Engineering

Abstract: In the broader context of the ecological transition, it is essential to identify solutions that ensure indoor environmental quality encompassing thermal, visual, acoustic, and indoor air quality conditions to safeguard occupant health and well-being. These solutions should also meet the demand for energy-efficient buildings. With specific regard to thermal environments, a distinction must be made between residential and non-residential settings, where comfort conditions can be achieved, and industrial environments, where thermal stress—and consequently health risks—may arise. To evaluate the quality of a thermal environment, key metrics are necessary. These include the Predicted Mean Vote (PMV) for thermal comfort, Predicted Heat Strain (PHS) and the Wet Bulb Globe Temperature (WBGT) for hot environments, and Required Insulation (IREQ) for cold environments, all governed by ISO-EN standards. The use of indices in residential and non-residential buildings outlines two critical challenges. The first relates to the fact that, in certain instances involving non-air-conditioned buildings, conditions can be borderline between comfort and thermal stress, which must be accurately identified. Secondly, the application of indices frequently neglects necessary variables, disregarding the fundamental limitations and operational boundaries inherent to both objective and personal input quantities. Moreover, the use of measurement devices inconsistent with the minimum requirements laid down by the standards in the field results in unwanted biases with unforeseeable consequences. This review explores the formulation, use, and limitations of the four indices mentioned, providing a perspective on their future development. It establishes the criteria for reliable long-term assessments of thermal and energy environments, encompassing the analysis of both hot and cold strains.

Abstract: Computer-aided simulation and data-driven analysis provide an effective technical basis for optimizing the spatial organization of mechanical, electrical, and plumbing systems in large-scale commercial complexes. To reduce construction clashes, repeated rework, and investment losses caused by high-density MEP layouts, a BIM-based spatial topology and stochastic optimization model is developed. The model integrates BIM data parsing, component coding, topology construction, bounding-box screening, precise distance calculation, conflict-intensity evaluation, and Monte Carlo simulation. Conflict-type severity, impact range, and rework probability are normalized, and their coefficients are estimated by constrained non-negative regression rather than subjective assignment. Safety clearances are determined from design codes, equipment maintenance manuals, installation tolerances, and project coordination requirements. The stochastic model specifies Beta-Bernoulli, lognormal, and triangular distributions for conflict occurrence, construction and rework losses, and operation and maintenance losses, respectively, and performs 50,000 simulation iterations. A 420,000 m² commercial complex in Shenzhen is used for validation. Compared with the original scheme, the integrated optimization scheme reduced pipeline density from 7.82 m/m² to 6.44 m/m², total conflict nodes from 186 to 109, and the conflict intensity index from 5.85 to 3.41. The average conflict occurrence probability decreased from 55.2% to 33.8%, the rework cost ratio declined from 9.6% to 4.1%, the comprehensive investment cost index decreased by 10.7%, and the unit-area investment return index increased by 12.6%. The results demonstrate that the proposed model can support reproducible MEP spatial optimization and quantitative investment-risk control.

Abstract: Residential courtyards in Saharan cities face extreme summer thermal conditions that render them physiologically unusable for much of the day, yet their performance under standardized promotional-housing morphologies remains poorly documented. This study characterizes the summer environmental quality of a representative R+3 courtyard in Laghouat, Algeria (Köppen BWk), combining in situ measurements at five contrasting stations with ENVI-met simulation across six output maps. All five stations remained in the EXTREME physiological-stress category (PET = 51.1–52.3°C), with a peak thermal sur-plus of +4.87 to +5.97°C and minimum relative humidity of 9.26–10.35%. Sky view factor correlated strongly with thermal amplitude (r = 0.936) and shading with both humidity (r = 0.977) and comfort (PET, r = −0.887); given the small sample (n = 5), these associations are indicative rather than conclusive. A central finding is a vegetation paradox: the vege-tated, unshaded station recorded the highest thermal surplus and lowest humidity, con-sistent with stomatal closure under extreme aridity (VPD ≈7 kPa), while a shaded, vege-tated station performed best—indicating that shading, not vegetation per se, is the prima-ry regulator of courtyard microclimate here. ENVI-met simulation corroborates a 9.5°C surface-temperature differential between bare asphalt and shaded vegetated ground, in-forming bioclimatic redesign of Saharan promotional housing.

