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dc.contributor.authorAkinsola, Abiodun Olanrewaju
dc.date.accessioned2009-12-22T12:21:35Z
dc.date.available2009-12-22T12:21:35Z
dc.date.issued1997
dc.identifier.citationAkinsola, A.O. (1997). An intelligent model of variations' contingency on constructions projects. University of Wolverhampton, Wolverhampton
dc.identifier.urihttp://hdl.handle.net/2436/88494
dc.descriptionA thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy
dc.description.abstractThis research is an explanatory and quantitative investigation of variations on construction projects in UK. Variations on construction projects are inevitable often causing a major impact on the progress of the work resulting in disruption and delay, often resulting in claims and costly litigation. However at the pre-contract stage little attention is given to establishing the appropriate contingency allowance for variations, this is probably one of the most neglected areas of construction management. The premise of the research was that the magnitude of variations on construction projects is influenced by construction process factors. The research sought to identify those factors and to establish the relationship between the magnitude of variations and the factors. Using the established relationship, a quantitative model can be developed to predict the contingency allowance for variations. Through a questionnaire case study approach, several factors in the construction process were found to influence the magnitude of variations. The relationship between variations and those factors forra the basis for the developed artificial intelligent based neural network model developed in the research for predicting variations' contingency. The validation test of the model shows that the model can, with an acceptable degree of accuracy, predict and justify the total cost of variations on construction projects. The model, if used as a management tool to predict the contingencies, would not only assist in planning for variations but also facilitate negotiation of their cost and reduce the uncertainty of the project.
dc.formatapplication/pdf
dc.language.isoen
dc.publisherUniversity of Wolverhampton
dc.titleAn intelligent model of variations' contingency on constructions projects
dc.typeThesis or dissertation
dc.type.qualificationnamePhD
dc.type.qualificationlevelDoctoral
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
refterms.dateFOA2020-04-20T16:14:19Z
html.description.abstractThis research is an explanatory and quantitative investigation of variations on construction projects in UK. Variations on construction projects are inevitable often causing a major impact on the progress of the work resulting in disruption and delay, often resulting in claims and costly litigation. However at the pre-contract stage little attention is given to establishing the appropriate contingency allowance for variations, this is probably one of the most neglected areas of construction management. The premise of the research was that the magnitude of variations on construction projects is influenced by construction process factors. The research sought to identify those factors and to establish the relationship between the magnitude of variations and the factors. Using the established relationship, a quantitative model can be developed to predict the contingency allowance for variations. Through a questionnaire case study approach, several factors in the construction process were found to influence the magnitude of variations. The relationship between variations and those factors forra the basis for the developed artificial intelligent based neural network model developed in the research for predicting variations' contingency. The validation test of the model shows that the model can, with an acceptable degree of accuracy, predict and justify the total cost of variations on construction projects. The model, if used as a management tool to predict the contingencies, would not only assist in planning for variations but also facilitate negotiation of their cost and reduce the uncertainty of the project.


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