The selection criteria for contractor prequalification are characterized by the coexistence of both quantitative and qualitative data. The qualitative data is nonlinear, uncertain and imprecise. An ideal decision support system for contractor prequalification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear 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 prequalification criteria variables were identified for the model. One hundred and twelve real prequalification cases were collected from civil engineering projects in Hong Kong, and 88 hypothetical prequalification cases were also generated according to the Ifthen rules used by professionals in the prequalification process. The results of the analysis totally comply with current practice public developers in Hong Kong. Each prequalification case consisted of input ratings for candidate contractors' attributes and their corresponding prequalification 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. Crossvalidation was applied to estimate the generalization errors based on the resampling of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors' attributes and their corresponding prequalification disqualification decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor prequalification task.
Engineering, Construction and Architectural Management – Emerald Publishing
Published: Mar 1, 2000
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