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Berg, R and Hinze, J (2005) Theft and Vandalism on Construction Sites. Journal of Construction Engineering and Management, 131(07), 826–33.

Castañeda, J A, Tucker, R L and Haas, C T (2005) Workers’ Skills and Receptiveness to Operate Under the Tier II Construction Management Strategy. Journal of Construction Engineering and Management, 131(07), 799–807.

Chua, D K and Shen, L J (2005) Key Constraints Analysis with Integrated Production Scheduler. Journal of Construction Engineering and Management, 131(07), 753–64.

Horman, M J and Thomas, H R (2005) Role of Inventory Buffers in Construction Labor Performance. Journal of Construction Engineering and Management, 131(07), 834–43.

Lee, S, Thomas, S R and Tucker, R L (2005) Web-Based Benchmarking System for the Construction Industry. Journal of Construction Engineering and Management, 131(07), 790–8.

Mitropoulos, P, Abdelhamid, T S and Howell, G A (2005) Systems Model of Construction Accident Causation. Journal of Construction Engineering and Management, 131(07), 816–25.

Park, H, Thomas, S R and Tucker, R L (2005) Benchmarking of Construction Productivity. Journal of Construction Engineering and Management, 131(07), 772–8.

Thomas, H R, Riley, D R and Messner, J I (2005) Fundamental Principles of Site Material Management. Journal of Construction Engineering and Management, 131(07), 808–15.

Wilmot, C G and Mei, B (2005) Neural Network Modeling of Highway Construction Costs. Journal of Construction Engineering and Management, 131(07), 765–71.

  • Type: Journal Article
  • Keywords: Construction costs; Bids; Cost estimates; Neural networks; Forecasting; Predictions; Highway construction;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2005)131:7(765)
  • Abstract:
    The objective of this research was to develop a procedure that estimates the escalation of highway construction costs over time. An artificial neural network model was developed which relates overall highway construction costs, described in terms of a highway construction cost index, to the cost of construction material, labor, and equipment, the characteristics of the contract and the contracting environment prevailing at the time the contract was let. Results demonstrate that the model is able to replicate past highway construction cost trends in Louisiana with reasonable accuracy. Future construction input costs are estimated from commercially available forecasts of indicator variables closely associated with the price of construction labor, construction equipment, and a representative set of highway construction materials. Future contract characteristics and the contracting environment that is likely to exist in the future are estimated from past trends or stipulated to be consistent with policy decisions in the future. The predictions produced by the model estimate that highway construction costs in Louisiana will double between 1998 and 2015.

Xu, T, Tiong, R L, Chew, D A and Smith, N J (2005) Development Model for Competitive Construction Industry in the People’s Republic of China. Journal of Construction Engineering and Management, 131(07), 844–53.

Zayed, T M and Halpin, D W (2005) Productivity and Cost Regression Models for Pile Construction. Journal of Construction Engineering and Management, 131(07), 779–89.