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Title: An intelligent model of variations' contingency on constructions projects
Author: Akinsola, Abiodun Olanrewaju
ISNI:       0000 0001 3403 8494
Awarding Body: University of Wolverhampton
Current Institution: University of Wolverhampton
Date of Award: 1997
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This 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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Construction industry; Project management