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A fuzzy neural network approach for contractor prequalification

Author

Listed:
  • K. C. Lam
  • Tiesong Hu
  • S. Thomas Ng
  • Martin Skitmore
  • S. O. Cheung

Abstract

Non-linearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which lead to the process being more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eightyfive cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractors' ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The fuzzy neural network is a practical approach for modelling contractor prequalification.

Suggested Citation

  • K. C. Lam & Tiesong Hu & S. Thomas Ng & Martin Skitmore & S. O. Cheung, 2001. "A fuzzy neural network approach for contractor prequalification," Construction Management and Economics, Taylor & Francis Journals, vol. 19(2), pages 175-188.
    • Handle: RePEc:taf:conmgt:v:19:y:2001:i:2:p:175-188
    DOI: 10.1080/01446190150505108
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    Citations

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    Cited by:

    1. Song Xu & Reena Nupur & Devika Kannan & Rashi Sharma & Pallavi Sharma & Sushil Kumar & P. C. Jha & Chunguang Bai, 2023. "An integrated fuzzy MCDM approach for manufacturing process improvement in MSMEs," Annals of Operations Research, Springer, vol. 322(2), pages 1037-1073, March.
    2. 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.
    3. Mimović Predrag & Krstić Ana, 2016. "Application of Multi-Criteria Analysis in the Public Procurement Process Optimization," Economic Themes, Sciendo, vol. 54(1), pages 103-128, March.
    4. Amirhosein Jafari, 2013. "A contractor pre-qualification model based on the quality function deployment method," Construction Management and Economics, Taylor & Francis Journals, vol. 31(7), pages 746-760, July.
    5. K. C. Lam & T. S. Hu & S. T. Ng, 2005. "Using the principal component analysis method as a tool in contractor pre-qualification," Construction Management and Economics, Taylor & Francis Journals, vol. 23(7), pages 673-684.
    6. Aziz Naghizadeh Vardin & Ramin Ansari & Mohammad Khalilzadeh & Jurgita Antucheviciene & Romualdas Bausys, 2021. "An Integrated Decision Support Model Based on BWM and Fuzzy-VIKOR Techniques for Contractor Selection in Construction Projects," Sustainability, MDPI, vol. 13(12), pages 1-28, June.

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