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Bergström, M and Stehn, L (2005) Benefits and disadvantages of ERP in industrialised timber frame housing in Sweden. Construction Management and Economics, 23(08), 831-8.

Bonnal, P, Gourc, D, Hameri, A-p and Lacoste, G (2005) A linear-discrete scheduling model for the resource-constrained project scheduling problem. Construction Management and Economics, 23(08), 797-814.

Bröchner, J, Josephson, P-e and Alte, J (2005) Identifying management research priorities. Construction Management and Economics, 23(08), 793-6.

Gangwar, M and Goodrum, P M (2005) The effect of time on safety incentive programs in the US construction industry. Construction Management and Economics, 23(08), 851-9.

Ivory, C (2005) The cult of customer responsiveness: is design innovation the price of a client-focused construction industry?. Construction Management and Economics, 23(08), 861-70.

Kadefors, A (2005) Fairness in interorganizational project relations: norms and strategies. Construction Management and Economics, 23(08), 871–8.

Larsen, G D (2005) Horses for courses: relating innovation diffusion concepts to the stages of the diffusion process. Construction Management and Economics, 23(08), 787-92.

Low, S P and Min, W (2005) Just-in-time management in the ready mixed concrete industries of Chongqing, China and Singapore. Construction Management and Economics, 23(08), 815-29.

Myers, D (2005) A review of construction companies' attitudes to sustainability. Construction Management and Economics, 23(08), 781-5.

Zayed, T M, Halpin, D W and Basha, I M (2005) Productivity and delays assessment for concrete batch plant-truck mixer operations. Construction Management and Economics, 23(08), 839-50.

  • Type: Journal Article
  • Keywords: Artificial Neural Network (ANN); productivity; cost analysis; cycle time; modeling; concrete batch plant (CBP); truck mixer
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/01446190500184451
  • Abstract:

    Current research focuses on assessing productivity, cost, and delays for concrete batch plant (CBP) operations using Artificial Neural Network (ANN) methodology. Data were collected to assess cycle time, delays, cost of delays, cost of delivery, productivity, and price/m3 for the CBP. Two ANN models were designated to represent the CBP process considering many CBP variables. Input variables include delivery distance, concrete type, and truck mixer’s load. Output variables include the assessment of cycle time, cost of delays, delivery cost, productivity, and price/m3. The ANN outputs have been validated to show the ANN’s robustness in assessing the CBP output variables. The average validity percent for the ANN outputs is 96.25%. A Time-Quantity (TQ) chart is developed to assess the time required for both truck mixers and the CBP to produce a specified quantity of concrete. Charts have been developed to predict cycle time/truck, delays/truck, cost of delays/truck, cost of delivery/m3, and price/m3.