| Graduate Student Internship (GSI) | Award | 2019-04-15 | Sathishkumar Nachimuthu |
| Sadler Graduate Scholarship in Mechanical Engineering | Award | 2019-09-01 | Yuejian Chen |
| Scholarship | Award | 2018-08-01 | Dongdong Wei |
| The Best Oral Presentation Award at the Graduate Research Symposium | Award | 2019-06-25 | Yuejian Chen |
| Top-Up Award in Mechanical Engineering | Award | 2017-10-18 | Yuejian Chen |
| Determining project contingency reserve using a fuzzy arithmetic-based risk analysis methodTraditional techniques for estimating contingency reserve fail to capture subjective uncertainties and expert knowledge, and they rely on historical data. This paper proposes a fuzzy risk analysis model (FRAM) that uses fuzzy arithmetic to analyze risk and opportunity events and determine construction project contingency reserve. The FRAM allows experts to use natural language to assess the probability and impact of risk and opportunity events by employing linguistic scales represented by fuzzy numbers, thus addressing the data reliance problem of probabilistic methods. It enables experts to customize linguistic scales and fuzzy numbers for different project types and stages. The FRAM also deals with the challenges associated with deterministic approaches by addressing measurement imprecision and the subjective uncertainty of experts’ opinions. Moreover, the FRAM allows analysts to estimate contingency at different levels of confidence. This paper also illustrates Fuzzy Risk Analyzer© (FRA©), software that implements the fuzzy arithmetic procedure of the FRAM. University of Alberta | Publication | 2020-01-07 | Seyed Hamed Fateminia, Fayek, A. |
| Consensus Building in Group Decision Making for the Risk Assessment of Wind Farm ProjectsA multi-criteria group decision making developed for the risk assessment of wind farm projects. University of Alberta | Publication | 2019-06-10 | Yajie Hao, Nebiyu Kedir, Nima Gerami Seresht, Pedrycz, W., Fayek, A. |
| Convolutional Neural Networks for Fault Diagnosis Using Rotating Speed Normalized Vibration University of Alberta | Publication | 2019-01-01 | Dongdong Wei, KeSheng Wang, Stephan Heyns, Zuo, M. |
| Developing a Risk Breakdown Matrix for the Construction of On-Shore Wind Farm ProjectsWind farm projects have recently gained popularity in many countries. However, since wind farms are a novel type of infrastructure for energy production for which limited historical data are available, numerous unique challenges are encountered during their construction. One of the main challenges involves risk management. Many researchers and practitioners have investigated on- and off-shore wind farm projects in terms of risk identification. However, they have mostly focused on off-shore wind farm projects; there is little research on risk identification for on-shore wind farm projects. To address this gap in the research, this paper develops a risk breakdown matrix (RBM) for the construction of on shore wind farm projects. Due to a lack of research on risk identification for on shore wind farm projects, in this paper, the case-based reasoning (CBR) technique is used to develop the RBM. First, the construction work packages (CWPs) of on shore wind farm projects are identified. Then, by comparing the CWPs of these projects to those from other types of construction projects, the work-package level risks that affect each CWP are identified based on the similarities between on shore wind farm projects and other types of construction projects. The RBM developed in this paper can be used for the risk identification and risk management of on-shore wind farm projects. The contributions of this paper are twofold: First, it introduces CBR as a risk identification technique for on shore wind farm or other similar construction projects, which is a topic that has not previously been comprehensively studied. Second, it identifies the work-package-level risks affecting these projects and maps each risk factor to the affected CWPs. University of Alberta | Publication | 2020-11-09 | Sahand Somi, Nima Gerami Seresht, Fayek, A. |
| Early gear tooth crack detection based on singular value decomposition University of Alberta | Publication | 2019-06-17 | Yuejian Chen, Zuo, M. |
