Development of risk analysis models for decision-making in project management
Risks and uncertainties are inherent in construction projects and if neglected these risks often lead to project cost and time overruns. Traditional methods of forecasting risks rely upon intuition and 'feel' which has proved inadequate for the needs of investors in modern construction projects. To cope with these recognised risks, a risk management framework, which consists four components (risk identification, risk classification, risk analysis and risk response), has been developed. The present research focuses on financial risks in construction management, and in particular, the development of enhanced quantitative, probabilistic methods for risk analysis. A comprehensive review of the treatment of risk and uncertainty in the construction industry is undertaken. Background knowledge of probability theory and Monte Carlo simulation is reviewed, as is previous investigations into construction network analysis and project economics. A comparison of the Programme Evaluation and Review Technique (PERT) and the Monte Carlo Simulation (MCS) methods in construction networks risk analysis is carried out. Two example projects are analysed by both methods. When applying the MCS method, a sensitivity analysis is carried out by investigating the effect of different probability distributions (Normal, Log-Normal, Beta, Triangular and Uniform) for individual activity durations, the number of simulations used and the effect of the manner of how the mean and standard deviations are set for the different probability distributions. A new analytical method, the Modified Stochastic Assignment Model (MSAM), is proposed for the prediction of project duration. Five example projects are used to demonstrate the validity of the MSAM and to illustrate its application in construction project evaluations. The accuracy of the MSAM method is assessed by comparison to the MCS method. A comparison of the MSAM with other analytical methods commonly used in construction network analysis, such as PERT and the Probabilistic Network Evaluation Technique (PNET), is also presented. The First Order Second Moment (FOSM) method, a methodology previously used solely in system reliability analysis is applied to project economics. The definition of the FOSM method is given and detailed mathematical treatments of these methods are described. The methodology of using the FOSM in construction economics is explained and ten examples are analysed using both the FOSM method and the MCS to show the applicability and the degree of accuracy of these methods. The current research shows that the MSAM method yields the probability of project completion within a prescribed target time, or the required project time at a specific probability. The research also shows that it is possible to use the FOSM methods for risk analysis in decision-making in construction economics in such areas as selection of project, elemental cost analysis, cash flow streams and setting of plant hire rates. Both methods require computational time that is significantly less than an equivalent MCS.