Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine ()

Gwang-Hee Kim, Jae-Min Shin, Sangyong Kim, Yoonseok Shin

Department of Architectural Engineering, Graduate School of Kyonggi University, Suwon-Si, Korea.

Department of Plant & Architectural Engineering, Kyonggi University, Suwon-Si, Korea.

School of Construction Management and Engineering, University of Reading, Reading, UK.

**DOI: **10.4236/jbcpr.2013.11001
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Department of Architectural Engineering, Graduate School of Kyonggi University, Suwon-Si, Korea.

Department of Plant & Architectural Engineering, Kyonggi University, Suwon-Si, Korea.

School of Construction Management and Engineering, University of Reading, Reading, UK.

Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.

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Kim, G. , Shin, J. , Kim, S. and Shin, Y. (2013) Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine. *Journal of Building Construction and Planning Research*, **1**, 1-7. doi: 10.4236/jbcpr.2013.11001.

Conflicts of Interest

The authors declare no conflicts of interest.

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