• International conference on construction engineering and project management
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• 2009.05a
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• pp.1254-1259
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• 2009

The purpose of this study is to analyze elemental maintenance costs for educational buildings. The adapted research method selected three school buildings in Seoul as BTL projects. On the basis of the selected case, the study suggested a model to establish a system for each parts and to estimate analyzed maintenance costs through that system. According to the analysis, the study proposed a partial maintenance costs standard and analyzed proper maintenance costs. The results of this study are as follows 1) The system is divided into 8 large-groups and 24 small-groups for the analysis elemental maintenance costs. 2) The average rations followed by analysis of partial maintenance costs of the three school buildings are as followings, the total maintenance costs are analyzed 3,992 million won and each part of average rations is exterior of building 10.9%, interior of building 41.58%, electricity & fire fighting facility 14.22%, water supply & healthy facility 11.39%, heating & water supply facility 12.93%, landscape 6.3%, civil engineering works 2.69%.

• Korean Journal of Construction Engineering and Management
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• v.11 no.1
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• pp.142-150
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• 2010

The purpose of this study is to analyze maintenance costs and estimation of elemental costs for educational buildings. The adapted research method selected four school buildings in seoul region as BTL projects. On the basis of the selected case, the study suggested a model to establish a system for each parts and estimate analyzed construction costs and maintenance costs according to that system. According to the analysis, the study proposed a partial construction costs analysis table and maintenance costs table and analyzed proper construction costs and maintenances costs. The results of this study are as follows 1) The average ratio followed by calculation of partial construction costs of the four school buildings are as followings. The total construction costs is analysed 6,666million won and each part of average rations is structure 47.15%, exterior of building 8.34%, interior of building 23.37%, electricity & fire fighting facility 5.74%, water supply & healthy facility 4.27%, heating & water supply facility 5.25%, landscape 1.36%, civil engineering works 4.51%. 2)The average ratio followed by analysis of partial maintenance costs of the four school buildings are as followings. The total maintenance costs is analysed 4,309 million won and each part of average ratio is exterior of building 11.02%, interior of building 41.81%, electricity & fire fighting facility 14.81%, water supply & healthy facility 11.22%, heating & water supply facility 12.76%, landscape 5.75%, civil engineering works 2.63%.

• Korean Journal of Construction Engineering and Management
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• v.8 no.2
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• pp.173-181
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• 2007

The purpose of this study is to estimate elemental costs of an apartment building. The adapted research method includes a case study from $^{\circ}{\AE}H^{\circ}{\phi}$ construction company. The results of this study are as follows: 1) An elemental cost format of an apartment building is proposed, 2) An elemental cost table based on a case study of $^{\circ}{\AE}H^{\circ}{\phi}$ construction company is suggested.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.383-386
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• 2001

The ability to make good cost overruns predictions is a very important aspect of in major construction project. The probabilistic cost models can provide more reliable than traditional cost models which have been used in korea to prepare Bill of Quantities, if the actual cost data are sufficient enough to analyze the trends of the variables. The paper considers non-deterministic methods in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The objectives of this research is to develop a method to forecast the probabilistic total construction cost and the elemental work cost

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.120-125
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• 2003

The estimated construction costs derived at the Planning stage of a project are utilized as important data determining the feasibility of overall project. Therefore the clients usually expect to have estimations of the construction cost as precise as possible and there has been extensive studies carried out by the cost specialists worldwide to satisfy this client's specific need whereas the local estimators still heavily rely on producing quantities and detail quotations only. This paper is to introduce a case study where elemental/functional area cost analysis is incorporated into a database system for the improved accuracy in cost estimations.

• Korean Journal of Construction Engineering and Management
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• v.2 no.2 s.6
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• pp.90-97
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• 2001

The paper considers non-deterministic methods of analysing the risk exposure in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The Monte Carlo method is popular method for incorporating uncertainty relative to parameter values in risk assessment modelling. Non-deterministic methods, they are here presented as possibly effective foundation on which to risk management in cost estimating. The objectives of this research is to develop a computerized algorithms to forecast the probabilistic total construction cost and the elemental work cost at the planning stage.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.192-200
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• 2003

The object of this research is to develop a computerized algorithm of cost estimation method to forecast the total construction cost in the bidding stage by the historical and elemental work cost data. Traditional cost models to prepare Bill of Quantities in the korea construction industry since 1970 are not helpful to forecast the project total cost in the bidding stage because the BOQ is always constant data according to the design factors of a particular project. On the contrary, statistical models can provide cost quicker and more reliable than traditional ones if the collected cost data are sufficient enough to analyze the trends of the variables. The estimation system considers non-deterministic methods which referred to as the 'Monte Carlo simulation. The method interprets cost data to generate a probabilistic distribution for total costs from the deficient elemental experience cost distribution.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.243-244
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• 2015

This paper presents a Stochastic Long-Term Maintenance Costs Estimating Method for the Apartment Housing (SLCE). A simulation approach is used for generating the stochastic long-term maintenance cost, and it is based on the defined variability in repair cycle of the individual maintenance elemental within the process. SLCE provides the probability distribution of the budget required to maintain the apartment housing. A case study is presented to demonstrate and to validate the system.

• Korean Journal of Construction Engineering and Management
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• v.1 no.1 s.1
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• pp.63-71
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• 2000

The paper considers two non-deterministic methods of analysing the risk exposure in a cost estimate The fist method(referred to as the 'conventional statistical' method) analyses cost data directly, to describe a probability distribution for total cost. The second method(referred to as the 'Monte Carlo simulation' method) interprets cost data directly, to generate a probability distribution for total costs from the descriptions of elemental cost distribution. The common practice of allowing for risk through an all-embracing contingency sum or percentage addition is challenged. Rather than excluding conventional, non-deterministic methods, they are here presented as possibly the only of effective foundation on which to risk management in cost estimating.