United Arab Emirates University
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DATA-DRIVEN MACHINE LEARNING APPLICATIONS FOR PREDICTIVE MODELING OF PETROCHEMICAL AND ECOFRENDLY SYSTEMS
Get PDFTraditional experimental approaches in industrial processes, such as Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, thermogravimetric analysis (TGA), and well-drilling operations, are often constrained by time, cost, and operational limitations. This research explores the application of data-driven Machine Learning (ML)-based predictive modeling to improve efficiency and reduce dependency on resource-intensive experimentation. The study develops ML models for three distinct processes: FTIR intensity prediction of bitumen thermal cracking products, thermal degradation of Medium-Density Fibreboard (MDF) using TGA data, and Rate of Penetration (ROP) prediction in petrochemical industry. Six algorithms: Linear Regression (LinReg), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Gradient Boosting Regression (GBR), Random Forest (RF), and K-Nearest Neighbors (KNN) were evaluated across multiple scenarios. The models were assessed usingmetrics such as the coefficient of determination (R²) and Root Mean Squared Error (RMSE) to ensure both accuracy and generalization capabilities. All computational modeling, including data cleaning, feature engineering, ML modeling and Bayesian Optimization (BO), was performed using Python.Results show that ensemble models, particularly GBR and RF, consistently outperformed other techniques in predictive accuracy and generalizability. In the FTIR analysis, GBR achieved 99.65% accuracy under an 80/20 data split, while RF yielded 94.37% accuracy when trained on lower temperatures and tested on unseen high temperatures. For the TGA data, RF achieved 100% test accuracies in oxidation and pyrolysis under full dataset splits, while GBR maintained strong performance in extrapolative scenarios achieving 98.91% accuracy for oxidation and 99.67% for pyrolysis when trained on lower heating rates and tested on higher ones. In ROP prediction, the GBR model reached 96.2% accuracy, outperforming empirical models such as the Bourgoyne and Young (BY) and Bingham models. The findings emphasize the importance of data distribution in training/testing splits, particularly when extrapolating to high-temperature conditions.This study demonstrates the transformative potential of ML in enhancing predictive accuracy across various industrial systems. The integration of Python-based modeling, scenario-driven analysis, and advanced hyperparameter tuning through BO establishes a versatile framework for data-driven optimization. These outcomes support the broader adoption of ML in petrochemical and environmentally focused industries, offering pathways toward more sustainable, efficient, and intelligent process management
NEW CONTRAST-BASED METRICS FOR ROADWAY LIGHTING DESIGN
Get PDFRoadway lighting standards specify design targets of horizontal illuminance or luminance at the roadway surface for various roadway types. Although the luminance and illuminance method to roadway lighting standards incorporate some factors that affect human visibility, other important factors, such as the target’s contrast and size, are not examined in these standards. Roadway lighting standards should consider how the human visual system detects objects and provides comfortable approaches to improve visibility for drivers. As luminous contrast has a crucial impact on visibility measures, recent studies have been directed to propose new approaches that utilize it. This research aims to propose new roadway design indexes based on the contrast of obstacles and to investigate whether they can be integrated with or replace the design based on luminance or illuminance. An experimental program was performed on a two-lane road located in Falaj Hazzah. Grids comprising five models, each model representing obstacles of different heights (0.05, 0.5, 1.0, 1.5 m), were distributed along the lateral and longitudinal directions of the road and systematically moved between two streetlights. A vehicle-mounted camera was placed at a distance equal to the total stopping distance from each obstacle to