Abstract: Determining the construction age of vernacular buildings is essential for the conservation and documentation of historical and cultural heritage. Traditional approaches, however, rely heavily on questionnaire surveys and expert judgment, which are both inefficient and highly subjective. To overcome these limitations, this paper presents an automatic method for predicting construction age based on machine learning and architectural facade image features. First, we visited 29 villages in the Dezhou region of China and compiled 630 vernacular building cases, creating a facade image dataset that spans multiple periods and architectural styles, with construction ages labeled as time intervals. Random forest and decision tree models were then introduced to identify the core factors influencing age determination from a wide range of facade features and to establish their quantitative criteria. The results reveal that wall finishing materials, the presence of sunrooms, window materials, and wall body materials are the core factors affecting the judgment of construction age. Based on these factors, a decision tree model was constructed for age determination. This model achieved an accuracy of 98.92% on the test set, with both precision and recall exceeding 99% and an F1 score of 0.992, demonstrating the effectiveness and robustness of the proposed quantitative classification system. The method offers a highly interpretable and accurate technical pathway for identifying the age of vernacular architectural heritage.

Abstract: Green building certification systems such as LEED, BREEAM, EDGE, and WELL have become central instruments for steering the construction sector toward measurable environmental and social performance. Their uptake, however, remains highly uneven across the developing world, and almost nothing is known about their relevance to construction markets in Central Africa. This study examines the applicability of established green building certification frameworks to Chad's construction sector, a market characterized by rapid urbanization, import-dependent materials, and an emerging regulatory environment for sustainability. Combining a narrative and comparative review of certification systems with contextual grounding from a previously reported survey of 79 Chadian architects and engineers (Dagou et al., 2025), the study assesses the environmental, economic, institutional, and social fit of each framework against Chad's specific conditions. The results indicate that the International Finance Corporation's EDGE system, designed explicitly for emerging markets, offers the most immediately viable entry point, while LEED, BREEAM, and WELL remain constrained by cost, assessor availability, and the absence of a national green building code. Regional experience from Tanzania, Ghana, Burkina Faso, and South Africa is used to identify transferable lessons. The paper proposes a phased, contextually adapted framework for green building adoption in Chad, built around institutional groundwork, pilot certification in donor-funded projects, and the eventual development of a locally calibrated rating tool. The findings offer a starting point for policymakers, financiers, and practitioners seeking to align Chad's construction sector with global sustainability standards without importing frameworks that do not fit local realities.

Abstract: This study presents a field-based, empirically grounded investigation into the spatiotemporal dynamics and indoor–outdoor (I/O) coupling mechanisms of PM₂.₅ in residential buildings across North China. Concurrent high-resolution (10-min interval) measurements of indoor and outdoor PM₂.₅ mass concentrations were conducted from April to December 2025 across six instrumented residential units—stratified by urban/rural setting, building age, heating infrastructure, and envelope integrity—to capture representative heterogeneity in exposure contexts. Data were acquired using calibrated β-attenuation monitors (BAM-1020, ±2.5 µg/m³ accuracy) with integrated temperature/humidity compensation, and synchronized via GPS time-stamping to ensure temporal alignment.Statistical analysis employed rigorous inferential methods: paired t-tests (α = 0.001, two-tailed) confirmed statistically significant concentration disparities between indoor and outdoor environments (p < 0.001 for all sites), rejecting the null hypothesis of I/O equilibrium. Linear regression modeling (R², slope, intercept, residual diagnostics) quantified infiltration-driven coupling strength, while Pearson correlation coefficients (r) and associated p-values assessed monotonic dependence under varying operational conditions. The observed I/O ratio spanned 0.674–2.673, reflecting pronounced building-specific modulation of infiltration and source dominance. Critically, under window-open conditions, residences with active indoor sources (e.g., cooking, incense burning, biomass space heating) exhibited mean I/O ratios >1.0 (1.32 ± 0.18), whereas under window-closed conditions—where infiltration is suppressed—the same units registered I/O <1.0 (0.87 ± 0.11), indicating net indoor generation outweighing penetration loss. In contrast, source-free dwellings maintained strong linear I/O correlation (r = 0.89–0.95, p < 0.001) across both ambient and haze episodes (PM₂.₅ > 150 µg/m³), with regression slopes consistent with empirically derived infiltration factors (0.62–0.78). Conversely, source-active units displayed statistically significant negative Pearson correlations (r = −0.41 to −0.63, p < 0.001) during source events—demonstrating dynamic decoupling wherein indoor concentrations diverge inversely from outdoor trends due to dominant internal emission fluxes.

