• Journal of the Korea Institute of Building Construction
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• v.15 no.3
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• pp.307-316
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• 2015

As there are a lot of differences in the public building construction cost depending on the construction scale of actual construction cost system, a lot of problems occur in the estimation of the cost. So, the development of a predictive model depending on the construction scale shall be used in a way that it is applied to the case selectively and differently. This study drew a cost estimating model through a regression analysis. For this, 42 construction sites which were ordered during 2011 to 2012 by Public Procurement Service data were selected as a historical data. Based on the application of the model to new construction and the verification of its effect, the reasonable model for estimating the construction cost has been suggested.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.665-675
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• 2008

The objective of this paper is to provide an approximate cost estimating model for tunnel that can be utilized both in quick construction cost estimating for design alternatives, and in evaluating efficiently the cost effects according to the environmental changes during design and construction stage. To meet this requirement, this study analyzes critical cost factors influencing tunnel construction costs. The cost factors include 7 elements such as rock drilling method, advancing method, type of detonator, loader capacity, unit weight and soil volume change factor, length of tunnel. This paper investigates the cost variance according to the change of the cost factors. The result is expected to be used in formulating approximate tunnel cost estimating model.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.182-186
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• 2011

Early cost estimates are important to decision-making for a construction project. Moreover, the possibility of reducing the project cost is getting less as the project is progressed. Case-based reasoning (CBR), which can be viewed as an effective method for early cost estimating, is widely utilized recently. Early cost estimates using CBR have advantages over the traditional ones as they produce reasonable outputs and self-studying is possible by simply adding new cases. Case-based reasoning is composed of a cycle of retrieve, reuse, revise, and retain process. However, in the majority of research cases, they are focused on how to retrieve the similar cases, instead of revising the cases which is expected to increase accuracy results of cost estimation. This research suggests a method of revising retrieved similar cases in a GA-CBR cost model which is widely studied and utilized for early cost estimating recently. To validate the proposed method, case study is conducted based on Korean public apartment projects.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.176-181
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• 2011

A role of cost modeler is that of facilitating design process by the systematic application of cost factors so as to maintain sensible and economic relationships between cost, quantity, utility and appearance. These relationships help to achieve the client's requirements within an agreed budget. The purpose of this study is to develop a parametric cost estimating model for the early design stage by using the multi-dimensional system of OLAP (On-line Analytical Processing) based on the case of quantity data related to architectural design features. The parametric cost estimating models have been adopted to support decision making in the early design stage. These models typically use a similar instance or a pattern of historical case. In order to effectively use this type of data model, it is required to set data classification and prediction methods. One of the methods is to find the similar class in line with attribute selection measure in the multi-dimensional data model. Therefore, this research is to analyze the relevance attribute influenced by architectural design features with the subject of case-based quantity data used for the parametric cost estimating model. The relevance attributes can be analyzed by Analytical Characterization. It helps determine what attributes to be included in the OLAP multi-dimension.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.256-261
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• 2011

Labor productivity is a significant factor related to control time, cost, and quality. Many researchers have developed models to define method of measuring the relationship between productivity and various constraints such as the size of working area, maximum working hours, and the crew composition. Most of the previous research has focused on estimating productivity; however, this research concentrates on estimating labor productivity and developing time and cost data for repetitive concrete pouring activity. In Korea, "Standard Estimating" only contains the average productivity data of the construction industry, and it is difficult to predict the time and cost of any particular project; hence, there are some errors in estimating duration and cost for individual activity and project. To address these issues, this research collects data, measures productivity, and develops time and cost data using labor productivity based on field conditions from the collected data. A probabilistic approach is also proposed to develop data. A case study is performed to validate this process using actual data collected from construction sites and it is possible that the result will be used as the EVMS baseline of cost management and schedule management.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1242-1247
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• 2009

The sudden changes in the construction market and the progressively intensifying price wars have amplified the importance of the construction cost estimation in the initial and planning phases of construction projects. However the methodologies and process of estimating construction cost in the planning and design phase are not standardized in the domestic market, in contrast to the markets of more developed countries. Therefore this paper proposes a new approximate estimation model to be used from the initial stages of construction projects. This methodology that extracts, modifies and synthesizes comparable elements of previous cases. This will introduce the foundation for the implementation of systems with improved usability and applicability.

• 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.

• Journal of Urban Science
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• v.6 no.2
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• pp.35-47
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• 2017

Tax evasion is increasing, but efficiency of tax administration is evaluated as improving. This is because the taxation cost, which is a measure to judge the efficiency of the tax administration, does not consider the tax evasion effect at all. This method of estimating the cost of taxation is a dispute that neglects the role of taxation authorities in tax evasion. The existing study focuses on the development of a tax evasion model focused on maximizing the utility through the tax evasion of the taxpayer as the tax evasion approaches the individual 's deviant problem. However, this has the aspect of making the role of the tax authorities in tax evasion negative. This study empirically derived the optimal size of tax administration in Korea by using tax collection cost and tax cooperation cost. Also, it is meaningful to consider the role of the taxation authorities in tax evasion and to derive the optimal taxation cost model by estimating the decrease in tax evasion due to the taxation expenditure of the tax authorities. In order to derive the optimal size of tax administration in Korea, taxation cost and tax cooperation cost are derived by classifying tax officials. The optimal taxation cost model was derived by estimating the taxation expenditure related to tax evasion. This study is meaningful to make it possible to emphasize the role of tax authorities in studying future tax evasion by studying the effect of taxation expenditure on tax evasion.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6D
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• pp.829-837
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• 2011

Construction cost estimation in planning phase which calculates the cost for performing construction tasks is used for various ways. Meanwhile, in the case of road construction, the existing cost estimating method in early phase based on numerical mean value of the past is not accurate to be used. This paper propose neural network model for estimating road construction cost in planning phase to solve the limit of current cost estimating method. The model was designed using past road construction bidding records, and variables of model were optimized through trial and error. The estimation result of the model was compared with regression analysis and government's standard and it was verified that the model is better in accuracy. It is expected that the proposed model will be used for road cost estimation in planning phase.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.5
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• pp.565-576
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• 2012

Cost estimates are necessary for government acquisition program to support decisions about funding, to develop annual budget requests and to validate resource requirements at key decision points. Many researches have been done about cost estimating technique recently. Parametric cost estimating models based on CERs(Cost Estimating Relationships) have been mainly used using regression method with historical data. However, there are many restrictions in developing Korean version CERs because the number of data points are too small. Specially, data collection and data management system are unstable in Korean defense environment, when developing CERs. In this research, we analyzed the historical data, and found some cost drivers in guided weapon system area. We developed the Acquisition Unit Cost CER using the regression to remove multicollinearity in the historical data. So we could overcome the restriction of the insufficient sample number. This research as a first attempt is meaningful in terms of obtaining our own Acquisition Unit Cost CER using historical cost and physical characteristic in Korean development environment.