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A fuzzy neural network approach for contractor pre-qualification

Lam, K.C., Hu, T., Ng, S.T., Skitmore, Martin, & Cheung, S.O. (2001) A fuzzy neural network approach for contractor pre-qualification. Construction Management and Economics, 19(2), pp. 175-188.

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Abstract

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process 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 the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five 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. Contractor’s 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 FNN is a practical approach for modelling contractor prequalification.

Impact and interest:

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ID Code: 29549
Item Type: Journal Article
Additional URLs:
Keywords: Fuzzy reasoning, neural network, contractor prequalification
DOI: 10.1080/01446190150505108
ISSN: 0144-6193
Subjects: Australian and New Zealand Standard Research Classification > BUILT ENVIRONMENT AND DESIGN (120000) > BUILDING (120200) > Building Construction Management and Project Planning (120201)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Urban Development
Copyright Owner: Copyright 2001 Routledge
Deposited On: 08 Jan 2010 10:56
Last Modified: 11 Aug 2011 02:29

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