Artificial neural network cost flow risk assessment model created by Henry A. Odeyinka,John Lowe and Ammar P. Kaka

Previous attempts have been made to model cash flow forecast at the tender stage using net cash flow, value flow and cost flow approaches. Despite these efforts, significant variations between the actual and modelled forecasts were still observable. The main cause identified is the issue of risk inherent in construction. Using the cost flow approach, a model is developed to assess the impacts of risk occurring during the construction stage on the initial forecast cost flow. A questionnaire survey and case study approach were employed. As a first step, a questionnaire survey was administered to UK construction contractors to determine the significant risk factors impacting on their cost flow forecast. Using mean ranking analysis, the survey yielded 11 significant risk factors. The second stage of data collection involves the collection of forecast and actual cost flow data from case study projects to establish their variations at predetermined time periods. Using the significant risk factors identified in the first phase, relevant construction professionals who worked on the case study projects were requested to score the extent of risk occurrence that resulted in the observed variations. A combination of these two sets of data was used to model the impact of risk on cost flow forecast using an artificial neural network back propagation algorithm. The model enables a contractor to predict the likely changes to a cost flow profile due to risks occurring in the construction stage.