Decision support system (DSS) for construction project risk analysis and evaluation via evidential reasoning (ER)

This research explores the theory and practice of risk assessment and project evaluation and proposes novel alternatives. Reviewing literature revealed a continuous endeavour for better project risk modelling and analysis. A number of proposals for improving the prevailing Probability-Impact (P-I) risk model can be found in literature. Moreover, researchers have investigated the feasibility of different theories in analysing project risk. Furthermore, various decision support systems (DSSs) are available for aiding practitioners in risk assessment and decision making. Unfortunately, they are suffering from a low take-up. Instead, personal judgment and past experience are mainly used for analysing risk and making decisions. In this research, a new risk model is proposed through extending the P-I risk model to include a third dimension: probability of impact materialisation. Such an extension reflects the characteristics of a risk, its surrounding environment and the ability of mitigating its impact. A new assessment methodology is devised. Dempster-Shafer Theory of Evidence (DST) is researched and presented as a novel alternative to Probability Theory (PT) and Fuzzy Sets Theory (FST) which dominate the literature of project risks analysis. A DST-based assessment methodology was developed for structuring the personal experience and professional judgment of risk analysts and utilising them for risk analysis. Benefiting from the unique features of the Evidential Reasoning (ER) approach, the proposed methodology enables analysts to express their evaluations in distributed forms, so that they can provide degrees of belief in a predefined set of assessment grades based on available information. This is a very effective way for tackling the problem of lack of information which is an inherent feature of most projects during the tendering stage. It is the first time that such an approach is ever used for handling construction risk assessment. Monetary equivalent is used as a common scale for measuring risk impact on various project success objectives, and the evidential reasoning (ER) algorithm is used as an assessment aggregation tool instead of the simple averaging procedure which might not be appropriate in all situations. A DST-based project evaluation framework was developed using project risks and benefits as evaluation attributes. Monetary equivalent was used also as a common scale for measuring project risks and benefits and the ER algorithm as an aggregation tool. The viability of the proposed risk model, assessment methodology and project evaluation framework was investigated through conducting interviews with construction professionals and administering postal and online questionnaires. A decision support system (DSS) was devised to facilitate the proposed approaches and to perform the required calculations. The DSS was developed in light of the research findings regarding the reasons of low take-up of the existing tools. Four validation case studies were conducted. Senior managers in separate British construction companies tested the tool and found it useful, helpful and easy to use. It is concluded that the proposed risk model, risk assessment methodology and project evaluation framework could be viable alternatives to the existing ones. Professional experience was modelled and utilised systematically for risk and benefit analysis. This may help closing the gap between theory and practice of risk analysis and decision making in construction. The research findings recommend further exploration of the potential applications of DST and ER in construction management domain.