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Selection of vertical formwork system by probabilistic neural networks models

Author

Listed:
  • C. M. Tam
  • Thomas Tong
  • Tony Lau
  • K. K. Chan

Abstract

A good formwork system enables speedy completion of the concrete structure, following which other subsequent trades can be started. However, the current intuitive judgment approach in the selection of formwork systems cannot assure an optimal and consistent result. Artificial neural networks may improve the selection process. Formwork represents a significant part of the cost of concrete structure construction. Most subsequent trades including internal finishing and external cladding depend on the completion of the building structure. A suitable formwork system is thus crucial for maintaining the smooth flow of the various trades and a proper working sequence of various work activities. Based on data collected from a previous study, it is clear that the key factors affecting the selection of a relevant formwork system include building height and structural system, concrete finish, site conditions, availability of equipment and building shape. Neural network models are developed for the selection of vertical formwork systems using the architecture of the probabilistic neural network (PNN) model. A case study verifies the validity of this approach.

Suggested Citation

  • C. M. Tam & Thomas Tong & Tony Lau & K. K. Chan, 2005. "Selection of vertical formwork system by probabilistic neural networks models," Construction Management and Economics, Taylor & Francis Journals, vol. 23(3), pages 245-254.
    • Handle: RePEc:taf:conmgt:v:23:y:2005:i:3:p:245-254
    DOI: 10.1080/0144619042000205912
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    Cited by:

    1. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2013. "Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques," International Journal of Information Management, Elsevier, vol. 33(4), pages 627-641.

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