capture calibrated high-dynamic-range (HDR) luminance maps for diverse scenarios. HDR images provided detailed luminance data to evaluate factors influencing contrast. The dynamic of contrast results was investigated under various roadway lighting design variables, such as pole spacings, lumen output, and vehicle headlights. Dialux software was utilized to replicate the existing road to validate the model as a design tool. The contrast results were then used to propose two different metrics for roadway lighting standards, named the Useful Contrast Index (UCI) and the Mean Contrast Index (MCI). The performance of the proposed metrics was evaluated by changing lighting design variables such as lumen output and pole spacings. Results showed a weak correlation between obstacle contrast and road pavement luminance, highlighting the need to incorporate new contrast-based approaches into current roadway lighting design. For very low obstacles, contrast decreased with increased lumen output, whereas for taller obstacles (1.5 meters), contrast improved with higher lumen output. While reduced pole spacing increased pavement luminance, it did not increase the contrast of obstacles. The results also show that the proposed contrast-based indexes have merit and could be integrated into current roadway lighting guidance, aiming at enhancing the visual environment for drivers and improving safety
EXPLORING THE QUALITY OF PROFESSIONAL DEVELOPMENT IN ADDRESSING CHALLENGES WITH BEHAVIOR MANAGEMENT OF SPECIAL NEEDS STUDENTS IN INCLUSIVE CLASSROOMS: AN INSTRUMENTAL CASE STUDY
Get PDFInclusive education has become a major educational priority globally and within the United Arab Emirates (UAE), driven by policies promoting the full participation of students with special educational needs (SEN) in inclusive classrooms. This study explores how elementary-level general education teachers perceive the quality of professional development (PD) in addressing their challenges with behavior management of students with SEN in inclusive classrooms. A qualitative case study approach was used, utilizing semi-structured interviews. Three overarching main themes were generated from the interviews, which were further categorized into eight themes and 18 subthemes. The findings indicated that teachers face challenges from the behavioral issues of SEN students in inclusive classrooms, affecting both typical peers and teachers. Moreover, teachers had mixed perceptions of PD quality, noting significant advancements in some areas and acknowledging gaps. Many teachers expressed concerns about its overall design, delivery, and limited relevance of the content. Teachers emphasized the need for ongoing, practical, and context-specific training aligned with classroom realities. The study concludes with evidence-informed implications, grounded in the interview findings, aimed at improving teacher readiness, instructional effectiveness, and outcomes in inclusive classrooms. It offers an original contribution by adding to the UAE’s inclusive education policy discussions, providing insights into increasing teacher capacity for behavior management in inclusive settings
Mental Health Quality of Women Employees at the University of Sharjah
No full textThis study examines the quality of mental health among 200 female employees at the University of Sharjah—teaching and administrative—during the academic year 2023–2024. The Ryff Model of Psychological Well-Being was applied using the Ryff scale with six dimensions: self-acceptance, positive relations with others, personal growth, purpose in life, environmental mastery, and autonomy. Tool validity and reliability were confirmed statistically. Findings indicate generally high mental-health quality; autonomy ranked highest among the six domains, whereas a relative weakness appeared in self-perception. Significant differences were observed by years of service (1–5, 6–10, ≥11), by marital status (single, married, widowed, divorced), and by work nature (academic vs. administrative), with patterns favoring some groups. The study underscores the need to consider personal and professional variables when designing initiatives to promote women’s mental health at the university.
