• The Journal of Society for e-Business Studies
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• v.15 no.1
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• pp.139-155
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• 2010

The purpose of this paper is to develop a framework of software cost estimation guidelines and to derive a mid-long term direction for development of the software cost estimation guidelines. In this paper, all the steps in the software life cycle are researched in the view of cost estimation, and current software cost estimation guidelines and models have been reviewed and analysed first. Second, a plan to separate unit cost per function point from standard procedure in current software cost estimation guidelines is presented to strengthen maket self-regulating function as a mid-long term developmental direction for software cost estimation guidelines. Third, construction of cost repository, making standard procedure for software cost estimation guidelines, development of various kinds of software cost estimation models, and a system for experts on software cost estimation are presented as the prerequisites for the future model framework of software cost estimation guidelines. Finally a roadmap for establishing the future model is proposed.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.11a
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• pp.98-99
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• 2014

Construction equipment cost is critical to estimate the total construction costs, particularly in large and complicated projects. Despite its importance, the construction equipment cost may not reflect the current market value since the equipment database is being updated every 6 years at most. To keep construction equipment cost up to date, it is highly recommended to use the standard market price that is reported each year by the Ministry of Security and Public Administration (MOSPA). However, there is still a gap to adopt the standard market price system for the construction equipment cost computing system. Therefore, this paper suggests an effective way to develop a construction equipment cost estimation system.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.423-426
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• 2002

The foundation for developing a cost estimation system based on historical data has been being prepared in Korea. Historical data is a priori of developing a cost estimation model. Cost Index, one of the historical data, is used to estimate construction cost and to adjust the amount of contract money in the foreign country, whereas it is not used in domestic except for the road construction project in Korea. Construction cost indices can be used by an estimator in tender analysis, pricing, price adjustment, cost planning, and forecasting. In this regards, this paper identified the problems in developing Cost Index evaluation process by comparing the standard of framing Cost Index used in British to the one used in Korea. Then, the scheme for improving a Cost Index required for Site Developing Construction was proposed. Twenty-two cases of engineering estimate data were used to compare the domestic standard to the foreign one in deriving a Cost Index.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.922-925
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• 2008

Many highway projects are under way in Korea. However, owners frequently find that the project cost exceeds the budget and they are unable to identify the underlining reasons. The main purpose of this research is to develop cost models for transportation projects in Korea using the multiple linear regression (MLR). The data consist of 27 completed transportation projects, built from 1991 to 2001, The technique of multiple regression analysis is used to develop the parametric cost estimating model for total budget cost per highway square meter (TBC/$m^2$). Findings of the study indicated that MLR car be applied to highway projects in Korea. There are twf) major contributions of this research. (1) the identification of transportation parameters as a significant cost driver for transportation costs and (2) the successful development of the parametric cost estimating models for transportation projects in Korea.

• Korean Journal of Construction Engineering and Management
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• v.9 no.1
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• pp.155-166
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• 2008

The Equipment Expenses section of the Poom-Saem that is used for Construction Cost Estimation of public sectors in Korea has been rarely revised, and so does not reflect changes of Construction Equipment technology and construction methods, since it was established based on data from Japan and the U.S in the early 1960s. In order to make reasonable estimation of equipment expenses for a bulldozer and a loader, the domestic and overseas documentary records were investigated. Further, 16 construction sites were visited, and 20 Construction Equipment leasing services were surveyed. The optimum level of each item which consists of equipment costs was revealed based on the result of the analysis on the figures of equipment costs calculation from the Poom-Saem and the collected data. The research will be the foundation for applying the rapid development of Construction Equipment and technology to the appropriate cost estimations and the ground work of related studies.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1456-1461
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• 2004

Companies of Build-to-Order(BTO) strive to achieve customer responsiveness and cost efficiency simultaneously. The success of BTO depends upon the high volume production based on product plat form and delayed differentiation principle on the one hand, and upon the rapid estimation of delivery and cost for the customer orders on the other hand. Expeditious processing of a specific order requires the rearrangement of production resources and the schedule, which results in increased cost. This paper describes a cost estimation method using activity-based costing depending on the schedule change caused by urgent customer orders.

• Korean Journal of Construction Engineering and Management
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• v.17 no.1
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• pp.3-9
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• 2016

This study proposes a approximate cost estimation system based on Excel with VBA using weighted CBR(Case Based Reasoning). One characteristic of this system is that it generates the sheet automatically as many as the number of similar case and new estimation when it performs a case learning and a new estimate and cell formula is automatically entered into each sheet. User can be free to compose a combination of attribute factors because they can select up to ten attribute factors. This paper presents an applicability of estimation model for estimating the soil remediation cost when it use a landfarming method. When compared to a estimation model by using average unit cost and optimum multiple regression, this model shows a better result. This study was aimed at landfarming method, but it is expected that a cost estimation model using CBR will be more likely to apply in soil remediation technologies which various remediation technologies and pollutant species exist.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.373-379
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• 2009

Cost estimation in the early phase enables government to plan public budgeting more efficiently by providing information about construction cost. However, cost estimation in the early phase is difficult to predict because only a little information can be utilized. The cost estimation method now being used by the government is calculated by length of the road multiplied by unit cost per length and shows high error rate because it cannot reflect the unique characteristics of each project. As the project is being proceeded, level of available information also changed. So, reflecting available information of a project is important. This paper divided early phase into two parts : planning phase and early design phase, and developed cost estimation model considering level of available information of each phase. Total 143 cases are utilized to find influencing variables and develop cost estimation model and model validation is done by adopting required accuracy level. This cost estimation model reflecting level of available information can be applied to public budgeting, feasibility test, and comparison between routes.

• Journal of Construction Engineering and Project Management
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• v.4 no.4
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• pp.9-16
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• 2014

The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.

• Korean Journal of Construction Engineering and Management
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• v.13 no.1
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• pp.44-52
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• 2012

In the cost management of public construction projects, it is an important issue to develop an adequate cost data for estimating the predetermined amount by various methods. For a long time, a standard of estimation in korea is used as a basis for estimating the predetermined amount of public construction. However, they did not have a reasonable cost data based on a labor and equipment productivity analysis. For this reason, it is difficult to make a reasonable and efficient estimation of the costs, and this situation presents an urgent need for more accurate cost data to use in an early phase. This study analyzed the productivity of form work by the CYCLONE model, and presented the model on the number of optimal labor through sensitivity analysis. This CYCLONE model can be useful in analyzing productivity on the various sizes of form. Also, the regression model to estimate the daily output can be used in predicting the amount of labor. Considering the work duration in the regression model is expected to make the daily output estimation much more accurate.