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

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  • Shasha Xie
  • Jun Fang

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.

Suggested Citation

  • Shasha Xie & Jun Fang, 2018. "Prediction of construction cost index based on multi variable grey neural network model," International Journal of Information Systems and Change Management, Inderscience Enterprises Ltd, vol. 10(3), pages 209-226.
    • Handle: RePEc:ids:ijiscm:v:10:y:2018:i:3:p:209-226
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    Cited by:

    1. Nguyen, Phong Thanh, 2020. "Application Machine Learning in Construction Management," MPRA Paper 109899, University Library of Munich, Germany, revised 01 Aug 2021.
    2. Biolek Vojtech & Hanak Tomaš, 2024. "Life-cycle cost estimation of a building structure: An example of partition walls," Organization, Technology and Management in Construction, Sciendo, vol. 16(1), pages 13-26, January.

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