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Decision support system for contractor pre‐qualification—artificial neural network model

K.C. LAM (Department of Building and Construction, City University of Hong Kong, Hong Kong)
S. THOMAS NG (Department of Civil Engineering, The University of Hong Kong, Hong Kong)
TIESONG HU (Department of Hydraulic Engineering, Wuhan University of Hydraulic and Electric Engineering, China)
MARTIN SKITMORE (School of Construction Management and Property, Queensland University of Technology, Australia)
S.O. CHEUNG (Department of Building and Construction, City University of Hong Kong, Hong Kong)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 1 March 2000

Abstract

The selection criteria for contractor pre‐qualification are characterized by the co‐existence of both quantitative and qualitative data. The qualitative data is non‐linear, uncertain and imprecise. An ideal decision support system for contractor pre‐qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated non‐linear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre‐qualification criteria (variables) were identified for the model. One hundred and twelve real pre‐qualification cases were collected from civil engineering projects in Hong Kong, and 88 hypothetical pre‐qualification cases were also generated according to the ‘If‐then’ rules used by professionals in the pre‐qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre‐qualification case consisted of input ratings for candidate contractors' attributes and their corresponding pre‐qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross‐validation was applied to estimate the generalization errors based on the ‘re‐sampling’ of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated non‐linear relationship between contractors' attributes and their corresponding pre‐qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre‐qualification task.

Keywords

Citation

LAM, K.C., THOMAS NG, S., HU, T., SKITMORE, M. and CHEUNG, S.O. (2000), "Decision support system for contractor pre‐qualification—artificial neural network model", Engineering, Construction and Architectural Management, Vol. 7 No. 3, pp. 251-266. https://doi.org/10.1108/eb021150

Publisher

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MCB UP Ltd

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