| Evaluating Risk Response Strategies on Construction Projects Using a Fuzzy Rule-Based SystemThe development and implementation of risk response strategies contributes to effective risk management processes in construction organizations. Risk response strategies need to be developed and implemented as follows: first, all possible risk responses for each given risk event of a project are identified; next, each risk response is evaluated to determine its effectiveness; then, for each risk event of the project, the optimal risk response is identified and implemented; and finally, the risk events and responses are consistently monitored. The existing literature confirms that there is a lack of research on evaluation criteria for risk responses, making it difficult to determine their effectiveness. This paper presents research that fills this gap by developing a way to evaluate the effectiveness of risk response strategies using a fuzzy rule based system (FRBS) that consists of three inputs and one output. The inputs of the FRBS are the affordability and the achievability of risk responses and the controllability of risk events; the output is the effectiveness of the risk response. The application of fuzzy ranking methods instead of crisp ranking methods allows the model to mimic three human attitudes towards risk: risk averse, neutral, and risk taking. The proposed model lays the foundation for an automated evaluation of risk response strategies and provides a decision support tool for experts in the field. University of Alberta | Publication | 2019-05-21 | Seyed Hamed Fateminia, Nima Gerami Seresht, Fayek, A. |
| Health index development for a planetary gearboxConference paper University of Alberta | Publication | 2020-07-14 | Weixuan Tang, Zuo, M. |
| Modelling factors affecting operation and maintenance costs of offshore wind farmsSathishkumar Nachimuthu, Ming J Zuo and Yi Ding. “Modelling factors affecting operation and maintenance costs of offshore wind farms”. Forthcoming in: Proceedings of the 2018 IISE Annual Conference. Accepted February 22, 2018. University of Alberta | Publication | 2018-02-22 | Sathishkumar Nachimuthu, Zuo, M., "Yi Ding" |
| 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. | Publication | 2020-09-30 | Emad Mohamed, Nima Gerami Seresht, Simaan AbouRizk |
| Tooth crack severity assessment in the early stage of crack propagation using gearbox dynamic model University of Alberta | Publication | 2020-03-20 | Xingkai Yang, Zuo, M., Tian, Z. |
| A novel neural network-based fuzzy ranking method of decision-making in renewable energy University of Alberta | Activity | 2023-11-28 | Ye Cui, Hanyu E, Pedrycz, W., Fayek, A., Simaan AbouRizk |
| Decision Support Systems for Improving the Construction and Maintenance of Renewable Energy ProjectsRenewable energy projects have recently gained popularity due to their low adverse environmental impacts. While the improvement of the construction and maintenance of such projects requires that project and operation managers make the right decisions in a timely fashion, the complexity and novelty of these projects leads to numerous challenges related to decision-making. Renewable energy projects involve numerous uncertain factors; these projects often require managers to coordinate many complex and dynamic processes for decisions-making; and managers must consider sometimes contradictory criteria and/or objectives for decision-making. In recent years, the application of advanced modeling and computational techniques has emerged in different engineering disciplines to develop decision support systems for supporting practitioners in dealing with such challenges. This special session focuses on the development and application of decision support systems for improving the construction and maintenance of renewable energy projects. University of Alberta | Activity | 2020-08-20 | Nima Gerami Seresht, Fayek, A. |
| Machine-Learning Methods for PHM and ReliabilityKeynote speech in the 4th Workshop & Symposium on Safety and Integrity Management of Operations in Harsh Environments, July 15-17, 2019, ST. JOHN’S, NEWFOUNDLAND AND LABRADOR, CANADA
University of Alberta | Activity | 2019-07-15 | Zuo, M. |
| Small Data Models of Machine Learning University of Alberta | Activity | 2023-11-28 | Hanyu E, Pedrycz, W., Ye Cui, Fayek, A., Simaan AbouRizk |
| Decision-Support System for Construction Risk Management in Onshore Wind ProjectsWind 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 new Canadian wind farms and supporting infrastructure. As with
other types of construction, the execution phase of wind farm projects is associated with
unanticipated risks (e.g., weather-related challenges and unknown stakeholder interactions),
which create uncertainty during project execution. Uninformed decisions made in response to
such risks can lead projects to deviate from original objectives, resulting in time and cost
overruns, safety issues, and quality deficiencies.