Keywords: mental-health quality; women employees; academics; University of Sharjah; Ryff model
ASSESSING TEACHERS’ PRACTICES IN SUPPORTING GIFTED/TALENTED AND TWICE-EXCEPTIONAL STUDENTS IN SCHOOLS AND CENTERS IN ABU DHABI
No full textGifted/talented and twice-exceptional (2e) students, especially those whose needs are often overlooked in mainstream classrooms, are more likely to benefit from consistently implemented Differentiated Instructional Practices (DIPs). This study aimed to examine implelemntation of DIPs for gifted/talented and 2e students and to compare the practices implemented between of Special Educational Needs (SEN) teachers and General Education teachers in schools and centers within Abu Dhabi. This research used a quantitative, cross-sectional survey design to collect data from eighty-six teachers from Abu Dhabi. A questionnaire was designed to rate teachers’ self-reported implementation of DIPs across the four domains of content, process, product, and learning environment. Results indicated that while teachers reported a moderate to high level of overall implementation of DIPs in their schools, a gap exists between teacher groups, SEN teachers reported consistently higher levels of implementation across all domains compared to their general education colleagues. The greatest difference was observed in the modification of content, where general education teachers scored very low, indicating a specific area for urgent focus. Therefore, the results of this study can inform school leaders, professional development providers, and policymakers at ADEK and the MOE to identify the key levers for effective changes and advance the inclusive and gifted education in UAE schools
ISOLATION AND CHARACTERIZATION OF PLANT GROWTH PROMOTING RHIZOBACTERIA FROM SOILS IN THE UAE AND THEIR EFFECT ON PLANT SALT TOLERANCE
No full textPlants are constantly challenged by environmental stresses that restrict their growth and productivity. In the United Arab Emirates (UAE), soil salinity is a critical barrier to agriculture, particularly for tomato (Solanum lycopersicum), which is highly sensitive to moderate salinity levels (\u3e2.5 dS m⁻¹). This study explored the potential of plant growth–promoting rhizobacteria (PGPR), especially actinobacteria, to enhance salt tolerance in tomato. The objective was to evaluate the effeccts of rhizosphere-competent (RC) and non-rhizosphere-competent (NRC) PGPR on plant physiology and yield in saline sandy soils. Actinobacterial strains were isolated from the tomato rhizosphere were screened for the production of 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase (ACCD) and plant growth regulators (PGRs). Greenhouse trials under salt stress (200 mM NaCl) examined morphological, physiological, and biochemical responses. Among the isolates, strain #36 (an efficient PGR-producer) and RC isolate #53 (an efficient ACCD producer) were tested individually and in combination. Inoculation with actinobacterial consortia markedly improved shoot and root growth compared with uninoculated controls, with RC consortia consistently outperforming NRC strains under both normal and saline conditions. Strain #36 enhanced growth and photosynthetic efficiency, while its combination with strain #53 provided superior stress tolerance, reducing endogenous ACC levels threefold. This study is the first to demonstrate the synergistic role of RC actinobacterial consortia in alleviating salt stress in tomato. The findings highlight their promise as bioinoculants for sustainable agriculture. By uncovering the mechanisms of PGPR-mediated salinity tolerance, the research offers valuable insights for developing bioinoculant-based strategies to strengthen food security and agricultural sustainability in the UAE
BIOACTIVE MEMBRANE SYSTEM FOR EFFICIENT EMERGING POLLUTANTS TREATMENT
No full textEmerging pollutants (EPs), such as pharmaceuticals and personal care products, are increasingly detected in municipal wastewater due to household discharge and improper industrial disposal. Their persistence and ecological risks pose major challenges to conventional treatment technologies. Among advanced options, membrane filtration, particularly microfiltration and ultrafiltration, has gained prominence for its efficiency and scalability. However, membrane fouling, especially biological and chemical fouling, remains a critical barrier, accounting for nearly half of total energy consumption and requiring frequent use of harsh chemical cleaning agents that further burden the environment.This PhD research addresses these challenges by developing and evaluating hybrid bioactive enzyme-active membranes (EAMs) as a sustainable and effective solution for EP removal, using ibuprofen as a model contaminant. The work introduces a novel approach by immobilizing the biocatalyst Aspergillus oryzae peroxidase onto a PVDF membrane and integrating