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A neural networks approach for cost flow forecasting

Author

Listed:
  • A. H. Boussabaine
  • A. P. Kaka

Abstract

Artificial neural networks, which simulate neuronal systems of the brain, are useful methods that have attracted the attention of researchers in many disciplinary areas. They have many advantages over traditional methods in situations where the input-output relationship of the system under study is not explicitly known. This paper investigates the feasibility of using neural networks for predicting the cost flow of construction projects, explains the need for cost flow forecasting, and demonstrates the limitation of the existing models. It then introduces neural networks as an alternative approach to those mathematical and statistical methods. The method used in collecting data and modelling the cost flow is described. Results of the testing are presented and discussed.

Suggested Citation

  • A. H. Boussabaine & A. P. Kaka, 1998. "A neural networks approach for cost flow forecasting," Construction Management and Economics, Taylor & Francis Journals, vol. 16(4), pages 471-479.
    • Handle: RePEc:taf:conmgt:v:16:y:1998:i:4:p:471-479
    DOI: 10.1080/014461998372240
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    Cited by:

    1. Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    2. Mahir Msawil & Faris Elghaish & Krisanthi Seneviratne & Stephen McIlwaine, 2021. "Developing a Parametric Cash Flow Forecasting Model for Complex Infrastructure Projects: A Comparative Study," Sustainability, MDPI, vol. 13(20), pages 1-26, October.
    3. Qingbin Cui & Makarand Hastak & Daniel Halpin, 2010. "Systems analysis of project cash flow management strategies," Construction Management and Economics, Taylor & Francis Journals, vol. 28(4), pages 361-376.
    4. Cheng, Min-Yuan & Cao, Minh-Tu & Herianto, Jason Ghorman, 2020. "Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    5. Xin J. Ge & G. Runeson, 2004. "Modeling Property Prices Using Neural Network Model for Hong Kong," International Real Estate Review, Global Social Science Institute, vol. 7(1), pages 121-138.
    6. A. H. Boussabaine & Taha Elhag, 1999. "Applying fuzzy techniques to cash flow analysis," Construction Management and Economics, Taylor & Francis Journals, vol. 17(6), pages 745-755.

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