Prediction of construction cost index based on multi variable grey neural network model

Shasha Xie and Jun Fang

International Journal of Information Systems and Change Management, 2018, vol. 10, issue 3, 209-226

Abstract: The construction cost index can reflect the construction market price changes on the project cost. Due to unsuitable way of management and the consideration of data security, there is the possibility of researchable sample data missing, or the possibility of not enough data. This way many classical prediction models cannot function well in practice. The study compared numerous models. According to the characteristics of poor sample data and numerous factors for construction engineering, the grey model and BP neural network were selected to establish the prediction model. This study built material and machinery shift as independent variables, the construction cost as the dependent variable to establish the grey neural network model. In empirical analysis, data processing, training and simulation were used by MATLAB, the results concluded: compared with other conventional models, the combined forecasting model for poor data is more effective.

Keywords: construction cost index; grey model; BP neural network; poor data sample; prediction model. (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijiscm:v:10:y:2018:i:3:p:209-226

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