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Chan, A T S and Chan, E H W (2005) Impact of Perceived Leadership Styles on Work Outcomes: Case of Building Professionals. Journal of Construction Engineering and Management, 131(04), 413–22.

Cheng, E W L and Li, H (2005) Analytic Network Process Applied to Project Selection. Journal of Construction Engineering and Management, 131(04), 459–66.

Ekström, M A and Björnsson, H C (2005) Valuing Flexibility in Architecture, Engineering, and Construction Information Technology Investments. Journal of Construction Engineering and Management, 131(04), 431–8.

El-Rayes, K and Hyari, K (2005) {[}CONLIGHT:{]} Lighting Design Model for Nighttime Highway Construction. Journal of Construction Engineering and Management, 131(04), 467–76.

El-Rayes, K and Kandil, A (2005) Time-Cost-Quality Trade-Off Analysis for Highway Construction. Journal of Construction Engineering and Management, 131(04), 477–86.

Elazouni, A M and Metwally, F G (2005) Finance-Based Scheduling: Tool to Maximize Project Profit Using Improved Genetic Algorithms. Journal of Construction Engineering and Management, 131(04), 400–12.

Gil, N, Tommelein, I D, Stout, A and Garrett, T (2005) Embodying Product and Process Flexibility to Cope with Challenging Project Deliveries. Journal of Construction Engineering and Management, 131(04), 439–48.

Hinze, J (2005) Use of Trench Boxes for Worker Protection. Journal of Construction Engineering and Management, 131(04), 494–500.

Lee, E and Ibbs, C W (2005) Computer Simulation Model: Construction Analysis for Pavement Rehabilitation Strategies. Journal of Construction Engineering and Management, 131(04), 449–58.

Liu, M and Ling, Y Y (2005) Modeling a Contractor’s Markup Estimation. Journal of Construction Engineering and Management, 131(04), 391–9.

  • Type: Journal Article
  • Keywords: Construction management; Neural networks; Bids; Fuzzy sets; Contractors;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2005)131:4(391)
  • Abstract:
    The estimation of markup is a difficult process for contractors in a changeable and uncertain construction environment. In this study, a fuzzy logic-based artificial neural network (ANN) model, called the fuzzy neural network (FNN) model, is constructed to assist contractors in making markup decisions. With the fuzzy logic inference system integrated inside, the FNN model provides users with a clear explanation to justify the rationality of the estimated markup output. Meanwhile, with the self-learning ability of ANN, the accuracy of the estimation results is improved. From a survey and interview with local contractors, the factors that affect markup estimation and the rules applied in the markup decision are identified. Based on the finding, both ANN and FNN models were constructed and trained in different project scenarios. The comparison of the two models shows that FNN will assist contractors with markup estimation with more accurate results and convincing user-defined linguistic rules inside.

Navon, R and Shpatnitsky, Y (2005) Field Experiments in Automated Monitoring of Road Construction. Journal of Construction Engineering and Management, 131(04), 487–93.

Zhang, J, Eastham, D L and Bernold, L E (2005) Waste-Based Management in Residential Construction. Journal of Construction Engineering and Management, 131(04), 423–30.