Tamimi, S T (1989) An influence diagramming based risk analysis system. PhD thesis, University of Colorado at Boulder, USA.
Abstract
Risk has been recognized as one of the most serious problems confronting the construction industry. Quite often construction projects fail to achieve their goals. This failure may be partly attributed to omissions and inaccurate planning, however, a substantial portion is caused by the occurrence of unanticipated risks. From start to finish the construction process is complex and characterized by many uncertain variables. Risk analysis is very important, for knowledge of the risks a project may encounter enables management to employ risk mitigation methods to avoid, transfer, minimize or share the risk with other participants. Additionally, risk analysis is central to such decisions as project selection, contractor selection, and many others. Much has been written in Construction Engineering literature concerning the need for adequate risk analysis procedures and techniques. Mathematical, Monte Carlo and ad hoc procedures have all been suggested. Yet despite the widespread interest and the abundance of available techniques, the design and construction industry does not often execute reliable risk analyses for its projects due to a lack of appropriate analysis tools. The existing risk analysis techniques which are elementary enough to be accomplished by project personnel are not potent enough to model real world project complexities. Those techniques which are powerful to model the true complexity of project risk are not understandable by normal project personnel. This research sets forth a risk analysis procedure for construction projects. An Influence Diagramming based risk analysis System (IDS) has been developed to provide a better tool for evaluating and measuring risk. IDS employs powerful procedural and representational risk analysis schemes (Influence Diagramming Techniques, Monte Carlo Simulation, and Fuzzy Set Theory) to model the complexity of project risk. Yet, IDS is designed for easy use by embedding these powerful techniques in a user friendly environment. The technique proposed in this research provides better and easier to obtain estimates of project risks, and improves the overall modeling and effectiveness of risk and decision analysis as applied to construction industry. This is achieved by improving the construction of the risk model, by allowing project personnel to specify risky variables in linguistic terms, and by improving the accuracy of the marginal probability density function of the total project risk. Improved modeling of project risk will allow the construction industry to pursue projects with more efficiency. This research can be generalized to other aspects of engineering risk analysis. Technical performance, failure analysis, stability and reliability analyses are a few of the areas which may be amenable to the techniques of this research.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Diekmann, J |
| Uncontrolled Keywords: | Monte Carlo simulation; accuracy; complexity; construction engineering; contractor selection; decision analysis; failure; fuzzy set; personnel; probability; reliability; risk analysis; simulation |
| Index terms: | accuracy, stability, personnel, project complexity, complexity, risk analysis, failure analysis, risk mitigation, construction project, construction process, fuzzy set theory, fuzzy set, Monte Carlo simulation, decision analysis, reliability analysis, contractor selection, density, efficiency, construction industry, estimate, modelling, design and construction, effectiveness, construction engineering, project selection |
| Subjects: | engineering methods, performance management, management, diagnostic methods, professional development, decision analysis, systems engineering, industry analysis, value management, modelling and simulation, financial and cost management, tendering, structural engineering, financial risk, production management, organizational theory, building construction, environmental hazards, decision-making and optimization, reliability engineering, analytical methods, contractual arrangements |
| Topics: | Quality Management, Engineering Principles, Project Management, Risk Management, Sustainability, Procurement, Urban Studies, Digital Applications, Human Resources, Site Management, Cost Management, Information Management, Research Practice |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here