• 교육녹색환경연구
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• 제11권3호
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• pp.46-54
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So various of techniques are developed to predict the construction cost accurately and expeditely. Among the techniques, Support Vector Machine(SVM) has an excellent ability for generalization performance. Therefore, the purpose of this study is to construct the prediction model for construction cost of educational building project using support vector machine technique. And to verify the accuracy of prediction model for construction cost. The performance data used in this study are 217 school building project cost which have been completed from 2004 to 2007 in Gyeonggi-Do, Korea. The result shows that average error rate was 7.48% for SVM prediction model. So using SVM model on predicting construction cost of educational building project will be a considerably effective way at the early project stage.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2002년도 학술대회지
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• pp.64-71
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• 2002

This research developed construction cost forecasting model using Building Construction Cost Index, time series analysis and Artificial Neural Networks. By this model, we could calculate the forecasted values of construction cost precisely and efficiently. And we also could find out that the standard deviation of forecasted values is 0.375 and it is a very exact result, so the standard deviation is just 0.33 percent of 112.28, the average of Building Construction Cost Index. And it show more exact forecasting result in comparison with Time Series Analysis.

• 한국건축시공학회지
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• 제15권3호
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• pp.307-316
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• 2015

공공건축물공사의 실적공사비제도에서 공사규모에 대한 건축공사비 차이가 크게 나타나므로, 산정방식에서 많은 문제점으로 제기된다. 이에 공사규모에 따라서 선별적, 차등적으로 적용할 예측모형의 개발이 필요하다. 이에 본 연구는 조달청에서 2011~2012년에 발주한 이미 준공된 42개 현장의 실적자료에서 공사규모 $5,000m^2{\sim}20,000m^2$으로 선정하여, 회귀분석을 통해서 추정된 모형을 도출하였다. 이를 근거로 신규공사에 적용하여 검증함으로써 합리적인 건축공사비 예측모형을 제시하였다.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.685-691
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• 2009

Recently, public office building projects are being recognized by many construction engineers and researchers, as the critical projects in the construction industry. The project budgets have sometimes exceeded due to the lack of core knowledge, experiences, skills and experts concerned in cost planning and estimating in the pre-construction stage. It has been highlighted that planning and estimating effectively the cost of public office building projects as critical in the design stage. Within this context, some cost data books and systems, such as RSMeans cost data systems and Spon's price book, have been systematically developed and used by many construction cost managers and organizations in order to effectively estimate and use their project budgets. As a result of this research, a cost estimating model for finishes has been developed, considering the cost data used in public office building projects.

• 한국건축시공학회지
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• 제8권6호
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• pp.91-98
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• 2008

It is time-consuming and difficulty to solve the time-cost trade-off problems, as there are trade-offs between time and cost to complete the activities in construction projects and this problems do not have unique solutions. Typically, heuristic methods, mathematical models and GA models has been used to solve this problems. As heuristic methods and mathematical models are have weakness in solving the time-cost trade-off problems, GA based model has been studied widely in recent. This paper suggests the time-cost trade-off optimization algorithm using particle swarm optimization. The traditional particle swarm optimization model is modified to generate optimal tradeoffs among construction time and cost efficiently. An application example is analyzed to illustrate the use of the suggested algorithm and demonstrate its capabilities in generating optimal tradeoffs among construction time and cost. Future applications of the model are suggested in the conclusion.

• Geomechanics and Engineering
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• 제28권1호
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• pp.65-75
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• 2022

Time and cost of construction are key factors in decision-making during a tunnel project's planning and design phase. Estimations of time and cost of tunnel construction projects are subject to significant uncertainties caused by uncertain geotechnical and geological conditions. The Gaussian Process Regression (GPR) technique for predicting ground condition and construction time and cost of mountain tunnel projects is used in this work. The GPR model is trained with data from past mountain tunnel projects. The model is applied to a case study in which the predicted time and cost of tunnel construction using the GPR model are compared with the actual construction time and cost for model validation and reducing the uncertainty for the future projects. In addition, the results obtained from the GPR have been compared with to other models of artificial neural network (ANN) and support vector regression (SVR) that the GPR model provides more accurate results.

• 한국IT서비스학회지
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• 제16권3호
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• pp.47-65
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• 2017

The reference guide for the cost of establishing the digital documents has been used as a basis for establishing the budget for the construction of the knowledge information resource. However, due to the abolition of the nominal unit price notification in terms of IT projects, it is necessary to conduct research to convert the standard of the current labor force grade standard to the national incompetency standard (NCS). In this study, we investigate and revise the system and contents of the current knowledge information cost estimation model. In specific, i) we conducted gap analysis of cost estimation model and existing NCS model. As the contents conforming for the construction of the knowledge information resource were not adoptable, we define the description of the construction of the knowledge information resource and to identify the core elements of NCS prior to the improvement of the cost model. ⅱ) then we proposed improve the cost model considering integration with newly proposed NCS model for knowledge information construction job. In order to ensure the validity of the application of NCS development and cost estimation model, the experts reviewed relevant contents and made plans for improvement by using experts from supply and demand groups of various fields of national knowledge informatization projects.

• 한국건축시공학회지
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• 제5권1호
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• pp.97-103
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• 2005

Building costs means capital costs which include cost of land, costs of acquiring and preparing the site, construction costs, professional fees, furnishings, cost of financing the project. and cost of management required to run and maintenance the building for use. There are several phases that determine the building costs : design phase, construction phase, and operation & maintenance phase. So, the cost of work could be set against the examining the full range of complexities that a building program might contain. To solve this problem, it needs to compute building cost systematically. This is still in the development stage, awaiting the organization of rational cost data base. The method of cost saving by cost control could be constituted by detailed knowledge of building costs for all possible combinations of components and subsystems that can be assembled into integration model of cost factor on each phase of project development. The model of cost saving in each building phase is available for procedures of cost control of building systems.

• 한국강구조학회 논문집
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• 제22권1호
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• pp.55-65
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• 2010

공공 건설공사에서 공사 단계별 합리적인 공사비를 산정하는 것은 국가 예산의 효율적 확보 및 집행 등에 있어 매우 중요한 요소이다. 일반적으로 공사예정가격은 대상 구조물의 설계 종료시점에서 산출된다. 그러므로 대상 구조물의 구체적인 상세 단면이 주어지지 않은 기획단계 및 설계초기단계에서 교량 구조물에 대한 단순 정보만을 가지고 개략공사비를 추정하는 것은 상당히 어려운 문제일 것으로 생각된다. 본 연구에서는 선행연구에서의 61개의 강박스거더교 공사비 분석에 의해 효율적이고 적절한 기획단계 및 설계초기단계의의 개략공사비 산정 모델의 제시 및 타당성을 검증하고자 한다. 연구결과, 본 연구에서 제시된 공사비 산정 모델에 의해 얻어진 추정공사비는 기존에 사용된 방법과 비교할 때 매우 높은 신뢰도를 갖는 것을 알 수 있었다.

• 한국건축시공학회지
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• 제11권1호
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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.