Abstract: Traditional educational assessment systems have primarily relied on episodic, institutionally confined models, such as examinations, grades, and static credentials, that capture isolated learner performance. However, learning is a continuous process increasingly distributed across digital platforms, workplaces, collaborative networks, and AI-mediated environments, highlighting a widening gap between learning processes and traditional assessment practices. Recent advances in artificial intelligence, learning analytics, multimodal analytics, learner modeling, and semantic interoperability now support scalable interpretation of varied learning evidence across contexts and over time. This paper introduces the AI-Mediated Continuous Assessment Infrastructure (AIM-CAI), a socio-technical framework for continuously inferring learner competencies from distributed evidence generated in educational, professional, and digital domains. AIM-CAI reconceptualizes assessment as a continuous, probabilistic process, moving beyond isolated evaluative events. The framework integrates distributed evidence systems, evidence serialization mechanisms, semantic translation layers, probabilistic learner models, dynamic competency profiles, and federated governance architectures to support scalable interpretation of learning while preserving privacy, accountability, and learner agency. The paper examines how continuous assessment can transform credentialing, lifelong learning, institutional roles, interoperability, and governance in AI-mediated education. It further outlines a research agenda to address key psychometric, ethical, and governance challenges, including validity, algorithmic bias, surveillance risks, semantic instability, and ownership of learning evidence.

Abstract: To achieve the recast Energy Performance of Buildings Directive targets and overcome slow, costly on-site renovation practices, off-site prefabrication is vital, yet existing research often neglects real-world applicability and circularity constraints. This study presents a systematic market mapping and a three-category taxonomy—Single-function envelopes, Multifunctional integrated systems, and Stand-alone installations—of prefabricated European renovation solutions. Applying a structured three-step protocol screening literature, industry reports, and EU projects, technologies were evaluated through a multidimensional framework capturing maturity, functional integration, structural constraints, and circularity indicators. Results reveal a strongly polarized market where passive envelope systems dominate, while multifunctional integrated solutions remain confined to prototype stages. Furthermore, most systems target low-to-mid-rise residential buildings, show limited compatibility with complex geometries, and exhibit underdeveloped circularity due to conventional material reliance. This study concludes that bridging the research-to-market gap requires system interface standardization, demand stimulation through Green Public Procurement, and regulatory adaptation for increased envelope thickness. Ultimately, this taxonomy provides an operational assessment framework and establishes a rigorous foundation for a future quantitative Prefabrication Readiness Index (PRI).

Abstract: Despite conventional approaches to incorporating green spaces into urban areas, carbon emissions persist, posing risks to the realisation of Sustainable Development Goals (SDGs) 11 and 13, which are linked to sustainable cities and climate action. This study explores the integration of Artificial Intelligence (AI) technologies to enhance urban green infrastructure (UGI) for carbon neutrality in smart buildings. A phenomenological qualitative research approach was adopted in this study. The purposively and snowball-sampled data from 30 stakeholders comprising architects, planners, engineers, and information and communication experts from Lagos, Abuja, and Kano via a Google Form questionnaire and virtual interview attained saturation at the 28th participant. The data extracted were manually analysed and thematically presented. The results revealed that AI predicts maintenance and optimises energy in smart buildings, and monitors UGI. While deficient skills, financial constraints, and poor regulations were identified as challenges, incentives, public-private collaborations, and inter-professional training were advocated as initiatives. To actualise AI-enabled green design and carbon management in smart buildings, the researchers adopted the Technology Acceptance Model (TAM), the Technology-Organisation-Environment (TOE) framework, and the Triple Helix Model (THM). The study offers insights for built environment experts and policymakers on how to leverage AI to harness UGI potential in smart buildings, mitigate the carbon footprint, and foster sustainable cities.

