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Cox, A and Ireland, P (2002) Managing construction supply chains: the common sense approach. Engineering, Construction and Architectural Management, 9(05), 409–18.

Fo, S, Marsh, x and Cockerham, G (2002) How building design imperatives constrain construction productivity and quality. Engineering, Construction and Architectural Management, 9(05), 378–87.

Formoso, C T, Tzortzopoulos, P and Liedtke, R (2002) A model for managing the product development process in house building. Engineering, Construction and Architectural Management, 9(05), 419–32.

Lim, E H and Ling, F Y Y (2002) Model for predicting clients' contribution to project success. Engineering, Construction and Architectural Management, 9(05), 388–95.

Soetanto, R and Proverbs, D G (2002) Modelling the satisfaction of contractors: the impact of client performance. Engineering, Construction and Architectural Management, 9(05), 453–65.

Stephenson, P, Morrey, I, Vacher, P and Ahmed, Z (2002) Acquisition and structuring of knowledge for defect prediction in brickwork mortar. Engineering, Construction and Architectural Management, 9(05), 396–408.

Tam, C M, Tong, T K L and Tse, S L (2002) Artificial neural networks model for predicting excavator productivity. Engineering, Construction and Architectural Management, 9(05).

  • Type: Journal Article
  • Keywords: artificial neural network; excavator; multiple regression; productivity
  • ISBN/ISSN: 0969-9988
  • URL: http://www.blackwell-synergy.com/links/doi/10.1046/j.1365-232X.2002.00277.x/abs/
  • Abstract:
    This paper aims to develop a quantitative model for predicting the productivity of excavators using artificial neural networks (ANN), which is then compared with the multiple regression model developed by Edwards & Holt (2000). A neural network using the architecture of multilayer feedforward (MLFF) is used to model the productivity of excavators. Finally, the modelling methods, predictive behaviours and the advantages of each model are discussed. The results show that the ANN model is suitable for mapping the non-linear relationship between excavation activities and the performance of excavators. It concludes that the ANN model is an ideal alternative for estimating the productivity of excavators.

Whyte, J, Bouchlaghem, D and Thorpe, A (2002) IT implementation in the construction organization. Engineering, Construction and Architectural Management, 9(05), 371–7.

Zhang, H, Shi, J J and Tam, C-M (2002) Application of simulation related techniques to construction operations. Engineering, Construction and Architectural Management, 9(05), 433–45.