Journal
AUTOMATION IN CONSTRUCTION
Volume 18, Issue 3, Pages 321-329Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.autcon.2008.09.007
Keywords
Pre-qualification; Contractor selection; Procurement; Support vector machine; Artificial neural network
Funding
- Research Grants Council of Hong Kong Special Administration Region, China [9041155]
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In complex and high value projects, prequalification is crucial for both contractors and clients, as it targets towards best value delivery through qualification safeguards and streamlined competition among potential candidates. Due the complex nature of the procurement problems such as prequalification exercises, the robust models are rarely attempted. The research reported in this paper presents an overview of potential suitability of Support Vector Machine (SVM) method for contractor/consultant prequalification transactions in the construction project procurements. Furthermore, the performance of SVM is compared with specific artificial neural network outcomes. The results obtained from practical datasets indicate encouraging potentials for SVM applications in the procurement problems such as prequalification and contractor selection. Hence, a SVM-based decision support framework is proposed. (C) 2008 Elsevier B.V. All rights reserved.
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