Risk management has become a popular approach in the construction industry to reduce
project uncertainties and risks for improved decision-making. However, previous research
studies do not address the distinctive characteristics, unique risks, and data limitations associated
with wind farm construction, restricting the ability of practitioners to adequately assess the risks
affecting the construction phase of onshore wind 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 and methods to deal with data
insufficiency, hinder the use of advanced quantitative techniques, such as simulation, to assess
risks. Finally, distinctive characteristics, including location-bias to high wind speeds, impose
unique challenges during the execution of these projects that are not addressed by existing
methods.
This thesis describes the development of a novel decision-support system designed to
iii address current limitations by facilitating and enhancing the identification, analysis, and
assessment of risk factors affecting the construction phase of onshore wind farm projects. The
decision-support system was developed by adopting existing analytical methods and simulation.
First, critical generic risk factors affecting onshore wind projects in Canada were identified.
Then, a context-driven approach for identifying project-specific risk factors was developed.
Once risk factors were identified, a method to enhance the input modelling of these risk factors
for quantitative risk assessment was proposed. Next, a domain-specific risk assessment method
was proposed for onshore wind projects. Finally, since adverse weather was identified as the
most critical risk factor affecting the construction phase of onshore wind projects in Canada, a
simulation-based approach was proposed to more effectively model weather risk.
This research contributes to the state-of-the-art by (1) providing a systematic and
thorough analysis—focused exclusively on the construction phase—of the risk factors affecting
onshore wind projects, (2) identifying the most critical risk factors in onshore wind projects in
Canada using a hybrid multi-criteria approach; (3) developing a context-driven approach that
considers the specific characteristics of a project to facilitate the identification of project risks;
(4) 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 improved Monte Carlo simulation; and (6) developing a simulationbased approach that allows decision-makers to dynamically and rapidly assess the impact of upcoming weather conditions on project performance during lookahead scheduling. | Publication | 2021-09-01 | Emad Mohamed |
| Fuzzy Modeling with Population-based Optimization: Design and Analysis | Publication | 2021-01-01 | Ali Safari Mamaghani |
| Rule-Based Models with Information Granules: Enhancements and Applications | Publication | 2022-01-01 | Ye Cui |
| Vibration-based fault detection and severity assessment for fixed-axis gearboxes | Publication | 2020-01-01 | Yuejian Chen |
| Decision Support Systems for Improving the Construction and Maintenance of Renewable Energy ProjectsRenewable energy projects have recently gained popularity due to their low adverse environmental impacts. While the improvement of the construction and maintenance of such projects requires that project and operation managers make the right decisions in a timely fashion, the complexity and novelty of these projects leads to numerous challenges related to decision-making. Renewable energy projects involve numerous uncertain factors; these projects often require managers to coordinate many complex and dynamic processes for decision-making; and managers must consider sometimes contradictory criteria and/or objectives for decision-making. In recent years, the application of advanced modeling and computational techniques has emerged in different engineering disciplines to develop decision support systems for supporting practitioners in dealing with such challenges. This Special Issue focuses on the development and application of decision support systems for improving the construction and maintenance of renewable energy projects. It also includes extensions of selected papers from the 9th Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modelling (APARM 2020). University of Alberta | Publication | 2020-11-20 | Fayek, A., Nima Gerami Seresht |
| Impact of Prognostics and Health Management in Systems Reliability and Maintenance Planning, Special Issue of Reliability Engineering and System SafetySpecial Issue of Reliability Engineering and System Safety, Editorial 1 page. University of Alberta | Publication | 2019-04-01 | Joo Ho Choi, Zuo, M. |
