Modelling hook times of mobile cranes using artificial neural networks

C.M. Tam, Thomas K.L. Tong and Sharon L. Tse

Construction Management and Economics, 2004, vol. 22, issue 8, 839-849

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.

Date: 2004
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DOI: 10.1080/0144619042000202771

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