Doctoral Dissertations

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Recent Submissions

  • Publication
    Detection of Traffic Incidents Using Machine Learning Techniques
    (2023-04) ElSahly, Osama Mohamed; Abdelfatah, Akmal
  • Publication
    Decision Support Model for Selecting the Project Delivery Method for Sustainable Construction Projects
    (2023-03) Ahmed, Salma Nasser Korany Megahed; El-Sayegh, Sameh
    As the demand for sustainable construction increases, the need to update project management practices in order to satisfy the objectives of sustainability becomes more significant. Project delivery method is a crucial decision in project management that impacts the success of construction projects. The extensive literature review conducted identified several gaps such as the lack of a comprehensive criteria list to select project delivery methods in sustainable construction projects and a lack of selection model that comprises that comprehensive criteria list. This research bridges the gaps in literature by developing a comprehensive decision support model that will provide decision-makers with a justified rationale for choosing the most appropriate delivery method for their sustainable construction projects. In order to achieve this, the underlying challenges of sustainable construction delivery were first categorized using factor analysed. These were then used to derive relevant sustainability -specific criteria which consisted of five groups: level of integration, green liability, green team, green criteria, and technology and innovation. Structural Equation Modelling was then used to predict the significance of these sustainability-specific selection criteria to the achievement of project success criteria such as efficiency, impact on client, team effectiveness and sustainability. Moreover, Analytical Hierarchy Process was used to calculate the relative weights of the traditional and sustainability-specific criteria as well as the effectiveness values of the three most common delivery methods in achieving the comprehensive criteria. The outputs of all these statistical procedures were then used to develop two decision support models that incorporate a comprehensive selection criteria list of both traditional and sustainability-specific criteria and the three most common delivery methods. Moreover, a customizable prototype software of the decision model was developed where the unfamiliar operations required in the adopted technique would be transparent to the end-users. A case-study that was administered in the end clearly indicates that DBB is outmatched by the unique requirements of sustainable construction. While, CMR and DB are both potentially competitive candidates that can enhance the success rate of sustainable construction projects.
  • Publication
    Maintenance and Sustainability: Decision Support Tools and Role of Maintenance Digital Transformation
    (2023-03) Saihi, Afef; Ben-Daya, Mohamed; As'ad, Rami
    Maintenance is a crucial activity conducted throughout the use phase of the engineered object life cycle, which has tremendous impact on all three pillars of sustainability. While economic and technical impacts of maintenance activities are well-studied and vastly addressed in the literature, the associated impacts on environmental and social pillars are not sufficiently tackled. As such, maintenance-related decisions, in all its facets, have been mostly driven by these economic and technical measures. Full integration of sustainability considerations into maintenance practices requires close monitoring and assessment of maintenance impact on the triple-bottom-line through relevant key performance indicators (KPIs) and appropriate decision-support tools that take into account pertinent sustainability issues. To that end, there is a need for further research concerning the integration of all sustainability aspects into maintenance practices along various directions, including (1) developing adequate and relevant KPIs, (2) proposing models for accurate maintenance sustainability performance evaluation, (3) exploring the role of technology in enabling this integration, and (4) devising sound decision-making tools. Therefore, this research addresses the existing gaps and aims to develop effective tools for integrating sustainability aspects into maintenance decisions practices, and to explore the challenges that hinder this integration and the potential role of technology in overcoming them. First, a comprehensive and hierarchical framework of sustainable maintenance performance indicators is developed and validated by experts in the field. Using this framework as a guide, a fourth-order Partial Least Square-Structural Equation Modeling (PLS-SEM) based higher component model for measuring sustainable maintenance performance is proposed and validated in the Oil and Gas industry. Furthermore, given that digital transformation (DT) of maintenance can play a pivotal role in facilitating the integration of sustainability issues in maintenance decision-making, it is crucial to identify the key ingredients and the most influencing factors, beyond purely technological aspects, that drive the success of digitalization efforts. Then, a hybrid reactive Delphi approach is adopted to identify and validate the key factors driving the success of maintenance DT and aiding its implementation. Finally, a first-step is taken towards proposing planning models that integrate sustainability in the decision-making process.
