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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
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Items in this Collection
- 2Fuzzy logic
- 1 Onshore wind projects
- 1Construction Management
- 1Construction management
- 1Discrete Event Simulation
- 1Input modeling
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A Hybrid Fuzzy Discrete Event Simulation Framework for Analysis of Stochastic and Subjective Uncertainties in Construction Projects
DownloadSpring 2015
. Stochastic uncertainty is a system property and represents the uncertainty associated with variation of a variable. Stochastic uncertainty can be represented by a probability distribution. On the other hand, subjective uncertainty represents the lack of knowledge of the system modeller regarding the actual
DES is only able to consider stochastic uncertainty using probability distributions; and cannot handle subjective uncertainty. Fuzzy set theory provides a methodology for mathematical modelling of subjective uncertainty. Recently, fuzzy discrete event simulation (FDES) has been proposed for
considering subjective uncertainty in construction simulation models. However, the fundamental differences between fuzzy numbers and probability distributions introduce new challenges to FDES frameworks. Furthermore, subjective and stochastic uncertainties may simultaneously exist in a simulation model
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Fall 2021
) developing an integrated simulation approach for assessing risks in onshore wind projects that considers both the cost and time impact of risks; (5) proposing a method for deriving probability distributions of a risk factor’s impact using fuzzy logic and multivariate analysis to enhance input modelling for
Wind energy is emerging as a primary source of renewable energy in Canada, attracting over $23 billion in investment. Steadily increasing, a total capacity of 31,640 MW of wind energy must be installed by 2040 to meet the requirements of the Paris Agreement on Climate, requiring the construction of
projects—particularly in the Canadian wind energy sector. In particular, the identification of project-specific (i.e., contextual) risk factors still relies heavily on traditional risk identification techniques that are demanding in terms of time and effort. This, together with a lack of historical data