time of construction amount and price fluctuation application has a significant influence on index control rate.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.444-447
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• 2006

Construction managers need to pay a close attention to the resource utilization in order to deliver the construction project successfully. Construction scheduling is crucial for resource control in that it provides information when and how much to bring down work force to sites. In scheduling, activity duration is projected based on the productivity of historical data or the intuition of scheduler. This paper studies the opportunity of applying cost-based productivity for estimating activity duration. For cost-based productivity, the cost of resource is used as an input and the work quantities as an output. Out of historical data, regression model has been developed to understand the validity of applying cost-based productivity in projecting activity duration. The result of study will work as a prerequisite for implementing the environment of database-based construction scheduling.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.77-81
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• 2015

The unit price contracting is the standard contracting method for highway projects in the U.S. As a result, state highway agencies have collected a large amount of historical bid data that they can use to determine engineer's estimates for future projects. The estimator must carefully consider various characteristics of a new project such as its location to determine an engineer's estimate as accurate as possible before bid letting. Higher cost estimates can result in the loss of the available budget and lower cost estimates may lead to deferral and delay of projects. The study uses the historical bid data obtained from Iowa Department of Transportation and develops a Geographic Information System (GIS) tool to visually show the variation of unit prices over the map using a spatial interpolation technique. The interpolation map can be used to estimate the unit price of the item at any location across Iowa. This noble method allows the estimator to effectively and fully utilize the historical bid data in a very time efficient manner and determine more accurate cost estimation.

• Korean Journal of Construction Engineering and Management
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• v.9 no.1
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• pp.87-95
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• 2008

The government wants to apply the construction cost estimating method based on historical data published in the first six months of 2004. Construction companies, however, require the proposed cost estimation model, to be improved which makes it difficult to predict a reasonable construction costs. This paper presents an improved historical data selection model after analyzing the problem of previous method throughout comparing contracted unit prices of reinforced concrete works selected by the previous model to market prices. The model which can select more feasible data would assist participates such as general contractors and sub-contractors to earn a proper profits.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.488-493
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• 2013

Accurate initial cost estimates are essential to effective management of construction projects where many decisions are made in the course of project management by referencing the estimates. In practice, the initial estimates are frequently derived from historical actual cost data, for which standard distribution-based techniques are widely applied in the construction industry to account for risk associated with the estimates. This approach assumes the same probability distribution of estimate errors for any selected estimates. This assumption, however, is not always satisfied. In order to account for the probabilistic nature of estimate errors, an alternative method for measuring the risk associated with a selected initial estimate is developed by applying the Bayesian probability approach. An application example include demonstrates how the method is implemented. A hypothesis test is conducted to reveal the robustness of the Bayesian probability model. The method is envisioned to effectively complement cost estimating methods that are currently in use by providing benefits as follows: (1) it effectively accounts for the probabilistic nature of errors in estimates; (2) it is easy to implement by using historical estimates and actual costs that are readily available in most construction companies; and (3) it minimizes subjective judgment by using quantitative data only.

• 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.

• Journal of the Korea Institute of Building Construction
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• v.13 no.1
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• pp.66-74
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• 2013

In the early stages of a construction project, the most important thing is to predict construction costs in a rational way. For this reason, many studies have been performed on the estimation of construction costs for apartment housing and office buildings at early stage using artificial intelligence, statistics, and the like. In this study, cost data held by a provincial Office of Education on elementary schools constructed from 2004 to 2007 were used to compare the multiple regression model with an artificial neural network model. A total of 96 historical data were classified into 76 historical data for constructing models and 20 historical data for comparing the constructed regression model with the artificial neural network model. The results of an analysis of predicted construction costs were that the error rate of the artificial neural network model is lower than that of the multiple regression model.