| Consensus Building and Optimization in Group Decision-Making for the Risk Assessment of Wind Farm ProjectInfrastructure projects for harnessing renewable energy (e.g., Wind Farm Project) have recently gained popularity because of their low adverse impact on the environment. However, the risk assessment of the Wind Farm Project involves numerous challenges since historical data for these projects are either scarce or of low quality. Therefore, risk assessments for renewable energy infrastructure projects must heavily rely on the expert assessment of different risk factors associated with achieving project objectives in terms of cost, time, quality, and safety. Accordingly, the risk assessment of the Wind Farm Project needs to be tackled as a multi-criteria group decision-making problem, which necessitates building consensus between individual decision-makers who each supply their preference indices for decision alternatives (i.e., risk factors). In group decision-making problems, consensus must be built between individual decision-makers whom each supply their preference indices for decision alternatives. In this study, a new multiple criteria group decision-making technique is introduced for the risk assessment of the Wind Farm Project by building consensus among decision-makers—who have determined their decision alternative preferences representing the aggregated preference indices of decision alternatives as to the principle of justifiable granularity type 2 fuzzy numbers, thereby producing an interval-valued fuzzy set that represents the aggregated value of the reference indices assigned to decision alternatives by decision-makers. The preference indices obtained from each expert and linguistic conflicts are realized and clarified through the analytic hierarchy process. Moreover, the introduced multiple criteria group decision-making technique is used to assess risk for Wind Farm Project. Then, the construction work packages are ranked based on how much they contribute to the overall risk or uncertainty involved in achieving the project objectives for time, cost, quality, and safety. Due to insufficient real experts` knowledge, data is much more valuable, and the principle of justifiable granularity selects and elevated the consensus of decision-making problems, which excludes the extreme preference of experts. Partial preferences of experts can be elevated higher through the exploration and elevation of the multiple criteria group decision-making consensus, which relies on the constraints of randomization and particle swarm optimization, the elevation of information granule, and its corresponding granularity includes more experts` preferences without losing too much preciseness. The main objectives of the thesis are aiming at the collection of consensus through the principle of justifiable granularity and the exploration and elevation of consensus. | Publication | 2021-06-30 | Yajie Hao |
| Decision Models for Operation and Maintenance of Offshore Wind Farms Considering Uncertainties | Publication | 2020-01-01 | Sathishkumar Nachimuthu |
| Developing Risk Breakdown Matrix for Onshore Wind Farm Projects Using Fuzzy Case-Based ReasoningMany countries have invested in the development of renewable energy projects, particularly onshore and offshore wind farm projects because of their low adverse environmental impact on the environment. However, onshore and offshore wind farm projects are novel types of projects in most countries and risk identification of them are hindered by the scarcity of historical data, high cost for acquiring expert knowledge, and/or the limited research available on this topic. Previous research on risk identification of onshore and offshore wind farm projects are mainly focused on offshore wind farm projects because of their high-risk marine environment. The few studies conducted on risk identification of onshore wind farm projects focus mainly on project-level risks; work–package–level risks are not investigated in order to develop Risk Breakdown Matrix (RBM). Therefore, there is a gap in the research on the risk identification of onshore wind farm projects to develop RBM. Existing risk identification techniques mostly rely on expert knowledge and available research on project type. However, implementing those techniques is not appropriate for onshore wind farm projects because there is limited research and historical data available on this topic. Acquiring expert knowledge is also challenging because of the high cost of it. In addition, successful expert interviews highly depend on expert abilities, attitudes, and thoroughness which is a limitation of this technique. CBR techniques are well-known for their application to solve a new problem based on the similarity between different types of projects. However, there are a few studies on CBR techniques in hazard and risk identification, and those techniques did not consider subjective information in their techniques. Therefore, there is a gap in the research on developing the fuzzy-case based reasoning (FCBR) technique for risk identification of the novel type of project which captures the subjectivity of construction project information. To address these limitations, the main contributions of this research are twofold: (1) develop a risk breakdown matrix (RBM) for onshore wind farm projects by mapping each risk to those construction work packages affected by the risk. (2) proposes a new risk identification framework suitable for novel types of construction projects that are not comprehensively studied in the literature and have limited historical data. | Publication | 2021-06-30 | Sahand Somi |