  • Publication
    Optimization Of Smart Composite Panels For Enhanced Ballistic Performance
    (2022-05) AlAhmed, Yaqoub S.; Bahroun, Zied; Hussein, Noha
    Composite materials offer a unique combination of properties from lightweight and high strength to long-term durability and cost-effectiveness that opens the field for their application in different industries. However, under the application of an impact load, complex distribution of stresses and strains is produced in the composite. This is because the body is heterogeneous (reinforcement, matrix, and interface) with diverse mechanical properties of the phases, which may remain constant or degrade during service. Researchers investigated different impact mitigation techniques like changing core designs to enhance load impact resistance and strength. Others examined fluid-filled barriers to mitigate the impact of ballistic loading by investigating the energy response and the energy absorption. None of the research works considered investigating the optimum structure of a fluid-filled core. This research focused on designing a composite sandwich panel with an optimum fluid-filled core. A conceptual design of a recirculation capability water-filled sandwich panel core was proposed, investigated, and optimized. Numerical experiments were conducted using Abaqus/CAE software based on experimental planning. The developed model was validated using data and information drawn from the literature. Four design variables were considered: plate thickness, spacing distances between the core shapes, volume fraction of fluid in the core, and core structure shapes. Regression analyses were used to investigate the numerical model responses and develop the regression equations used in the optimization model. Later, optimization techniques were used to determine the optimum design parameters that maximize the impact resistance using GAMS software. Results show that the water’s ability to absorb kinetic energy resulted in a delay in damage initiation and propagation. Thicker plates and closer spacing between core-core structural elements increased the stiffness of the sandwich panel, improving its blast mitigation performance. Optimization results reveal that a tubular core fully filled with water offers the best combination of properties from maximizing the elastic strain energy and minimizing the external work done, the sandwich panel mass, and cost with a plate thickness of 5 mm, core-to-core spacing of 75 mm, and volume fraction of fluid of 100%. Sensitivity analysis showed that the optimum core shape selection was sensitive to all four factors.
  • Publication
    Project Planning and Control Models for Sustainable Construction Projects
    (2022-05) Rajabi, Sareh; El-Sayegh, Sameh; Romdhane, Lotfi
  • Publication
    Biomarker Discovery Utilizing Big Data: The Case of Diabetes in United Arab Emirates
    (2022-05) Banimfreg, Bayan Hassan; Shamayleh, Abdulrahim; Alshraideh, Hussam
    Diabetes Mellitus (DM) received substantial attention for exploring its mechanism as expected to be the seventh primary reason for death worldwide by 2030. The hallmark of DM leads to damaging effects on many organ systems, mainly the cardiovascular, ophthalmic, and renal systems. The number of adults with DM to reach 95 million by 2030 and 136 million by 2045 in the Middle East and North Africa region. Type 2 diabetes (T2DM) is the most common type of DM, accounting for around 90% of diabetes cases. T2DM is a multifactorial chronic metabolic disease caused by genetic and non-genetic factors resulting from an imbalance between energy intake and output and other lifestyle-related factors. However, the detailed understanding of T2DM etiology is still limited. As the focus of this work is the metabolomic derived biomarker discovery, a non-targeted metabolomics experiment using liquid chromatography with tandem mass spectrometry (LC-MS/MS) is conducted to explore the metabolic profile of diabetic Emirati Citizens to uncover potential novel diabetes biomarkers through big data analytics. The study is twofold: in the first part, a comprehensive analysis is performed to reveal the profiling metabolites of diabetic Emirates compared to healthy ones. Blood samples of 50 diabetic Emiratis versus 42 healthy were utilized to investigate for differential metabolites. In the second part, a metabolomic study of patients with diabetic kidney disease against dialysis non-diabetics patients was conducted to uncover their distinct biomarkers. Blood samples of 11 dialysis diabetics and 25 dialysis non-diabetic were used to reveal potential biomarkers. A great panel of potential differential metabolites was identified among diabetic and non-diabetic Emirates. The identified metabolites were sorted into classes, including Tryptophan and Purines. Several potential biomarkers and their related pathways were pinpointed among dialysis patients, including Tyrosine metabolism-related metabolite and 3,4-Dihydroxymandelic acid. These studies provide detailed coverage of blood metabolic changes related to T2DM in the Emirati population. The results of this work are mainly consistent with similar international studies, with a few added biomarkers reflecting the region-specific health profile. The worldwide consensus on common metabolites encourages the clinical trials of novel biomarkers that could expedite the treatment process for diabetics. Monitoring and managing diseases might move medicine from a therapeutic model to a prevention model.
  • Publication
    Portfolio Value Optimization Model for Sustainable Construction Projects
    (2021-05) Anjamrooz, Taha; El-Sayegh, Sameh
  • Publication
    Tracking the Commitment to Sustainable Development Decisions
    (2021-04) Al-Tekreeti, Mustafa Sahban; Beheiry, Salwa; Ahmed, Vian
  • Publication
    A Data Analytics Approach for Forecasting Cash Flow in Construction Projects
    (2021-03) Mahmoud, Hasan S.; Ahmed, Vian; Beheiry, Salwa
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