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http://dx.doi.org/10.5345/JKIBC.2013.13.1.066

A Comparison of Construction Cost Estimation Using Multiple Regression Analysis and Neural Network in Elementary School Project  

Cho, Hong-Gyu (Graduate School, Kyonggi University)
Kim, Kyong-Gon (Graduate School, Kyonggi University)
Kim, Jang-Young (The fifth rank official, Gyeonggi Provincial office of Education)
Kim, Gwang-Hee (Dept. of Plant & Architectural Engineering, Kyonggi University)
Publication Information
Journal of the Korea Institute of Building Construction / v.13, no.1, 2013 , pp. 66-74 More about this Journal
Abstract
In the early stages of a construction project, the most important thing is to predict construction costs in a rational way. For this reason, many studies have been performed on the estimation of construction costs for apartment housing and office buildings at early stage using artificial intelligence, statistics, and the like. In this study, cost data held by a provincial Office of Education on elementary schools constructed from 2004 to 2007 were used to compare the multiple regression model with an artificial neural network model. A total of 96 historical data were classified into 76 historical data for constructing models and 20 historical data for comparing the constructed regression model with the artificial neural network model. The results of an analysis of predicted construction costs were that the error rate of the artificial neural network model is lower than that of the multiple regression model.
Keywords
elementary school; construction cost estimation; multiple regression analysis; artificial neural networks;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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