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Ling, F Y Y (2005) Models for predicting quality of building projects. Engineering, Construction and Architectural Management, 12(01), 6–20.

Love, P E and Edwards, D J (2005) Taking the pulse of UK construction project managers' health: Influence of job demands, job control and social support on psychological wellbeing. Engineering, Construction and Architectural Management, 12(01), 88–101.

Othman, A A E, Hassan, T M and Pasquire, C L (2005) Analysis of factors that drive brief development in construction. Engineering, Construction and Architectural Management, 12(01), 69–87.

Oyedele, L O and Tham, K W (2005) Examining architects' performance in Nigerian private and public sectors building projects. Engineering, Construction and Architectural Management, 12(01), 52–68.

Peansupap, V and Walker, D (2005) Factors affecting ICT diffusion: A case study of three large Australian construction contractors. Engineering, Construction and Architectural Management, 12(01), 21–37.

Williams, T P (2005) Bidding ratios to predict highway project costs. Engineering, Construction and Architectural Management, 12(01), 38–51.

  • Type: Journal Article
  • Keywords: Construction Industry; costs; neural nets; regression analysis; roads
  • ISBN/ISSN: 0969-9988
  • URL: http://ariel.emeraldinsight.com/vl=8296175/cl=17/nw=1/rpsv/cw/mcb/09699988/v12n1/s3/p38
  • Abstract:
    Purpose - Ratios were constructed using bidding data for highway construction projects in Texas to study whether there are useful patterns in project bids that are indicators of the project completion cost. The use of the ratios to improve predictions of completed project cost was studied. Design/methodology/approach - Ratios were calculated relating the second lowest bid, mean bid, and maximum bid to the low bid for the highway construction projects. Regression and neural network models were developed to predict the completed cost of the highway projects using bidding data. Models including the bidding ratios, low bid, second lowest bid, mean bid and maximum bid were developed. Natural log transformations were applied to the data to improve model performance. Findings - Analysis of the bidding ratios indicates some relationship between high values of the bidding ratios and final project costs that deviate significantly from the low bid amount. Addition of the ratios to neural network and regression models to predict the completed project cost were not found to enhance the predictions. The best performing regression model used only the low bid as input. The best performing neural network model used the low bid and second lowest bid as inputs. Originality/value - The nature of bid ratios that can describe the pattern of bids submitted for a project and the relationship of the ratios to project outcomes were studied. The ratio values may be useful indicators of project outcome that can be used by construction managers.