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Modelling hook times of mobile cranes using artificial neural networks

Author

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  • C.M. Tam
  • Thomas K.L. Tong
  • Sharon L. Tse

Abstract

The hook times of mobile cranes are processes that are of non-linear and discrete nature. Artificial neural network is a data processing technique that lends itself to this kind of problem. Three common artificial neural network architectures - multi-layer feed-forward (MLFF), group method of data handling (GMDH) and general regression neural network (GRNN) - are compared. The results show that the GRNN model aided with genetic algorithm (GA) is most promising in describing the non-linear and discrete nature of the hook times. The MLFF model can also give a moderate level of accuracy in the estimation of hook travelling times of mobile cranes and is ranked second. The GMDH model is outperformed by the former two due to a less promising R-square.

Suggested Citation

  • C.M. Tam & Thomas K.L. Tong & Sharon L. Tse, 2004. "Modelling hook times of mobile cranes using artificial neural networks," Construction Management and Economics, Taylor & Francis Journals, vol. 22(8), pages 839-849, October.
    • Handle: RePEc:taf:conmgt:v:22:y:2004:i:8:p:839-849
    DOI: 10.1080/0144619042000202771
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