Abstract:
In this thesis, a methodology for construction projects risk assessment under epistemic uncertainty (i.e., uncertainty arising from lack of data/knowledge) has been proposed. In practice, as the sufficient data from historical sources for probabilistic analysis is quite difficult to obtain, qualitative risk assessment methodologies based on expert’s judgments (i.e., using linguistic terms) are commonly used in construction industry. However, these insufficient probabilistic data combining with experts’ judgments can be used in the risks evaluation process to reduce uncertainties and biasness. Since the assessment of risk is basically a measure of uncertainties, fuzzy reasoning technique can bean effective tool to deal with these uncertainties and capture the vagueness in the linguistic variables. Most of the existingrisk analysis models have evaluatedrisks based on two factors: risk likelihood and risk severity. In all these methodologies developed so far, it has been assumed that the degrees of uncertainties (level of uncertainties) involved in individual risk event are equal. However, in practice, the degree of uncertainties that involved in each risk event may vary due to the variation in the availability or quality of data obtained from multiple sources (e.g., from experts’ opinions and past data from similar projects). Therefore, evaluation of risks considering the degree of uncertainty involved in individual risk events may assist project manager in setting-up response strategies to mitigate threat to the project objectives. This thesis proposes a risk assessment methodology using triangular fuzzy numbering system to compute risk value by combining expert’s opinion and insufficient historical data. A modified form of general ramp type fuzzy membership function for quantification of uncertainty range of each risk event and an extended VIKORmethod for risks ranking with these uncertainty ranges have been proposed. The most notable difference with other fuzzy risk assessment methods is the use of algorithm to handle the uncertainties involved in individual risk event. An illustrative example on risk assessment of a building construction project is used to demonstrate the proposed methodology.