it with structured nanomaterials, including metal-organic frameworks (MOFs) such as ZIF-8 and ZIF-L. MOFs provide a high surface area, tunable pore structures, and improved enzyme–substrate interactions, thereby enhancing mass transfer and catalytic efficiency.A series of hybrid membranes were fabricated and systematically characterized to evaluate their mechanical stability, chemical resistance, and antifouling properties under realistic operating conditions. Enzyme encapsulation within MOFs proved superior to surface adsorption, as it preserved enzyme conformation, increased active surface area, and enhanced pollutant degradation. A diffusion–reaction model was developed to analyze internal transport dynamics, while adsorption kinetics and thermodynamics were modeled to optimize fixed-bed continuous operation.Further advancements included the co-immobilization of peroxidase with redox mediators, including ABTS, γ-Fe₂O₃, and kraft lignin. The peroxidase/ZIF-8/ABTS/PVDF membrane achieved high ibuprofen degradation, strong reusability, and excellent antifouling resistance, with ABTS•⁺ radicals playing a key role in boosting electron transfer and catalytic activity. Machine learning (ML) models were employed to support performance prediction and process optimisation. Linear regression models achieved high predictive accuracy, demonstrating the potential of AI-driven tools for forecasting membrane performance. The peroxidase/γ-Fe₂O₃/ZIF-8 hybrid membrane removed over 90% of ibuprofen under mild conditions without added H₂O₂, maintaining both permeability and catalytic activity over five cycles and 40 days. Incorporating kraft lignin further enhanced pollutant affinity and sustainability, with the peroxidase/lignin/ZIF-8/PVDF membrane achieving up to 99% ibuprofen removal, alongside excellent reusability and long-term stability for 5 cycles and 40 days, respectively. Addressing the environmental persistence and potential toxicity of fluorinated polymers, PVDF underscores the necessity of long-term sustainability in membrane materials, emphasizing the necessity of future development of eco-friendly alternatives and mitigation strategies.The versatility of the hybrid bioactive system was validated across PES membranes, which successfully integrated catalytic components without surface modification. This environmentally friendly processing method highlights adaptability to different polymer platforms while delivering strong pollutant degradation efficiency, improved antifouling resistance, and reliable reusability across multiple operational cycles.Finally, an economic assessment was conducted to evaluate scalability and complement the technical validation. The results indicate that the proposed approach is economically viable and technically feasible for large-scale implementation. This thesis demonstrates enzyme-active and hybrid bioactive membranes as promising alternatives to conventional pollutant removal technologies. By combining the catalytic power of enzymes with the structural advantages of MOFs, nanoparticles, and renewable biopolymers, the research establishes a new pathway for advanced water treatment systems. The findings provide a solid scientific and practical foundation for scalable, eco-friendly, and cost-effective technologies that directly address emerging pollutants\u27 challenges. Beyond its scientific contributions, the research supports national and global goals for sustainable water management and environmental protection, offering innovative solutions to safeguard water security for the future
THE RIGHT TO BE FORGOTTEN IN THE DIGITAL AGE
No full textThis dissertation addresses the right to be forgotten in the digital environment as one of the most significant emerging rights in today’s information society. It aims to clarify the theoretical, jurisprudential, and judicial foundations of this right, analyze its legal nature and sources, and delineate its scope and limits while balancing it against freedom of expression, the public’s right to know, and the requirements of the digital economy. The research employed a multidisciplinary methodology combining the descriptive-analytical approach in examining legal texts and international instruments, the comparative method between the European experience—particularly the General Data Protection Regulation (GDPR)—and Arab legislation in the UAE and Egypt, as well as the judicial approach through an analysis of landmark case law such as Google Spain (2014), and Google v. CNIL (2019).The dissertation concludes that the right to be forgotten is not absolute but is subject to key restrictions linked to public interest, national security, scientific research, and historical archiving. It also finds that European case law served as the cornerstone in transforming this right from a theoretical notion into a legally recognized framework, later codified explicitly in