Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 9 results ...

Buys, F and Nkado, R (2006) A survey of maintenance management systems in South African tertiary educational institutions. Construction Management and Economics, 24(10), 997-1005.

Carmichael, S and Murray, M (2006) Record keeping for contemporaneous delay analysis: a model for effective event management. Construction Management and Economics, 24(10), 1007-18.

Carrillo, P M, Robinson, H S, Anumba, C J and Bouchlaghem, N M (2006) A Knowledge Transfer Framework: the PFI context. Construction Management and Economics, 24(10), 1045-56.

Eeing, B T and Kruse, J B (2006) Valuing self-protection: income and certification effects for safe rooms. Construction Management and Economics, 24(10), 1057-68.

Fortune, C (2006) Process standardisation and the impact of professional judgement on the formulation of building project budget price advice. Construction Management and Economics, 24(10), 1091-8.

Fu, W-K, Lo, H-P and Drew, D S (2006) Collective learning, collective knowledge and learning networks in construction. Construction Management and Economics, 24(10), 1019-28.

Huang, R-Y (2006) A performance-based bridge LCCA model using visual inspection inventory data. Construction Management and Economics, 24(10), 1069-81.

Ok, S C and Sinha, S K (2006) Construction equipment productivity estimation using artificial neural network model. Construction Management and Economics, 24(10), 1029-44.

  • Type: Journal Article
  • Keywords: Construction equipment; artificial neural network; productivity estimation
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/01446190600851033
  • Abstract:

    Estimating equipment production rates is both an art and a science. An accurate prediction of the productivity of earthmoving equipment is critical for accurate construction planning and project control. Owing to the unique work requirements and changeable environment of each construction project, the influences of job and management factors on operation productivity are often very complex. Hence, construction productivity estimation, even for an operation with well-known equipment and work methods, can be challenging. This study develops and compares two methods for estimating construction productivity of dozer operations (the transformed regression analysis, and a non-linear analysis using neural network model). It is the hypothesis of this study that the proposed neural networks model may improve productivity estimation models because of the neural network’s inherent ability to capture non-linearity and the complexity of the changeable environment of each construction project. The comparison of results suggests that the non-linear artificial neural network (ANN) has the potential to improve the equipment productivity estimation model.

Stoy, C and Kytzia, S (2006) Benchmarking electricity consumption. Construction Management and Economics, 24(10), 1083-9.