Risk is endemic in construction projects and previous studies suggest that variability between contract sum and final account was as a result of risk occurring during the project‘s life. This study uses the risk theory to uncover the significant risk variables thought to impact the construction phase with attendant impact on the out turn cost of construction projects. The significant risk variables were then used to develop models for assessing risk impacts on the variability between contract sum and final account. A two-stage approach was adopted in data collection. The first was an online questionnaire survey of risk factors thought to impact the variability between contract sum and final account. A ranking of the mean score of the survey responses enabled the significant risk factors to be determined. The second stage of the data collection involved collection of data regarding contract sum and final account from recently completed projects. Quantity Surveyors whoworked on the projects were then requested to score the extent of occurrence of the dentified significant risk factors on a Likert-type scale. The pair of data set obtained was then used to model risk impacts on the variability between contract sum and final account using artificial neural network modelling method. The result obtained was promising and the developed models could help the construction contractor to predict the likely impacts of risk occurring at project execution phase on out turn construction cost.Keywords: artificial neural network, contract sum, final account, risk variables, risk modelling
|Title of host publication||COBRA 2011: Proceedings of the RICS Foundation Construction and Property Research Conference|
|Place of Publication||Salford|
|Publisher||Royal Institution of Chartered Surveyors|
|ISBN (Print)||ISBN: 978-1-907842-19-1|
|Publication status||Published - 12 Sep 2011|
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- artificial neural network
- contract sum
- final account
- risk variables
- risk modelling