Abstract: Translating concepts into a design product is among the most difficult challenges in the architecture design process. This is chiefly attributable to the clarity of the concepts in the designer's mind and their ability to convert them into a visual manifestation. Current AI tools offer efficient methods for transforming text into images, facilitating designers in swiftly seeing their concepts as tangible representations while developing design alternatives. This study introduces a ten-step numerical procedure for assessing the richness of textual prompts submitted to text-to-image generative AI tools within an architectural design studio. Twenty-three architecture students submitted prompts as part of a design assignment requiring AI-assisted conceptual visualisation. Each prompt was scored across seven weighted dimensions (subject specificity, style and medium, composition and framing, lighting and atmosphere, colour and palette, quality modifiers, and negative clauses) to produce a composite Prompt Richness Index (R, scale 0-100). Corresponding AI-generated images were independently scored using a parallel Output Richness Index (O, scale 0-100). Pearson correlation between per-student average R and O yielded r = 0.940 (p < 0.001), confirming a nearly perfect positive linear relationship. Rich-tier prompts were produced by two students and yielded the most architecturally coherent and visually distinctive outputs. Four students produced Sparse-tier prompts, consistently receiving undifferentiated, generically rendered outputs. Class-wide deficits were identified in lighting/atmosphere description and negative clause usage. Seven pedagogical recommendations are derived from the findings to guide prompt learning instruction in AI-integrated design studios.

Abstract: This paper investigates the use of parametric inverted chain models (ICMs) as pedagogical tools for teaching structural design to architecture students. A mixed-method action research study was conducted at the University of Belgrade – Faculty of Architecture through an educational intervention that integrated analog and digital form-finding within a learning-by-doing framework. The intervention combined physical hanging-chain models with computational simulations using Spider3D, a custom Grasshopper plugin developed for ICM simulation. Data collected through surveys, knowledge assessments, focus-group discussions, reflective journals, and instructor observations indicate that the approach improved students’ understanding of structural behavior, equilibrium forms, and the relationship between form and force. Participants reported increased confidence in structural reasoning, greater engagement with structural design concepts, and a stronger appreciation of structure as a generator of architectural form. The combination of physical and computational workflows enabled students to develop both intuitive and analytical perspectives on structural performance. The study demonstrates that parametric ICMs can function not only as form-finding tools but also as effective pedagogical instruments that support experiential learning, structural intuition, and creative exploration. The findings highlight the potential of integrating research-led digital tools into architectural education to strengthen the connection between structural design, computation, and creative practice.