Article 17 of the GDPR. While the UAE and Egypt have both recognized the rights of deletion and rectification, they have not yet fully crystallized the comprehensive concept of the right to be forgotten as developed in Europe. This highlights the need for clearer procedural mechanisms that would allow individuals to exercise this right effectively.The findings contribute academically by enriching Arab scholarship on data protection in the digital era, and legislatively by guiding national lawmakers toward strengthening the balance between protecting individuals and accommodating the needs of the digital economy. Socially, the study underscores that the right to be forgotten is a vital tool for preserving human dignity and reputation in the face of permanent digital archiving. In doing so, the dissertation fills a clear scholarly gap in Arab legal studies, which often approached the subject only partially or theoretically. It provides a comprehensive perspective that integrates doctrine, legislation, and case law, while also introducing a novel dimension by linking the right to be forgotten with the imperatives of digital transformation and the knowledge economy
CHILD OCCUPANT SAFETY IN THE UNITED ARAB EMIRATES: CRASH, INJURY, AND ANTHROPOMETRIC ANALYSIS
No full textThe United Arab Emirates (UAE) has one of the highest rates of child occupant injuries and fatalities globally. A comprehensive review of the literature revealed very limited research on child occupant injuries and fatalities in the UAE. All but two of the identified studies relied on data from Al Ain-based sources namely - Al Ain Department of Preventive Medicine, Al Ain Hospital, and Tawwam Hospital - and were restricted to that city. Furthermore, most of these studies were conducted nearly 30 years ago with the two most recent ones (conducted over a decade ago) based on crash data that was already ten years old at the time of analysis. To address this significant gap, the first aim of this dissertation was to identify the key risk factors associated with child occupant injuries and fatalities using a recent national dataset to provide recommendations for achieving comprehensive reductions in child occupant injuries and fatalities. A retrospective population-based analyses of UAE national crash data for child occupants aged 17 years or younger at the time of the crash was conducted. The data was obtained from the UAE Ministry of Interior (MOI) for the years 2012-2023. Descriptive statistical analyses were performed to describe the demographic, crash, environmental, temporal, and outcome variables. Univariate ordinal logistic regression analyses were conducted to determine the factors that were significantly associated with child occupant injury severity. The variables that were found to be significantly associated with child occupant injury severity were subsequently included in the multivariable model to adjust for potential confounding effects. The results show that girls were less likely to sustain injuries than boys (OR: 0.720, CI:-0.522, -0.137). Similarly, crashes due to tailgating (OR: 0.405, CI:-1.243, -0.562) and sudden deviation (OR: 0.713, CI:-0.596, -0.081) had lower injury risk compared to the “others” (distracted driving, negligence/inattention, impaired driving, non-compliance to traffic signals, right of way violation, etc.) crash causes. On the other hand, crashes due to speeding without considering road conditions (OR: 1.729, CI:0.211, 0.883) had higher odds of injuries for child occupants. Child occupants seated in the driver seating position (OR: 1.826, CI:0.350, 0.854) and front seat (OR: 1.784, CI:0.368, 0.790) were more likely to sustain injuries compared to those seated in the rear seat. Child occupant crashes that occurred at commercial areas (OR: 0.605, CI:-0.806, -0.199) had lower odds of injury compared to the “others” (government area, tunnel/bridge, school, sandy area, petrol station, etc.) crash locations. Conversely, highway crashes (OR: 1.301, CI:0.024, 0.503) were associated with increased injury risk for child occupants. Rollover crashes (OR: 2.718, CI:0.193, 0.862) and fixed-object collisions (OR: 1.465, CI:0.031, 0.732) were associated with higher odds of injury compared to the “others” (rotational collision, motionless collision, moving-object collision, consecutive collision, fall crash, etc.) crash types. Compared to crashes that occurred in fall, crashes that occurred in summer months were associated with higher injury risk for child occupants (OR: 1.307, CI:0.010, 0.526). These findings should be employed to develop strategies to improve child occupant safety in the UAE. Child passenger legislation (CPL) has proven to be effective in ensuring child restraint system (CRS) use, thereby positively enhancing the safety outcomes of traffic crashes involving children. Road safety legislation mandating the use of child safety