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Combining rule-based expert systems and artificial neural networks for mark-up estimation

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  • Heng Li
  • Peter Love

Abstract

Rule-based expert systems and artificial neural networks are two major systems for developing intelligent decision support systems. The integration of the two systems can generate a new system which shares the strengths of both rule-based and artificial neural network systems. This research presents a computer based mark-up decision support system called InMES (integrated mark-up estimation system) that integrates a rule-based expert system and an artificial neural network (ANN) based expert system. The computer system represents an innovative approach for estimating a contractor's mark-up percentage for a construction project. A rule extraction method is developed to generate rules from a trained ANN. By using the explanation facility embedded in the rule-based expert system, InMES provides users with a clear explanation to justify the rationality of the estimated mark-up output. Cost data derived from a contractor's successful bids were used to train an ANN and, in conjunction with a rule-based expert system, select the expected mark-up for a project. The combination of both ANN- and rule-based expert systems for estimating mark-up allows significant benefits to be made from each individual system, such as understanding why and how the estimated mark-up was derived and also the effects of imposing rules and constraints on a company's mark-up estimation. The mark-up decision support system presented can assist contractors in preparing a rational mark-up percentage for a project. Moreover, InMES as proposed will assist contractors in their tender decision making, that is, whether or not to submit a bid for a project considering the estimated mark-up.

Suggested Citation

  • Heng Li & Peter Love, 1999. "Combining rule-based expert systems and artificial neural networks for mark-up estimation," Construction Management and Economics, Taylor & Francis Journals, vol. 17(2), pages 169-176.
    • Handle: RePEc:taf:conmgt:v:17:y:1999:i:2:p:169-176
    DOI: 10.1080/014461999371664
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    References listed on IDEAS

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    1. Patricia M. Hillebrandt, 1985. "Economic Theory and the Construction Industry," Palgrave Macmillan Books, Palgrave Macmillan, edition 0, number 978-1-349-17934-3.
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    Cited by:

    1. Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    2. Mohammed Fadhil Dulaimi & Hon Guo Shan, 2002. "The factors influencing bid mark-up decisions of large- and medium-size contractors in Singapore," Construction Management and Economics, Taylor & Francis Journals, vol. 20(7), pages 601-610.
    3. João Adelino Ribeiro & Paulo Jorge Pereira & Elisio Moreira Brandão, 2020. "A real options approach to optimal bidding in construction projects considering volume uncertainty," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(4), pages 631-640, June.

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