Abstract: Climate-adaptive façade design is essential for reducing residential energy demand while maintaining thermal acceptability across diverse climate conditions. However, many optimization studies remain limited to single climates or present Pareto outputs without translating them into practical design rules. This study develops an explainable simulation-based framework for deriving robust façade design rules for energy-efficient, low-carbon-oriented residential buildings across eight global climate contexts. A standardized five-story residential apartment prototype with a gross floor area of 2240 m² was modeled in DesignBuilder using the EnergyPlus 9.4 simulation engine. The study covered Abu Dhabi, Athens, Berlin, Miami, Phoenix, Riyadh, Singapore, and Stockholm. Four façade variables were assessed: window-to-wall ratio, orientation, external shading depth, and glazing type. NSGA-II optimization was applied with a population size of 40 and 30 generations per city, producing approximately 1200 evaluations per climate context and about 9600 simulations in total. The optimization minimized energy use intensity and ASHRAE 55 discomfort hours. Random Forest models and SHAP analysis were then used to identify dominant performance drivers and support rule extraction. The optimized solutions reduced energy use intensity by 44.63%–60.19% and discomfort hours by 16.71%–49.71% relative to the baseline cases. Random Forest models achieved high predictive accuracy, with R² values of 0.9942 for energy performance and 0.9954 for comfort. Aggregated feature importance showed that climate context was the dominant determinant of performance, accounting for 45.1% of energy-model importance and 69.6% of comfort-model importance. Among façade variables, window-to-wall ratio was the strongest design driver, while orientation contributed only 0.5% in both models. The results show that robust façade design cannot rely on universal prescriptions. Hot-arid climates favored low glazing ratios, high-performance glazing, and external shading, while temperate and cold climates allowed larger glazing areas with efficient glazing and limited shading. The proposed framework converts simulation, optimization, and explainable machine-learning outputs into practical climate-adaptive façade rules for early-stage energy-efficient and low-carbon-oriented residential design.

Abstract: This study presents a multi-method, high-fidelity investigation into the thermal comfort performance of a window-type direct evaporative cooling (December) air-conditioning system installed in a student dormitory (Room 210, Building No. 1) at a university in Beijing. Conducted over a representative summer period (June 2025), the research integrates in situ physical measurements, standardized subjective questionnaire surveys (n = 198), and advanced computational thermal physiology modeling using ISO 7730–2021 and ASHRAE Standard 55–2023 frameworks. Environmental parameters—including dry-bulb temperature (t_a), relative humidity (RH), air velocity (v_a), and mean radiant temperature (t_r)—were monitored at eight spatially distributed points for 3 hours (12:00–15:00) with 1-minute resolution. Concurrently, clothing ensemble, activity level, and subjective thermal sensation votes (TSV) were collected via validated questionnaires aligned with ISO 10551 and ANSI/ASHRAE Standard 55 Annex B. The measured data served as input to a custom FORTRAN-based simulation platform implementing Fanger’s two-node thermoregulatory model, enabling deterministic calculation of the Predicted Mean Vote (PMV), Predicted Percentage Dissatisfied (PPD), Effective Temperature (ET*), and Standard Effective Temperature (SET*). Results demonstrate that the December unit achieved a stable outlet temperature depression of Δt = 8.48°C (inlet: 31.46°C; outlet: 22.98°C) with a wet-bulb efficiency of 59.6%, reducing indoor ta from ambient 31.5°C to a mean of 27.68°C while maintaining RH at 41.68%—a critical achievement given Beijing’s low summer humidity. The paper concludes with evidence-based design recommendations for December deployment in Northern Chinese educational buildings, emphasizing its energy-saving potential (32% lower electricity use), health advantages (no refrigerants, zero ozone depletion potential), and critical operational constraints (performance degradation above 65% RH). It provides a granular, academically rigorous critique of methodological, climatic, behavioral, and physiological limitations, establishing a definitive roadmap for future research.

Abstract: The elastoplastic behaviour and failure of unreinforced masonry structures under biaxial loading are critically governed by the mechanical response of brick-mortar contact interfaces. Detailed finite element micromodelling explicitly resolves brick units, mortar joints, and contact interfaces, offering rigorous numerical representation; however, practical implementation requires the determination of contact stiffness parameters. This study presents an experimental-numerical calibration methodology for these parameters, applied to a representative ceramic masonry system. The methodology integrates experimental characterisation of constituent materials and small-scale masonry specimens with numerical validation in Abaqus using a concrete damaged plasticity model for quasi-brittle materials and traction-separation laws for interfaces. Experimentally-calibrated formulations relating contact stiffness to interface geometry are proposed. Numerical simulations reproduce experimental behaviour with peak load predictions within ±6% for normal and ±1% for shear loading. Detailed micromodelling reveals that normal stresses develop at interfaces even under nominally pure shear, evidencing coupled normal-tangential behaviour, the key role of normal adhesive contact strength, and the justification for the cohesive-frictional interface characterisation.