| Health Index Development for Planetary GearboxesMaster's Thesis | Publication | 2020-01-15 | Weixuan Tang |
| Selection of Wind Direction Segment Size in Wind Farm Layout Optimization | Publication | 2021-01-01 | Siyun Ge |
| Resilience Optimization of Power Grid under Extreme Weather ConditionsPoster Presentation University of Alberta | Activity | 2019-03-14 | Sathishkumar Nachimuthu, Zuo, M. |
| A granular multicriteria group decision making for renewable energy planning problems University of Alberta | Publication | 2022-09-16 | Ye Cui, Hanyu E, Pedrycz, W., Fayek, A. |
| An Adaptive Hybrid Model for Determining Subjective Causal Relationships in Fuzzy System Dynamics Models for Analyzing Construction RisksModeling risk management systems in construction projects is a complex process because of various internal and external factors and their interrelationships. Fuzzy system dynamics (FSD) have been commonly employed to model and analyze construction risk management systems. To run FSD simulation models, all hard (objective) and soft (subjective) causal relationships between variables must be quantified. However, a research gap exists regarding structured methods for constructing soft causal relationships in FSD models. This paper proposes an adaptive hybrid model consisting of fuzzy analytical hierarchy process, weighted principle of justifiable granularity, and fuzzy aggregation operators to determine crisp values of causality degree for soft (subjective) causal relationships in FSD modeling of construction risk analysis. The proposed model is implemented in analyzing construction risks of a windfarm project to illustrate its applicability. The proposed model generates two results: (1) optimized membership functions for linguistic terms representing the causality degree of soft relationships and (2) the crisp value for the causality degree of soft relationships. The contribution of study is to propose a structured model to improve efficiency and effectiveness of developing FSD quantitative modeling by addressing soft causal relationships between different variables in FSD models and considering multiple risk expertise of heterogeneous experts in construction risk assessment. University of Alberta | Publication | 2021-09-03 | Seyed Hamed Fateminia, Fayek, A. |
| An improved singular value decomposition-based method for gear tooth crack detection and severity assessment University of Alberta | Publication | 2020-03-03 | Yuejian Chen, Xihui Liang, Zuo, M. |
| An Interval Type-2 Fuzzy Risk Analysis Model (IT2FRAM) for Determining Construction Project Contingency ReserveDetermining contingency reserve is critical to project risk management. Classic methods of determining contingency reserve significantly rely on historical data and fail to effectively incorporate certain types of uncertainties such as vagueness, ambiguity, and subjectivity. In this paper, an interval type-2 fuzzy risk analysis model (IT2FRAM) is introduced in order to determine the contingency reserve. In IT2FRAM, the membership functions for the linguistic terms used to describe the probability, impact of risk and the opportunity events are developed, optimized, and aggregated using interval type-2 fuzzy sets and the principle of justifiable granularity. IT2FRAM is an extension of a fuzzy arithmetic-based risk analysis method which considers such uncertainties and addresses the limitations of probabilistic and deterministic techniques of contingency determination methods. The contribution of IT2FRAM is that it considers the opinions of several subject matter experts to develop the membership functions of linguistic terms. Moreover, the effect of outlier opinions in developing the membership functions of linguistic terms are reduced. IT2FRAM also enables the aggregation of non-linear membership functions into trapezoidal membership functions. A hypothetical case study is presented in order to illustrate the application of IT2FRAM in Fuzzy Risk Analyzer© (FRA©), a risk analysis software. University of Alberta | Publication | 2020-07-01 | Seyed Hamed Fateminia, Fayek, A. |
| Context-driven ontology-based risk identification for onshore wind farm projects: A domain-specific approach | Publication | 2023-04-10 | Emad Mohamed, Nima Gerami Seresht, Simaan AbouRizk |
| Decision-making Model for Corrective Maintenance of Offshore Wind Turbine Considering Uncertainties
A decision support system for the maintenance of off-shore wind farm turbines. University of Alberta | Publication | 2019-04-12 | Sathishkumar Nachimuthu, Zuo, M., "Yi Ding" |
| 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. | Publication | 2021-07-21 | Emad Mohamed, Nima Gerami Seresht, Simaan AbouRizk |
| Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based ReasoningAbstract— Construction projects are highly risk-prone due to internal factors (i.e., organizational, contractual, project, etc.) and external factors (i.e., environmental, economic, political, etc.). Construction risks, therefore, may have a direct or indirect impact on project objectives, namely, cost, time, safety and quality. Accordingly, identifying construction risks is a crucial task in order to fulfill the project objectives. Although many tools and techniques are proposed for risk identification including literature review, questionnaire survey and expert interview, the majority of which highly rely on expert knowledge or the prior knowledge of the project acquired through the literature review or past projects. Therefore, the application of these techniques for the risk identification of renewable energy projects (e.g., wind farm projects and solar projects) is challenging, due to the novelty of these projects and limited availability of historical data or literature. This paper aims to address this challenge by introducing a new risk identification framework geared toward the risk identification of renewable energy projects by combining the case-based reasoning technique and fuzzy logic. Case-based reasoning technique helps to solve problems related to novel cases (e.g., renewable energy projects) based on their similarities to the existing well-studied cases (e.g., conventional energy projects); and fuzzy logic tends to capture the subjective uncertainty exist in construction-related problems. The applicability of the proposed framework is tested through the case study of on-shore wind farm projects. The contributions of this paper are: introducing a novel framework for risk identification of renewable energy projects; and identifying the risks associated with the construction of on-shore wind farm projects in work-packaged level. The results of this study can help construction researchers and practitioners for the risk management of renewable energy projects during the construction phase. University of Alberta | Publication | 2020-06-27 | Sahand Somi, Nima Gerami Seresht, Fayek, A. |
| From Fuzzy Rule-Based Models to Granular Models University of Alberta | Publication | 2024-10-30 | Ye Cui, Hanyu E, Pedrycz, W., Zhiwu Li, Xianmin Wang |
| 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). | Publication | 2020-12-04 | Emad Mohamed, Simaan AbouRizk |
| Hybrid fuzzy arithmetic-based model for determining contingency reserve University of Alberta | Publication | 2023-04-19 | Seyed Hamed Fateminia, Fayek, A. |
| Risk assessment for onshore wind projects in Canada | Publication | 2024-03-01 | Emad Mohamed, Nima Gerami Seresht, "Parinaz Jafari", Simaan AbouRizk |
| Simulation-Based Approach for Lookahead Scheduling of Onshore Wind Projects Subject to Weather Risk | Publication | 2021-09-01 | Emad Mohamed, Parinaz Jafari, Adam Chehouri, Simaan AbouRizk |
| Strategies for designing machine learning models in renewable energy with insufficient data University of Alberta | Publication | 2024-10-15 | Hanyu E, Ye Cui, Pedrycz, W., Fayek, A., Simaan AbouRizk |
| Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation University of Alberta | Publication | 2023-01-01 | Nebiyu Kedir, Phuong H D Nguyen, Citlaly Pérez, Pedro Ponce, Fayek, A. |
| Work-Packaged Level Risk Identification of On-shore Wind Farm Project: Applying Fuzzy CBR in Construction Risk IdentificationWind farm projects have recently gained popularity in many countries. However, wind farm projects are encountered with numerous unique challenges during their construction, since they are a novel type of infrastructure for energy production. One of such challenges is the risk management of wind farm projects during their construction phase; which occurs due to the novelty of these projects and consequently the limited historical data availability for these projects. Therefore, many researchers and practitioners have investigated wind farm projects to identify the risks that affect this type of construction projects. However, previous research is mostly focused on off-shore wind farm projects; and there is very few research conducted on the risk identification of on-shore wind farm projects. In this paper, this research gap is addressed by identifying the risks associated with the construction of on shore wind farm projects at the work package level using the fuzzy case-based reasoning (FCBR) technique. Case based-reasoning (CBR) is a technique for solving problems related to an unknown phenomenon based on the knowledge available about similar well-known phenomena relying on the idea that the same problems have the same solutions. Moreover, FCBR is a hybrid technique that combines CBR with fuzzy sets theory to process subjectivity uncertainty in data related to the previous cases and/or the similarity between the different cases. The contributions of this paper are two folds: First, it introduces FCBR technique as a new risk identification technique for those construction projects, which have not been comprehensively studied in the literature such as on shore wind farm projects. Second, it identifies the work-package-level risks that affect the construction of wind farm projects and maps each risk factor to those construction work packages (CWPs) that are affected by the risk factor. The results of this study can help construction researchers and practitioners for the risk management of wind farm projects during the construction phase. University of Alberta | Publication | 2020-02-19 | Sahand Somi, Nima Gerami Seresht, Fayek, A. |
