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Bridge, A J and Tisdell, C (2004) The determinants of the vertical boundaries of the construction firm. Construction Management and Economics, 22(08), 807-25.

Cuervo, J C and Low, S P (2004) Global performance measures for transnational construction corporations. Construction Management and Economics, 22(08), 851-60.

Dainty, A R J, Cheng, M-I and Moore, D R (2004) A competency-based performance model for construction project managers. Construction Management and Economics, 22(08), 877-86.

Dorée, A G and Holmen, E (2004) Achieving the unlikely: innovating in the loosely coupled construction system. Construction Management and Economics, 22(08), 827-38.

Poon, C S, Yu, A T W, See, S C and Cheung, E (2004) Minimizing demolition wastes in Hong Kong public housing projects. Construction Management and Economics, 22(08), 799-805.

Pryke, S D (2004) Analysing construction project coalitions: exploring the application of social network analysis. Construction Management and Economics, 22(08), 787-97.

Tam, C M, Tong, T K L and Tse, S L (2004) Modelling hook times of mobile cranes using artificial neural networks. Construction Management and Economics, 22(08), 839-49.

  • Type: Journal Article
  • Keywords: Artificial neural networks; hook time; mobile crane
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/0144619042000202771
  • 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.

Trigunarsyah, B (2004) Project owners' role in improving constructability of construction projects: an example analysis for Indonesia. Construction Management and Economics, 22(08), 861-76.