This study evaluates the spatial equity of community parks in Al-Khobar City, Saudi Arabia, by examining their proximity, availability, distribution, accessibility, and user satisfaction. Ensuring equitable access to public open spaces is vital for promoting urban liveability and achieving the sustainability objectives of Saudi Vision 2030. A mixed-methods approach integrating Geographic Information System (GIS)-based spatial analysis with a structured user survey was applied. GIS was used to map park locations, calculate per capita green space, and assess accessibility within a 500 m walking radius, while survey data from 300 respondents captured user satisfaction and perceptions of community park dimensions and indicators. The results reveal pronounced spatial disparities across neighbourhoods, with more than twenty areas lacking any park access and several others falling below the 5 m² per capita standard. In contrast, centrally located neighbourhoods demonstrate adequate provision and higher satisfaction levels. These findings indicate a fragmented and inequitable park distribution that limits community well-being and social inclusion. The study concludes that integrating GIS-based evidence with community feedback can inform data-driven planning policies and promote equitable, accessible, and sustainable community parks. The proposed framework offers a replicable model for assessing urban green space equity in other Saudi and Middle Eastern cities.

Neighborhood parks have the potential to support urban health, social interaction, and environmental quality; however, their usability depends not only on perceived quality but also on conditions that may constrain their use. This study examines neighborhood park performance by integrating user satisfaction with park quality and perceived barriers to use across three neighborhood parks in Al Khobar, Saudi Arabia. Park quality was assessed through six dimensions and 25 indicators derived from a thematic synthesis of the literature, while perceived barriers were examined across six categories covering environmental and climatic conditions, accessibility and mobility, safety and security, facilities and services, maintenance and environmental quality, and social and personal conditions. Field observations and surveys of 327 existing park users supported an exploratory comparison of quality satisfaction and perceived barriers; non-users were not represented. Results showed that Al Thuqba exhibited the greatest quality deficiencies and strongest overall barriers, while Al Rakah and Al Safa generally performed better. Environmental and climatic conditions emerged as the most prominent barriers across all three parks, particularly extreme heat and insufficient shade. At Al Thuqba, lower park-level satisfaction means for environmental quality, safety and recreational facilities occurred alongside higher perceived-barrier means. Climatic barriers remained prominent at Al Rakah and Al Safa despite comparatively positive quality evaluations. These aggregate patterns suggest site-specific assessment priorities; they do not establish individual-level relationships or effects on visitation. The combined assessment provides complementary descriptive evidence for planning the three parks and warrants testing in other settings.

Earthen architectural heritage is highly vulnerable to moisture, erosion, salt activity, and progressive loss of cohesion, creating a need for consolidants that improve mechanical performance without obscuring the limitations of laboratory-to-field transfer. This study evaluates a graphene oxide–ESTEL (GO–ESTEL) nanocomposite as an exploratory consolidant for adobe under controlled laboratory conditions in Egypt and discusses, separately, how the results may inform future validation at Sudus, Saudi Arabia. Laboratory specimens and non-Sudus reference adobe fragments were characterized using polarized light microscopy (PLM), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), computed tomography (CT), and uniaxial compressive strength testing. A fixed exploratory formulation based on a 3 wt.% aqueous GO dispersion combined with ESTEL was applied to the adobe specimens. The treated specimens showed denser microstructural features and improved particle bridging in SEM observations, while CT imaging provided complementary qualitative visualization of the internal structure of the treated adobe. Average maximum compressive load increased from 340.60 to 486.87 kgf, corresponding to a 42.94% increase; maximum displacement and strain at failure decreased by approximately 36.8%. These results demonstrate short-term laboratory strengthening of the tested adobe formulation, but they do not establish long-term durability, an optimum GO concentration, or direct compatibility with Sudus materials. The Sudus survey is therefore retained only as an application context and monitoring framework rather than evidence of field effectiveness. Site-specific mineralogical characterization, environmental monitoring, formulation optimization, accelerated ageing, and controlled pilot trials are required before any conservation application at Sudus or comparable heritage sites.

King Fahd University of Petroleum and Minerals (KFUPM) currently relies on administrative judgment for urban planning, which may introduce human errors and overlook environmental and social factors. This study examines the potential of Artificial Intelligence (AI) in enhancing urban planning decisions, using Autodesk Forma to analyze solar exposure, wind behavior, and microclimate variations across proposed building sites. The research utilizes an AI-driven site evaluation framework to conduct multi-faceted environmental analyses, including highly predictive wind movement simulations. This approach generated quantifiable metrics essential for sustainable urban design optimization, demonstrating, for example, that one design option achieved 0% uncomfortable pedestrian wind conditions, a key measure of success unattainable through conventional evaluation methods. The findings highlight AI’s role in improving planning accuracy, reducing reliance on static datasets, and supporting data-driven decision-making. However, human expertise remains essential for interpreting AI-generated outputs, ensuring alignment with institutional goals, and validating results. Future research should explore integrating custom datasets for enhanced wind analysis, AI-driven site selection automation, and expert-driven refinements to planning strategies. This study underscores AI’s value in urban planning while emphasizing the need for a hybrid AI-human decision-making framework.