A support vector machine model for contractor prequalification

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.

[1]  David Eaton,et al.  Contractor pre-qualification model: State-of-the-art , 2007 .

[2]  Keith R. Molenaar,et al.  Engineering The Procurement Phase to Achieve Best Value , 2003 .

[3]  Ekambaram Palaneeswaran,et al.  Recent advances and proposed improvements in contractor prequalification methodologies , 2001 .

[4]  Awad S. Hanna,et al.  Application of Neural Networks to Owner-Contractor Prequalification , 1997 .

[5]  Miroslaw J. Skibniewski,et al.  DECISION CRITERIA IN CONTRACTOR PREQUALIFICATION , 1988 .

[6]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[7]  Paul. Olomolaiye,et al.  A needs-based methodology for classifying construction clients and selecting contractors , 1998 .

[8]  Zedan Hatush,et al.  Evaluating contractor prequalification data: selection criteria and project success factors , 1997 .

[9]  Martin Skitmore,et al.  Assessment and evaluation of contractor data against client goals using PERT approach , 1997 .

[10]  Jeffrey S. Russell,et al.  QUALIFIER-1: CONTRACTOR PREQUALIFICATION MODEL , 1990 .

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  Victor Sanvido,et al.  Design/Build Prequalification System , 1994 .

[13]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[14]  Jen-Ying Shih,et al.  A study of Taiwan's issuer credit rating systems using support vector machines , 2006, Expert Syst. Appl..

[15]  Sheng-Tun Li,et al.  The evaluation of consumer loans using support vector machines , 2006, Expert Syst. Appl..

[16]  Ekambaram Palaneeswaran,et al.  Contractor Selection for Design/Build Projects , 2000 .

[17]  Hun Hee Cho,et al.  Application of support vector machines in assessing conceptual cost estimates , 2007 .

[18]  Robert L. K. Tiong,et al.  Contractor Selection Criteria: Investigation of Opinions of Singapore Construction Practitioners , 2006 .

[19]  Keith R. Molenaar,et al.  PUBLIC-SECTOR DESIGN/BUILD EVOLUTION AND PERFORMANCE , 1999 .

[20]  Martin Skitmore,et al.  Criteria for contractor selection , 1997 .

[21]  James H. Garrett,et al.  Artificial Neural Networks for Civil Engineers: Fundamentals and Applications , 1997 .

[22]  Xiaowei Yang,et al.  Support vector machine-based multi-source multi-attribute information integration for situation assessment , 2008, Expert Syst. Appl..

[23]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[24]  Gary David Holt,et al.  Evaluating prequalification criteria in contractor selection , 1994 .

[25]  Miroslaw J. Skibniewski,et al.  Qualifier-2: Knowledge-Based System for Contractor Prequalification , 1990 .

[26]  Zhili Gao,et al.  Selecting an architecture‐engineering team by using fuzzy set theory , 2008 .

[27]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[28]  Ekambaram Palaneeswaran,et al.  Web-Based Client Advisory Decision Support System for Design.Builder Prequalification , 2005 .

[29]  David Paull,et al.  Machine learning of poorly predictable ecological data , 2006 .

[30]  S. T Ng,et al.  Client and consultant perspectives of prequalification criteria , 1999 .

[31]  Frank C. Harris,et al.  Model for assessing building contractors' project performance , 1996 .

[32]  Hongbo Zhao.  Slope reliability analysis using a support vector machine , 2008 .

[33]  Carlos H. Caldas,et al.  Automating hierarchical document classification for construction management information systems , 2003 .

[34]  Victor Sanvido,et al.  IMPLEMENTING A DESIGN/BuILD PREQUALIFICATION SYSTEM , 1995 .

[35]  Mohan M. Kumaraswamy,et al.  Contractor evaluation and selection: a Hong Kong perspective , 1996 .

[36]  Lloyd M. Waugh,et al.  Contractor selection: qualification and bid evaluation , 1996 .

[37]  Ka Chi Lam,et al.  Decision support system for contractor pre-qualification : artificial neural network model , 2000 .

[38]  Tiesong Hu,et al.  A fuzzy neural network approach for contractor prequalification , 2001 .

[39]  Jeffrey S. Russell,et al.  Contractor prequalification data for construction owners , 1992 .

[40]  Rustam M. Vahidov,et al.  Application of machine learning techniques for supply chain demand forecasting , 2008, Eur. J. Oper. Res..

[41]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[42]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

[43]  Gary David Holt,et al.  Evaluating performance potential in the selection of construction contractors , 1994 .

[44]  Yongsheng Ding,et al.  Forecasting financial condition of Chinese listed companies based on support vector machine , 2008, Expert Syst. Appl..

[45]  Pijush Samui,et al.  Support vector machine applied to settlement of shallow foundations on cohesionless soils , 2008 .

[46]  Vadlamani Ravi,et al.  Soft computing system for bank performance prediction , 2008, Appl. Soft Comput..

[47]  Jeffrey S. Russell,et al.  Constructor Prequalification: Choosing the Best Constructor and Avoiding Constructor Failure , 1996 .

[48]  Robert L. K. Tiong,et al.  A Fuzzy Decision Framework for Contractor Selection , 2005 .

[49]  Mm Kumaraswamy,et al.  Consessionaire selection of BOT tunnel projects in Hong Kong , 2002 .

[50]  Farzad Khosrowshahi,et al.  Neural network model for contractors’ prequalification for local authority projects , 1999 .

[51]  Alfredo del Caño,et al.  Contractor selection: fuzzy-control approach , 2008 .

[52]  Ka Chi Lam,et al.  Using the principal component analysis method as a tool in contractor pre‐qualification , 2005 .

[53]  S. Thomas Ng,et al.  EQUAL: a case-based contractor prequalifier , 2001 .

[54]  Ekambaram Palaneeswaran,et al.  Concessionaire Selection for Build-Operate-Transfer Tunnel Projects in Hong Kong , 2002 .

[55]  Emilio Parrado-Hernández,et al.  Support vector machine interpretation , 2006, Neurocomputing.

[56]  S T Ng,et al.  Verification and Validation of Case-Based Prequalification System , 1998 .