Profile
Simaan AbouRizk received his Ph.D. in 1990 from Purdue University for his work on input modeling for construction simulation. Later that year, he accepted a faculty position at the University of Alberta where he focused his research efforts on advancing simulation modeling and analysis of construction processes. He was appointed as an Alberta Construction Industry Professor in 1994, promoted to full professor in 1996, and held the position of Industrial Research Chair in Construction Engineering and Management from 1997 to 2016. Dr. AbouRizk is currently a Distinguished University Professor (2015) in the Department of Civil and Environmental Engineering at the University of Alberta and is a Tier 1 Canada Research Chair in Operations Simulation.
Dr. AbouRizk is a renowned expert in the development and application of computer simulation for construction planning, productivity improvement, constructability review, and risk analysis. His research focuses on developing innovative information technologies for modeling, analyzing, and optimizing operations in the construction and natural resource extraction industries. The high-quality and impact of his research work is evidenced by his extensive publication record of over 140 peer-reviewed journal publications and by his receipt of numerous awards and recognitions including the Peurifoy Construction Research Award (2008); a Steacie Memorial Fellowship (2001); the Thomas Fitch Rowland Prize (2003); a Killam Professorship (1998); a Walter Shanly Award (2001); the E. Whitman Wright Award (2002); and his induction into the Canadian Society for Civil Engineering (2010), the National Academy of Construction (2012), and the Royal Society of Canada (2013).
Dr. AbouRizk is also a dedicated student mentor, having supervised over 80 thesis-based Master’s and doctoral students. Many of his students have assumed prominent leadership roles throughout academic and industrial communities around the world. His leadership efforts have also been integral to the development of the Hole School of Construction Engineering at the University of Alberta into one of the most reputable and well-respected construction engineering and management programs in North America.
The success and distinctiveness of Dr. AbouRizk’s research and educational programs are a consequence of his development of strong industrial research partnerships focused on advancing research, student education, and overall practice. This collaborative approach has garnered widespread support from numerous funding agencies, policy makers, and industrial practitioners and remains a primary example of the achievements that can arise from effective partnerships with industry. FES Funded ProjectsOutputs
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Simulation-based approach for risk assessment in onshore wind farm construction projectsAbstract—Wind farm projects are one of the fastest growing sources for renewable energy in Canada. The construction phase of wind farm projects is associated with numerous risks, which may lead to unpredictable consequences during project execution. Uninformed decisions made in response to such risks can lead projects to deviate from original objectives, resulting in project time and cost overruns. Risk management has become a popular approach in the construction industry for improving decision making and reducing the adverse impacts of risks on project objectives; however, little research work has focused specifically on wind farm projects. Simulation-based approaches for risk assessment have been widely and successfully applied to model and quantify the risks associated with different types of construction projects. This research aims to develop a Monte Carlo-Critical Path Method simulation model to quantify the impact of risks on the project cost and time of wind farm construction projects. An in-house developed simulation engine, SimphonyProject.Net, is used to simulate the construction processes of wind farm projects along with the risks affecting the project cost and time. The result of this research will assist decision makers in the wind energy industry to effectively estimate the time and cost contingencies of onshore wind farm projects.T11-P01 University of Alberta | Publication | 2020-09-30 | | Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm ProjectsCurrently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical data—large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor’s impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS).T11-P01 University of Alberta | Publication | 2020-12-04 | | Domain-specific risk assessment using integrated simulation: A case study of an onshore wind projectAlthough many quantitative risk assessment models have been proposed in literature, their use in construction practice remain limited due to a lack of domain-specific models, tools, and application examples. This is especially true in wind farm construction, where the state-of-the-art integrated Monte Carlo simulation and critical path method (MCS–CPM) risk assessment approach has yet to be demonstrated. The present case study is the first reported application of the MCS–CPM method for risk assessment in wind farm construction and is the first case study to consider correlations between cost and schedule impacts of risk factors using copulas. MCS–CPM provided reasonable risk assessment results for a wind farm project, and its use in practice is recommended. To facilitate the practical application of quantitative risk assessment methods, this case study provides a much-needed analytical generalization of MCS–CPM, offering application examples, discussion of expected results, and recommendations to wind farm construction practitioners.T11-P01 University of Alberta | Publication | 2021-07-21 | | Simulation-Based Approach for Lookahead Scheduling of Onshore Wind Projects Subject to Weather RiskT11-P01 University of Alberta | Publication | 2021-09-01 | | Context-driven ontology-based risk identification for onshore wind farm projects: A domain-specific approachT11-P01 University of Alberta | Publication | 2023-04-10 | | Small Data Models of Machine LearningT11-P01 University of Alberta | Activity | 2023-11-28 | | A novel neural network-based fuzzy ranking method of decision-making in renewable energyT11-P01 University of Alberta | Activity | 2023-11-28 | |
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