Profile
Keywords: Construction, non-electrical infrastructure, Decision support system, Future energy, Wind farm
Dr. Aminah Robinson Fayek is a tenured professor with the Department of Civil and Environmental Engineering at the University of Alberta and the Director of the Construction Innovation Centre. She holds the Tier 1 Canada Research Chair in Fuzzy Hybrid Decision Support Systems for Construction. Dr. Robinson Fayek holds the NSERC Industrial Research Chair (IRC) in Strategic Construction Modeling and Delivery, bringing together construction industry owners, contractors, and labour groups across Canada to develop comprehensive research-based solutions to key industry problems. She and also holds the prestigious Ledcor Professorship in Construction Engineering. Dr. Robinson Fayek’s core interests involve combining fuzzy logic with other modeling techniques, such as artificial neural networks, genetic algorithms, and simulation, in order to develop advanced decision support tools and approaches. Through a number of academic and industry-based collaborations, Dr. Robinson Fayek and her research team are currently investigating areas including construction project productivity, labour motivation and behaviour; organizational competencies and performance; risk analysis and mitigation; and assessment of the outcomes and impacts of R&D programs.
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Evaluating Risk Response Strategies on Construction Projects Using a Fuzzy Rule-Based System The 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.T11-P01 University of Alberta Publication 2019-05-21 Seyed Hamed Fateminia, Nima Gerami Seresht,
Fayek, A. T11-P01 Determining project contingency reserve using a fuzzy arithmetic-based risk analysis method Traditional 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.T11-P01 University of Alberta Publication 2020-01-07 T11-P01 An Interval Type-2 Fuzzy Risk Analysis Model (IT2FRAM) for Determining Construction Project Contingency Reserve Determining 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.T11-P01 University of Alberta Publication 2020-07-01 T11-P01 An Adaptive Hybrid Model for Determining Subjective Causal Relationships in Fuzzy System Dynamics Models for Analyzing Construction Risks Modeling 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.T11-P01 University of Alberta Publication 2021-09-03 T11-P01 Hybrid fuzzy arithmetic-based model for determining contingency reserve T11-P01 University of Alberta Publication 2023-04-19 T11-P01 Consensus Building in Group Decision Making for the Risk Assessment of Wind Farm Projects A multi-criteria group decision making developed for the risk assessment of wind farm projects.T11-P01 University of Alberta Publication 2019-06-10 T11-P01 Developing a Risk Breakdown Matrix for the Construction of On-Shore Wind Farm Projects Wind 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.T11-P01 University of Alberta Publication 2020-11-09 T11-P01 Work-Packaged Level Risk Identification of On-shore Wind Farm Project: Applying Fuzzy CBR in Construction Risk Identification Wind 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. T11-P01 University of Alberta Publication 2020-02-19 T11-P01 Decision Support Systems for Improving the Construction and Maintenance of Renewable Energy Projects Renewable 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.T11-P01 University of Alberta Activity 2020-08-20 T11-P01 Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning Abstract— 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.T11-P01 University of Alberta Publication 2020-06-27 T11-P01 Decision Support Systems for Improving the Construction and Maintenance of Renewable Energy Projects Renewable 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).T11-P01 University of Alberta Publication 2020-11-20 T11-P01 A granular multicriteria group decision making for renewable energy planning problems T11-P01 University of Alberta Publication 2022-09-16 T11-P01 Small Data Models of Machine Learning T11-P01 University of Alberta Activity 2023-11-28 T11-P01 A novel neural network-based fuzzy ranking method of decision-making in renewable energy T11-P01 University of Alberta Activity 2023-11-28 T11-P01 Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation T11-P01 University of Alberta Publication 2023-01-01 Nebiyu Kedir, Phuong H D Nguyen, Citlaly Pérez, Pedro Ponce,
Fayek, A. T11-P01 Strategies for designing machine learning models in renewable energy with insufficient data T11-P01 University of Alberta Publication 2024-10-15 T11-P01
