• 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.

• 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.2
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• pp.170-181
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• 2008

Cost of construction project have to be estimated based on drawing before execution. Cost estimate and check would be performed numerously for preparing general outline of requirements and determining the budget at conceptual planning stage, for obtaining decision on every matter related to design, specification, construction and cost at design stage, and for predicting bidding cost. Thus, importance of cost estimation cannot emphasize too much in construction. However, there are lack of standard estimation method, process, and cost analysis method, that square foot estimation method is as used as eyer, in Korea. Thus, This research present the direction for improving cost estimation and management in construction; It is demanded that establishing standard data base methodology, multi-level database model CUBE, and standard cost planning process, choosing cost estimation methodology according to objectives and cost planning process, and making more experts.

• Korean Journal of Construction Engineering and Management
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• v.7 no.4 s.32
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• pp.91-99
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• 2006

The purpose of this study is propose an Artificial Neural Network(ANN) model for the construction estimate of the public educational facility at conceptual stage. The current method for the preliminary cost estimate of the public educational facility uses a single-parameter which is based on basic criteria such as a gross floor area. However, its accuracy is low due to the nature of the method. When the difference between the conceptual estimate and detailed estimate is huge, the project has to be modified to meet the established budget. Thus, the ANN model is developed by using multi-parameters in order to estimate the project budget cost more accurately. The result of the research shows 6.82% of the testing error rates when the developed model was tested. The error rates and the error range of the developed model are smaller than those of the general preliminary estimating model at conceptual stage. Since the proposed ANN model was trained using the detailed estimate information of the past 5 years' school construction data, it is expected to forecast the school project cost accurately.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.15 no.2
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• pp.53-61
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• 2014

Previous studies reveal the need for a tool to cost estimation of building design in early design stages. This paper proposes an internal finishes cost model tool to address this need. The tool allows users to evaluate the functionality, economics and quality of finishes concurrently with high-rise building design. Lack of information in the early stages of the project enables a relatively accurate estimates of work to raise up. Measurements are automatically extracted from simple design information and profile driven estimates are revised in real-time. The data model uses a flexible unit rate system that can easily be extended to other estimate dimensions such as mix-use building surcharge rate estimation. The approach illustrated in this paper is applicable to BIM tool conceptual estimation that support for massing purposes other than the one chosen for this study.

• 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.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.585-588
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• 2007

This study attempts to analyze estimating process and the level of information performing literature review and questionnaire and identify phased influence factors on construction cost using phased information of project and suggests framework of approximate estimation model at the planning and design stage. This paper suggests phased influence factors on construction cost and framework of approximate estimation model for integrated project cost management.

• 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.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.