• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.607-612
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• 2007

Early cost estimation promote efficient budget plan by comparing alternatives and presenting cost information However it is hard to predict accurate cost because of vague cost standard and lack of available information in the early stage. The precious cost model has limitations in the accuracy because they are simple linear model which uses the unit cost per kilometer. This study presents the framework of early cost estimation for road construction projects to overcome the limitation of previous cost model. This study analyzed domestic and foreign cost model and cost data of previous road construction project to present method of cost model framework.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.207-211
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• 2008

The importance of cost management in early stage has been increasing due to market change and competition severence in construction industry. Because the adjustable budget is only 20% after finishing design stage, the critical decision is made in the early stage. However, in the early stage, the design information is not enough to make crucial decision. Therefore, this research suggests the predicting method on the purpose of accurate cost estimation. The parametric estimation is appropriate for the early stage, especially it has the strength of rapidity in cost estimation. This research analyzes 84 actual data of public apartment on the scale of $11{\sim}15$ stories, and then performs the correlation analysis between cost and influence factors. After eliminating the parameters which causes the problem of multicollinearity, this research derived the formula through the multi-regression analysis.

• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.87-100
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• 2009

The quality of early cost estimates is critical to the feasibility analysis and budget allocation decisions for public capital projects. Various researches have been attempted to develop cost prediction models in the early stage of a construction project. However, existing studies are limited on its applicability to actual projects because they focus primarily on a specific phase as well as utilize restricted information while the amount of information collectable differs from one another along with the project stages. This research aims to develop two-staged cost estimation model for the schematic planning and preliminary design process of a construction projects, considering the available information of each phase. In the schematic planning stage where outlined information of a project is only available, the Case-Based Reasoning model is used for easy and rapid elicitation of a project cost based on the extensive database of more than 90 actual highway construction projects. Then, the representing quantity-based model is proposed for the preliminary design stage where more information on the quantities and unit costs are collectable based on the alternative routes and cross-sections of a highway project. Real case studies are used to demonstrate and validate the benefits of the proposed approach. Through the two-stage cost estimation system, users are able to hold a timely prospect to presume the final cost within the budge such that feasibility study as well as budget allocation decisions are made on effectively and competitively.

• Journal of the Korean GEO-environmental Society
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• v.8 no.3
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• pp.11-18
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• 2007

This paper studied the economical efficiency of ground source heat pump system under various conditions in apartments which have important effects on the housing market. And this study analysed the initial cost increase, saved managing cost and recovery time of initial cost. Analysis result showed as time of heating-cooling and water heating increases, the amount of saved managing cost increased much than the initial construction cost, so recovery time shortened. And as the net area of apartment increases, the recovery time increased. The study of the relation between the installation type and recovery time of initial construction cost showed when heat-cooling system adapted ground source heat and water heating system adapted waste heat, the initial construction cost was recovered most quickly. When Ground Source Heating system was used for the heating-cooling and water heating system, ground source heating system was used for the heating-cooling and waste heat used for water heating, and ground source heating system was used for the heating-cooling and LNG used for water heating, the construction cost increased 72,000, 66,900 and 62,300 won each per $m^2$ compared to the current system (package air-conditioner, heating and water heating using LNG).

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.218-221
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• 2010

발주자는 건설 프로젝트의 주요 의사결정을 초기단계에 내리게 되므로 정확한 예산을 예측하는 것이 중요하다. 그러나 건설 프로젝트의 대형화, 복잡화 그리고 건설산업계의 새로운 패러다임인 BIM(Building Information Modeling)의 등장에도 불구하고, 초기단계에서 정확한 공사비를 예측할 수 있는 표준화된 산출방법이 없어 아직까지도 대부분의 설계사무소에서는 실시설계단계에서 확정된 설계안에 따른 견적을 이용하며 초과된 비용에 대해 통합품질을 저해하는 행위를 통해 조정하고 있다. 초기단계에서 보다 정확한 공사비를 예측하므로써 발주자는 의사결정을 함에 있어 올바른 판단을 내릴 수 있으며, 이 단계에서 발생한 정보를 활용하여 이후 단계에도 유용하게 쓰일 수 있을 것이다. 본 연구에서는 발주자 의사결정 지원시스템 상에서 BIM기반으로 개략공사비를 산출할 수 있는 프로세스를 제안한다.

