• Korean Journal of Construction Engineering and Management
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• v.13 no.2
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• pp.91-101
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• 2012

In planning phase of Public Multi-Housing Projects, it is required to develop the methodology and criteria for fair cost prediction with influencing power from planning phase to occupancy phase. Many studies still have focused on the prediction of cost by multiple regression. However, there is no logical explanation about the influence of nonmetric variables for the prediction of cost in planning phase. Accordingly, this research pursues a cost prediction model including nonmetric variables for use in planning phase. There are 3 steps of this research : 1) Finding the factors influencing construction cost and assigning variables for a multiple regression. 2) Conducting a dummy regression analysis with nonmetric variables and model validation by comparing actual cost data. 3) Developing the ratio of RC structure cost to wall structure cost by using cost predection model. The results could establish cost prediction process including the influence of nonmetric variables and the ratio of RC structure cost to wall structure cost.

• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.316-318
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• 2010

도로건설에 있어 계획단계에서 초기공사비의 정확한 예측을 위해 아직 연구가 미흡한 터널공사비 예측 모델을 시스템 다이내믹스를 이용하여 개발함으로써 합리적인 터널공사비 예측을 위한 토대를 마련하여 올바른 의사결정을 돕고 예산 낭비를 방지하기 위함을 목적으로 하였다.

• 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.

• Journal of the Society of Disaster Information
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• v.9 no.1
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• pp.11-21
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• 2013

This study proposed cost prediction equation model by considering duration, construction, size, actual cost with the subway construction started by the actual cost system which was introduced since 2004. Costs - scale exponent n(confidence range: 0.5 to 0.7) for cost prediction of subway construction was drawn total cost(0.713), net cost(0.77) in point of the 11 subway construction data. The cost prediction equation model of the subway construction which was presented in this study is able to effectively apply to business planning, preliminary investigation, feasibility study, basic design stage to estimate the approximate cost in the future.

• Journal of the Korea Institute of Building Construction
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• v.21 no.2
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• pp.105-112
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• 2021

While the scale of the domestic market for demolition work is steadily increasing, research on cost prediction for demolition work is insufficient. Thus, this study proposes a cost prediction model for demolition work that reflects various attributes influecing the fluctuation of demolition cost. 13 influencing factors and historical cost data were collected based on literature review and experts' advice, and two prediction models were constructed through regression analysis and the prediction accuracy was evaluated. As a result, it showed an average error rate of about 6 to 12%, and it was possible to explore the possibility of use as a reliable prediction model. The results of this study can contribute to estimating appropriate construction cost and improving related standards for domestic demolition works in the future.

• 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.

• 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.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.

• Journal of the Korea Institute of Building Construction
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• v.22 no.3
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• pp.317-326
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• 2022

With the development of various technologies in the area of artificial intelligence, a number of studies to application of artificial intelligence technology in the construction field are underway. Diverse technologies have been applied to the task of predicting construction costs, and construction cost prediction technologies applying artificial intelligence technologies have recently been developed. However, it is difficult to secure the vast amount of construction cost data required for machine learning, which has not yet been practically used. In this study, to predict the construction cost, the latest artificial neural network(ANN) method is used to propose a method to improve the construction cost prediction performance. In particular, to improve predictive performance, a log conversion method of target variables and a feature scaling method to eliminate the difference in the relative influence of each column data are applied, and their performance in predicting construction cost is compared and analyzed.

• 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.