seats (CSS) and seatbelts was enacted in July 2017 by the UAE government. This legislation requires the use of a CSS for children up to 4 years of age and seatbelt use for all other vehicle occupants. The second aim of this dissertation was to evaluate the impact of UAE’s CPL on reducing the counts and rates of injuries and fatalities per child population among child occupants aged 0–14 years from 2012 to 2023, controlling for several confounders. Time-series Poisson regression analyses were used compared to the incidence of these outcomes between the pre and post-legislation periods. The results indicate that the legislation significantly reduced the counts of minor injuries by 48% (IRR: 0.52, CI: 0.39-0.69, p\u3c 0.001), severe injuries by 55% (IRR: 0.45, CI: 0.21-0.96, p=0.044), and fatal injuries by 70% (IRR: 0.30, CI: 0.12-0.67, p=0.005) for children aged 0-4 years as well as the counts of moderate injuries by 33% (IRR: 0.67, CI: 0.51-0.87, p=0.003) for children aged 10-14 years. No significant reductions were observed in injury and fatality rates among children aged 0–14 years. The legislation was also more effective in reducing injury and fatality risk among girls and on roads with more than two lanes. These findings highlight that the legislation\u27s effectiveness varies across both modifiable and non-modifiable risk factors. To sustain and enhance the gains of the CPL, targeted interventions, continuous public education campaigns, and strict policy enforcement are recommended. CRS have been demonstrated to significantly reduce the risk of injuries and fatalities for child occupants. However, several studies conducted before the enactment of the CPL have reported low rates of CRS use in the UAE. Therefore, the third aim of this dissertation was to assess the prevalence of CRS use post CPL in the UAE and to identify the factors associated with CRS use, appropriate use, misuse, and best practice (BP) use among children. The social ecological model (SEM) was used to examine the individual, interpersonal, community, and societal factors. Parents and carers of children younger than 13 years completed a self-reported questionnaire (n = 409). Backward logistic regression was used to determine the factors associated with CRS use, appropriate use, misuse, and BP use. Results of the study indicate that only 44.3% of parents always restrained their child while travelling in a vehicle, 49% restrained their child appropriately, 21.4% had no CRS misuse while restraining their child, and 9% achieved BP CRS use. Individual level factors were mostly the predictors of CRS use, appropriate use, misuse, and BP use. Parental seatbelt use was associated with CRS use while non-use was associated with CRS misuse. Parental perception that their child is happy to be restrained in a CRS was associated with both CRS use and BP use. Frequent parental removal of their child’s CRS from the vehicle was associated with CRS misuse while infrequent removal was associated with BP use. Parental self-blame if their child was to sustain serious injuries while travelling unrestrained in a vehicle was associated with CRS appropriate use while the lack of self-blame is associated with CRS use. Strict parenting style and recommendation to use CRS were associated with CRS use while parental relationship with the child was associated with appropriate CRS use at the interpersonal level. At the community level, CRS use was associated with ethnicity. Multilevel factors including children aged less than 1 year and using a rear-facing CRS were associated with appropriate CRS use. The findings of this study should be translated into targeted interventions to promote CRS use, appropriate use, and BP use as well as reducing CRS misuse at the different levels of the SEM.Child occupants can be legally restrained by adult seatbelts from the age of 5 years in the UAE. However, the BP recommendation for achieving appropriate seatbelt fit for children is a minimum height of 148 cm, weight of 37 kg, and seated height of 74 cm. The fourth aim of this dissertation was to evaluate whether the current UAE CPL aligns with the physical characteristics of the local child population. Representative anthropometric data was collected from 627 children aged 5-12 years old in the UAE. Key measurements included height, weight, and seated height which are essential for assessing seatbelt fit. Descriptive statistical analysis was performed to obtain the frequencies and percentages for these measurements. The values for height, seated height, and weight were calculated for each child occupant age. To compare the anthropometric data of children in the UAE against the BP recommendations for protecting child occupants in vehicles, the proportions of child occupants smaller than 148 cm in height, smaller than 74 cm in seated height, and lighter than 37 kg in weight were calculated for the children. Results of the study shows that 100% of children aged 5–7 years, 99% aged 8–9 years, 87% aged 10 years, 76% aged 11 years, and 59% aged 12 years do not meet the combined requirement of 148 cm in height, 74 cm in seated height, and 37 kg in weight for achieving proper adult seatbelt fit. This finding should serve as justification for enhancements to the CPL in the UAE to ensure that child occupants are using an appropriate restraint for their age, shape, and size and therefore will be appropriately protected by the restraint in the event of a motor vehicle crash. Ultimately, the finding
