• The Journal of Sustainable Design and Educational Environment Research
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• v.10 no.1
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• pp.10-25
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• 2011

Area Offices of Education in Korea assign and execute government budget based on the evaluation of school buildings' safety rating and degree of their deterioration. However, it is never easy to estimate the most appropriate investment amount for old buildings under consideration of their service lives and residual values together. A model of estimating optimum investment cost for obsolete school building is developed taking its life cycle cost into account. The model is also applied to six old buildings in five different schools and found that some of the facilities hardly needed further investment and were better to be rebuilt. The study results will be a great beneficial for officers to make right decision on maintaining obsolete school buildings and to maximize tax payers' money.

• Korean System Dynamics Review
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• v.8 no.2
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• pp.253-273
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• 2007

To measure the effect of school zone on housing cost, Linear Regression Model is widely used, and school zone is known as a key determinant of housing cost in Korea. However, when the Hierarchical Linear Model (HLM) is applied with the same data, school effect on housing cost becomes statistically non-significant. It is because HLM effectively separates the effect of individual housing's attributes from the group effect. In sum, the housing cost of Kangnam, where good public schools are located, is apparently is higher than that of Kangbuk. However, the school effect on housing cost (Level 2) becomes non-significant when individual housing's attributes (Level 1) are controlled with HLM.

• 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.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.445-448
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• 2008

This study attempts to estimate approximate cost on construction of PSC BEAM Bridge using Case-Based Reasoning and suggests approximate estimation model at the planning and design stage. This paper suggests phased influence factors on construction cost and approximate estimation model for integrated project cost management.

• Magazine of the Korean Society of Agricultural Engineers
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• v.16 no.1
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• pp.3302-3318
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• 1974

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.377-377
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• 2000

Current software cost estimation models, such as the 1951 COCOMO, its 1987 Ada COCOMO update, is composed of nonlinear models, such as product attributes, computer attributes, personnel attributes, project attributes, effort-multiplier cost drivers, and have been experiencing increasing difficulties in estimating the costs of software developed to new lift cycle processes and capabilities. The COCOMO II is developed fur new forms against the current software cost estimation models. This paper provides a case-based analysis result of the cost driver in the software cost models, such as COCOMO and COCOMO 2.0 by fuzzy and neural network.

• 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.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.197-201
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• 2008

Conceptual cost estimates for domestic highway projects have generally been conducted using governmental unit-price references. Inaccuracies in governmental unit-price data has repeatedly addressed in the Korean construction industry which often lead to poor decision making and cost management practices. Thus, needs for developing a better way of conceptual cost estimating has been widely recognized. This research is considered as the first step in developing such model using real-world cost data based on actual construction activities. The data analyzed in this paper includes 41 P.S.C (Prestressed Concrete) Box bridges which broke into 4 categories based on construction methods such as I.L.M(Incremental Launching Method), M.S.S(Movable Scaffolding System), F.S.M(Full Staging Method), and F.C.M(Free Cantilever Method). Actual design documents; including actual cost estimating documents, drawings and specifications were carefully reviewed to effectively break down cost structures for PSC girder bridges. Among more than 40 cost categories for each P.S.C girder bridge type, 7 of them were identified which accounted for more than 95% of total construction cost (ILM: 99.47%, MSS: 99.22%, FSM: 98.18%, and FCM: 98.12%). In order to validate the clustering of cost categories, the variation of each cost category has been investigated which resulted in between -1.16 % and 0.59%.

• Journal of the Korea Institute of Building Construction
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• v.11 no.1
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• pp.91-99
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• 2011

Bayesian network is a form of probabilistic graphical model. It incorporates human reasoning to deal with sparse data availability and to determine the probabilities of uncertain cases. In this research, bayesian network is adopted to model the problem of construction project cost. General information, time, cost, and material, the four main factors dominating the characteristic of construction costs, are incorporated into the model. This research presents verify a model that were conducted to illustrate the functionality and application of a decision support system for predicting the costs. The Markov Chain Monte Carlo (MCMC) method is applied to estimate parameter distributions. Furthermore, it is shown that not all the parameters are normally distributed. In addition, cost estimates based on the Gibbs output is performed. It can enhance the decision the decision-making process.

• Korean Journal of Construction Engineering and Management
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• v.10 no.3
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• pp.139-147
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• 2009

Accurate construction cost estimation in the initial stage of building project plays a key role for project success and for mitigation of disputes. Total construction cost(TCC) estimation of apartment projects in Vietnam has become more important because those projects increasingly rise in quantity with the urbanization and population growth. This paper presents the application of artificial neural networks(ANNs) in estimating TCC of apartment projects. Ninety-one questionnaires were collected to identify input variables. Fourteen data sets of completed apartment projects were obtained and processed for training and generalizing the neural network(NN). MATLAB software was used to train the NN. A program was constructed using Visual C++ in order to apply the neural network to realistic projects. The results suggest that this model is reasonable in predicting TCCs for apartment projects and reinforce the reliability of using neural networks to cost models. Although the proposed model is not validated in a rigorous way, the ANN-based model may be useful for both practitioners and researchers. It facilitates systematic predictions in early phases of construction projects. Practitioners are more proactive in estimating construction costs and making consistent decisions in initial phases of apartment projects. Researchers should benefit from exploring insights into its implementation in the real world. The findings are useful not only to researchers and practitioners in the Vietnam Construction Industry(VCI) but also to participants in other developing countries in South East Asia. Since Korea has emerged as the first largest foreign investor in Vietnam, the results of this study may be also useful to participants in Korea.