Abstract: This research addresses the reception and variations of the Modern Movement in the French Caribbean, focusing on the urban transformation of Fort-de-France, Martinique, between 1927 and 1986. Through a critical methodology based on decentralized perspectives and “situated gazes,” this study challenges traditional historiographical narratives that often subordinate tropical architecture to mere simplified derivatives of European metropolises. Utilizing the development and implementation of the ARMOMA platform (Architecture du Mouvement Moderne en Martinique)—a specialized digital archive and cartographic database—this work documents, spatially visualizes, and analyzes the region’s modern heritage. Methodologically, it examines a specific sample of 77 state-owned buildings divided into three key typologies—educational (34), institutional (30), and sanitary (13)—extracted from a wider urban universe of 332 modern works, evaluating their spatial relations with 171 residential architectures. The results demonstrate that these public infrastructures were not mere replicates of Western models but acted as an active mirror, generating a vernacular tropical modernism successfully adapted to local climatic, economic, and cultural realities. In conclusion, the study highlights how the French state strategically utilized these architectural typologies to stage its political power and institutionalize the Welfare State after Martinique became an Overseas Department in 1946, culminating a distinct period of administrative modernization that concluded with the Decentralization Laws of 1982–1983.

Abstract: This study presents an explainable artificial intelligence framework for climate-responsive residential envelope design in Gulf coastal cities by integrating building performance simulation, multi-objective optimization, machine learning, and explainability analysis. While previous studies have largely focused on minimizing energy consumption, limited research has simultaneously considered energy performance, capital cost, and thermal comfort within a unified and interpretable decision-support framework. The objective of this research was to identify dominant envelope design variables and derive practical design recommendations for residential buildings located in Dubai, Doha, and Manama. A two-story detached villa prototype was developed and simulated under representative coastal hot-arid climate conditions. Six envelope and operational design variables, including window-to-wall ratio (WWR), shading depth, cooling setpoint, glazing type, wall construction, and roof construction, were evaluated through a simulation-based optimization framework. A total of 600 design alternatives were generated using NSGA-II optimization and subsequently used to train Random Forest predictive models for energy use intensity (EUI), capital cost, and ASHRAE 55 thermal discomfort hours. SHAP (Shapley Additive Explanations) analysis was then applied to quantify variable importance and extract interpretable design rules. The results demonstrated strong predictive capability, with Random Forest models achieving R² values of 0.933 for EUI, 0.982 for capital cost, and 0.955 for thermal discomfort. SHAP analysis revealed that WWR was the dominant driver of energy performance, accounting for 65.2% of total feature importance, while wall construction exerted the greatest influence on capital cost. Thermal comfort was primarily governed by cooling setpoint, followed by WWR and shading depth. Dependence analysis further identified clear threshold relationships between envelope variables and performance outcomes. The proposed framework transforms optimization datasets into actionable design knowledge and provides interpretable decision support for architects, consultants, and developers seeking cost-effective and climate-responsive residential envelope solutions in Gulf coastal environments.

Abstract: This study is anchored in the quest to systematically chart the academic territory of digital twin technology applications in smart city development. It aims to elucidate the extent and focus of scholarly discourse, pinpointing key thematic developments and assessing their evolution over time. This study conducts a bibliometric analysis using literature focusing on the period 2018–2024 of research on digital twins and smart cities. The most frequently used Scopus database was used to extract bibliometric data. 422 ar-ticles were considered for analysis. This study utilizes co-authorship, co-occurrence, citation analysis, and bibliographic coupling of author keywords while graphically mapping the bibliographic material using BiblioMagika software. This study identified the authors, institutions and countries that publish the most globally on the topic of Digital Twins and Smart Cities. The results revealed that most of the published articles come from China. The other highly ranked countries by origins of studies on Digital Twins and Smart Cities were found to be the USA and Italy all of which are developed nations or economies. The study makes significant and emergent contributions by building on the research area as well as providing a reference point for the interpretation of findings, as well as directions for future research.

