Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311595
Title: Knowledge based decision support system for the selection and appointment of sub-contractors for building refurbishment contracts
Author: Okoroh, Michael I.
ISNI:       0000 0001 3455 961X
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 1992
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Abstract:
This thesis describes the results of research analysing the sub-contractor's risk elements in refurbishment projects. One of the main characteristics of refurbishment projects is that work is usually in small packages and scattered throughout the building making it unprofitable for one contractor to undertake. It is argued that the selection and appointment of the most suitable sub-contractors is very important in refurbishment projects as all other control measures have little effect once a totally unsuitable subcontractor has been appointed. The research methodology involved the extensive collaboration of a retired chief estimator with over thirty years experience in one of Britain's biggest construction firms with extensive knowledge in the management of sub-contractors and several other refurbishment contractors' senior management staff who were involved in choosing subcontractors for their contracts. Knowledge acquisition and representation and the evaluation of expert system shells are extensively reviewed. One of the important features of knowledge based systems is its ability to handle uncertain knowledge. Fuzzy set theory is shown to have certain advantages over other methods of dealing with uncertainty and has been employed in developing this knowledge based system. The research began with an evaluation of sub-contractors' selection and appointment as it is currently performed by refurbishment contractors. This exercise consisted of a wide range of criteria of which information is both qualitative and subjective in an unstructured intuitive manner with considerable reliance on the judgement of the evaluee. Thus, the research focused on a more formalised approach to the subcontractor's appointment. An adaptation of the Repertory Grid knowledge elicitation technique and subsequent grid analysis provides a methodology for organising logically related propositions into a hierarchical structure. A prototype knowledge based decision support system SSARC, for the selection and appointment of the most suitable sub-contractors for refurbishment projects, has been developed. This system represents a contribution in this area of research into refurbishment contracts which has been largely neglected to date.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.311595  DOI: Not available
Keywords: Contracting Artificial intelligence Building Management
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