Urban Form, Density, and Energy Efficiency: A Typo-Morphology-Based Simulation Framework for Superblock Planning and Densification in Abu Dhabi
No full textUrban form plays a critical role in shaping environmental performance, particularly building energy. In Abu Dhabi and other GCC cities, development is structured around superblocks—large arterial-bounded land parcels units derived from NPU planning principles. Rapid urban expansion, rising energy demand, and the predominance of low-density suburban growth (now exceeding 55% of Abu Dhabi’s footprint) highlight the need for frameworks that can evaluate the energy implications of this morphology and guide both new development and retrofitting strategies. Superblocks therefore offer a consistent and policy-relevant scale for analysing form–energy interactions in the GCC’s hot-desert context, where early-stage, morphology-informed planning tools are essential. Despite extensive research on form–energy relationships, significant conceptual, methodological, and contextual gaps persist—particularly the limited integration of energy analysis within an explicit urban morphological and planning framework. Existing studies rely heavily on single-dimension indicators such as density, which is inconsistently defined and unable to capture the combined influence of geometry and network properties on energy performance. Moreover, no standardized or computationally efficient multivariate index exists for characterizing urban form at the neighborhood scale, and typology studies vary widely in spatial units, indicators, and clustering approaches. These gaps are especially pronounced in Middle Eastern cities, where superblock-based planning models and hot-desert climates produce form–energy dynamics not addressed by existing typological frameworks. Together, these limitations underscore the need for an integrated, morphology-sensitive, and context-specific framework for reliable neighborhood-scale energy evaluation. Applied to 165 superblocks in Abu Dhabi, this study develops a three-step, data-driven simulation framework. First, urban typologies are identified using an unsupervised machine-learning method combining Principal Component Analysis and Gaussian Mixture Model clustering. A multivariate Form Index—comprising nine indicators of density, geometry, and networks—is constructed, enabling representative typologies to be identified using Mahalanobis Distance and profiled through k-means discretisation. Second, a hybrid UBEM workflow translates these typologies into simplified “notional grids” through model-order reduction, enabling computationally efficient neighbourhood-scale energy simulations using established modelling techniques. Third, a parametric modelling framework systematically varies density indicators—floor area ratio, land coverage, and building height—and visualises scenario outcomes using Spacemate and parallel-coordinate plots to support early-stage energy-conscious planning. The analysis yields four clearly distinguishable superblock typologies: dense compact high-rise (T1), dense compact low-rise (T2), less dense open low-rise (T3), and spacious open low-rise (T4). T1, the most vertically and horizontally compact form, exhibits the lowest energy intensity, while T4, the most dispersed suburban form, records the highest. Energy performance improves with higher FSI, GSI, plan depth, obstruction angle, and network density, whereas high OSR, wide streets, and coarse networks increase demand. Balanced densification can reduce energy use (up to 10% total and 24% cooling), while poorly configured scenarios may increase consumption by ~11%. This thesis makes two principal contributions. First, it develops a comprehensive multivariate Form Index integrating density, geometry, and network measures into a coherent, planning-relevant descriptor of urban form—addressing the limitations of single-measure approaches and extending the Spacematrix method. Second, it establishes the first systematic, data-driven typo-morphological framework for classifying superblocks in Middle Eastern cities, complemented by a hybrid UBEM–parametric modelling workflow that offers planners an efficient, interpretable tool for evaluating neighbourhood-scale energy performance and densification strategies