• Korean Journal of Construction Engineering and Management
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• v.21 no.3
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• pp.48-56
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• 2020

Prediction of construction cost in the planning stage that provides basic information for feasibility study, budgeting, and planning is an important factor for successful project execution. In this study, a prediction model was developed for the purpose of improving the accuracy of estimating the construction cost of Hanok in the planning stage. The cost of this model is estimated by two methods. First, the cost of wood work, which accounts for the largest portion of the total construction cost, is estimated by calculating the approximate quantity under various conditions (structure type, roof type, plane type, etc.). Second, the cost of the rest work sections except the wood work is estimated by using the unit cost model. The predictive model was verified by two case projects, and the error rate of total construction cost was -4%(case 1) and -6%(case 2). These results showed an error rate in the range that can be applied to practice in the planning stage.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.103-111
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• 2023

Accurate cost estimation in the early stages of a construction project is critical to the successful execution of the project. In this study, an ANFIS model was presented to predict construction costs in the early stages of a construction project. To increase the usability of the model, open construction cost data was used, and a model using limited information in the early stage of the project was presented. We analyzed existing studies related to ANFIS to identify recent trends, and after reviewing the basic structure of ANFIS, presented an ANFIS model for predicting conceptual construction costs. The variation in prediction performance depending on the type and number of membership functions of the ANFIS model was analyzed, the model with the best performance was presented, and the prediction accuracy of representative machine learning models was compared and analyzed. Through comparing the ANFIS model with other machine learning models, it was found to show equal or better performance, and it is concluded that it can be applied to predicting construction costs in the early stage of a project.

• Korean Journal of Construction Engineering and Management
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• v.9 no.3
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• pp.65-74
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• 2008

Public institutions recognize the importance of cost management from the planning stage but they do not have an organized construction cost estimation and management system. Thus, at the stage of planning a new public construction project and estimating the cost, those in charge of budgeting estimate construction cost based on existing data and experiences, compare construction cost estimated after the basic design stage and the execution design stage with budgets, and then decide whether to continue the project or change the design according to the budgets. Therefore, we would develop the cost prediction model through regression analysis that can predict construction cost in Schematic Design Phase of the Public Multi-Family housing. Accordingly, if public institutions have a construction cost prediction model and management system that can estimate the optimum construction cost, they can make and execute budgets in a more efficient way than they do at present.

• Korean Journal of Construction Engineering and Management
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• v.9 no.3
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• pp.108-117
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• 2008

The appearance of the large distribution facility of large enterprise putting first reaches get to the various effect until change of leisure life and life pattern of the consumers from the distribution industry of the interior of a country. Competition of the distribution facility upgrade of the distribution facility and it shows the aspect which becomes the semi-department store, and construction cost is appearing different in proportion to each form or scale. Therefore, purpose of this study was to facilitate amicable construction progress between the owner and the builder through estimating the proper construction cost. This study investigated and analyzed the actual cost of 15 domestic distribution facilities and these datums were used to estimate the proper construction cost. This cost shows that from new project accomplishment through analysis of prediction construction cost for feasibility study from initial plan and design step and can be utilizable elementary data bH decision method to whether or not to propriety of distribution facilities business.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.22-34
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• 2023

Predicting accurately the construction cost budget in the early stages of construction projects is crucial to support the client's decision-making and achieve the objectives of the construction project. This holds true for public construction projects as well. However, the current methods for predicting construction cost budgets in the early stages of public construction projects are not sophisticated enough in terms of accuracy and reliability, indicating a need for improvement. The objective of this study is to develop a construction cost budget prediction model that can be utilized in the early stages of public building projects using an artificial neural network (ANN). In this study, an artificial neural network model was developed using the SPSS Statistics program and the data provided by the Public Procurement Service. The level of construction cost budget prediction was analyzed, and the accuracy of the model was validated through additional testing. The validation results demonstrated that the developed artificial neural network model exhibited an error range for estimates that can be utilized in the early stages of projects, indicating the potential to predict construction cost budgets more accurately by incorporating various project conditions.