• International conference on construction engineering and project management
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• 2009.05a
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• pp.203-211
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• 2009

Korean public owners who order public multi-family housing construction projects have yet to gain access to a model for predicting construction cost. For this reason, their construction cost prediction is mainly dependent upon historic data and experience. In this paper, a cost-prediction model based on Case-Based Reasoning (CBR) in the design phase of public multi-family housing construction projects was developed. The developed model can determine the total construction cost by estimating the different Building, Civil, Mechanical, Electronic and Telecommunication, and Landscaping work costs. Model validation showed an accuracy of 97.56%, confirming the model's excellent viability. The developed model can thus be used to predict the construction cost to be shouldered by public owners before the design is completed. Moreover, any change orders during the design phase can be immediately applied to the model, and various construction costs by design alternative can be verified using this model. Therefore, it is expected that public owners can exercise effective design management by using the developed cost prediction model. The use of such an effective cost prediction model can enable the owners to accurately determine in advance the construction cost and prevent increase or decrease in cost arising from the design changes in the design phase, such as change order. The model can also prevent the untoward increase in the duration of the design phase as it can effectively control unnecessary change orders.

• Journal of the Korean Society of Safety
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• v.32 no.1
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• pp.115-120
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• 2017

The aim of this study is to develop construction safety and health management cost prediction model using support vector machine (SVM). To this end, theoretical concept of SVM is investigated to formulate the cost prediction model. Input and output variables have been selected by analyzing the balancing accounts for the completed construction project. In order to train and validate the proposed prediction model, 150 data sets have been gathered from field. Effects of SVM parameters on prediction accuracy are analyzed and from which the optimal parameter values have been determined. The prediction performance tests are conducted to confirm the applicability of the proposed model. Based on the results, it is concluded that the proposed SVM model can effectively be used to predict the construction safety and health management cost.

• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.3
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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.

• 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.

• 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.

• 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.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.

• 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.

• Journal of KIBIM
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• v.5 no.2
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• pp.34-45
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• 2015

As buildings become larger and more complicated, construction costs have increased with a considerable effect on buildings' Life Cycle Cost (LCC). However, there has been little consideration on economic aspects in the selection of construction materials due to limited information on the materials and dependency in architects' experience and inefficiency in cost estimation, causing design changes, increase in maintenance cost, difficulty in budgeting, and decrease in building performance. To solve these problems, this study proposed a BIM-based material selection model which reflects the comprehensive economic efficiency of building materials. Our cost prediction model can estimates the material-related cost during the entire building life cycle. Furthermore, we implemented the proposed model in connection with BIM, which can analyze and compare LCC by material. Through the validation of the model, we could confirm the necessity of LCC-based material selection in comparison with the conventional cost-centered material selection.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.653-661
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• 2007

Overseas construction projects tend to be more complex than domestic projects, being exposed to more external risks, such as politics, economy, society, and culture, as well as more internal risks from the project itself. It is crucial to have an early understanding of the project condition, in order to be well prepared in various phases of the project. This study compares a structural equation model and multiple regression analysis, in their capacity to predict cost performance of international construction projects. The structural equation model shows a more accurate prediction of cost performance than does regression analysis, due to its intrinsic capability of considering various cost factors in a systematic way.