Abstract: With urbanization resulting in increased demand for indoor comfort, HVAC (heating, ventilation, and air-conditioning) systems, particularly air handling units (AHUs), are essentials for indoor climate control. The advent of big data and artificial intelligence (AI) have opened new avenues for enhanced safety and reliability in HVAC operations. Hence, this study focused on the predictive performance evaluation of AHUs, which is receiving less attention compared to its fault detection and optimal control issues. Utilizing real-time operational data from Oak National Laboratory, the proposed model employs multi-task learning (MTL) to refine prediction accuracy for AHU return air properties, including temperature, moisture content, and power consumption. This is achieved without allowing any single task to dominate others during the training phase. Moreover, the model introduces an ensemble approach that synergizes the capabilities of the different MTL algorithms using a boosting technique via gradient boosting regression tree (GBRT). This novel strategy has demonstrated superiority over conventional data-driven approaches in terms of performance. The paper culminates by showcasing the significant role of the proposed model as a metric for AHU performance evaluation and its contribution to smart decision-making in a real-world context. In essence, the developed model is poised to facilitate optimal decision-making regarding HVAC components and foster proactive strategies to ensure consistent operation and extend the lifespan of HVAC systems.

Abstract: During the conceptual design phase, detailed geometric models are often unavailable, hindering energy-driven decisions for nearly zero-energy buildings. This paper proposes a geometry simplification strategy for office buildings in hot-humid regions using only length, width, and number of stories. Based on 130,976 geometric parameter combinations, a standardized rectangular energy model is built, and EnergyPlus simulates orientations from 0° to 179° (1° step), totaling 23.6 million runs. Three simplification methods are compared: aspect ratio, floor area, and the proposed length-width combination. The length-width combination method achieves the lowest average relative deviation (6.88%), outperforming aspect ratio (8.41%) and floor area (6.91%), thus meeting conceptual design accuracy requirements. Using this simplified model, the optimal orientation is identified as 0° (true south-north), accounting for 83.14% of cases. The orientation range 39°–86° should be avoided, as it contains over 99% of worst-case orientations. The proposed strategy enables rapid energy estimation and orientation guidance from basic parameters, shifting energy-efficient design from late verification to early-stage driving, and providing quantifiable support for conceptual design of nearly zero-energy buildings in hot-humid regions.

Abstract: Buildings account for a substantial share of global energy consumption and greenhouse gas emissions, creating an urgent need for design strategies that simultaneously address operational performance, occupant comfort, and life-cycle environmental impacts. While simulation-based optimization has become increasingly common in building performance research, relatively few studies evaluate energy use, thermal comfort, and embodied carbon within a unified tri-objective framework. This study presents a simulation-based tri-objective Pareto optimization of residential buildings in Riyadh, Saudi Arabia, and Dubai, United Arab Emirates, using DesignBuilder, EnergyPlus, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). A standardized four-story residential apartment prototype comprising 16 thermal zones and 2239.82 m² of conditioned floor area was developed and simulated under identical geometric, operational, and HVAC assumptions. Window-to-wall ratio, glazing type, external shading depth, and cooling setpoint temperature were optimized to minimize annual site energy consumption, ASHRAE 55 thermal discomfort hours, and embodied carbon emissions. Baseline simulations revealed substantially higher operational demand in Dubai, with annual energy consumption reaching 272,077 kWh compared with 196,478 kWh in Riyadh, while discomfort hours increased from 2,530 h/year to 3,262 h/year. Optimization reduced annual energy demand by 72.9% in Riyadh and 74.5% in Dubai, while thermal discomfort was reduced to 776 h/year in the best-performing comfort solution. Pareto-optimal solutions consistently favored low window-to-wall ratios (10–16%), high-performance glazing, and external overhangs between 1.5 and 2.0 m. The findings demonstrate the effectiveness of tri-objective optimization for balancing operational efficiency, occupant comfort, and embodied carbon while providing climate-responsive façade design guidance for residential buildings in hot-arid Gulf environments.