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Abdou, A, Lewis, J and Alzarooni, S (2004) Modelling risk for construction cost estimating and forecasting: a review. In: Khosrowshahi, F (Ed.), Proceedings 20th Annual ARCOM Conference, 1-3 September 2004, Edinburgh, UK. Association of Researchers in Construction Management, Vol. 1, 141–52.
- Type: Conference Proceedings
- Keywords: Artificial Intelligence; cost estimating; fuzzy; and risk management
- ISBN/ISSN: 0 9534161 9 4
- URL: http://www.arcom.ac.uk/-docs/proceedings/ar2004-0141-0152_Abdou_Lewis_and_Alzarooni.pdf
Construction cost estimating is considered one of the most essential tasks in the budget development of any project life cycle. However, it is carried out under conditions of uncertainty. Traditional cost estimating methods are unsatisfactory aids to decision making due to lack of their accuracy especially in feasibility or appraisal stages. Risk management is a form of decision-making within the project management process. Previous research indicated that the construction industry in particular has been slow to realize the potential benefits of risk management. With the introduction of microcomputers, the use of project management techniques has become economical, even for small construction projects. However, dealing with qualitative and judgement-based types of problems has been the subject of a lot of research and attempts that led to the Artificial Intelligence (AI) based models and applications. This research work aims at reviewing different approaches for modelling risk and uncertainty in construction cost estimating and forecasting. It starts with an overview of risk management concepts and fundamentals. Following that, it highlights the different analysis and modelling techniques within the risk management field. Finally, a number of previous research work and case-studies for modelling risk in construction cost estimating and forecasting are presented and reviewed.