Search Results (1,509)

Manual window cleaning in high-rise urban buildings is labor-intensive, risky, and resource-inefficient. This study addresses these challenges by investigating a resource-aware mechatronic architecture through the design, development, and experimental validation of a modular Automated Window Cleaning System (AWCS). Unlike conventional open-loop solutions, the AWCS integrates mechanical scrubbing with a closed-loop fluid management system, featuring precise dispensing and vacuum-assisted recovery. The system is governed by a deterministic finite state machine implemented on an ESP32 microcontroller, enabling low-latency IoT connectivity and autonomous operation. Two implementation variants—integrated and retrofit—were validated to ensure structural adaptability. Experimental results across 30 cycles demonstrate a cleaning efficiency of ~2 min/m², a water consumption of <150 mL/m² (representing a >95% reduction compared to manual methods), and an optical cleaning efficacy of 96.9% ± 1.4%. Safety protocols were substantiated through a calculated mechanical safety factor of 6.12 for retrofit applications. This research establishes the AWCS as a sustainable, safe, and scalable solution for autonomous building maintenance, contributing to the advancement of resource-circular domestic robotics and smart home automation. Full article

Existing office buildings in Australia contribute to 24% of the nation’s electricity consumption and 10% of greenhouse gas emissions, with energy use projected to rise by 84%. Meeting the 2050 sustainability target and United Nations (UN) 17 Sustainable Development Goals (SDGs) requires improving sustainability within existing office buildings. This systematic review examines net zero energy and climate resilience strategies in these buildings by analysing 74 studies from scholarly literature, government reports, and industry publications. The literature search was conducted across Scopus, Google Scholar, and Web of Science databases, with the final search in early 2025. Studies were selected based on keywords and research parameters. A narrative synthesis identified key technologies, evaluating the integration of net zero principles with climate resilience to enhance energy efficiency through HVAC modifications. Technologies like heat pumps, energy recovery ventilators, thermal energy storage, and phase change materials (PCMs) have been identified as crucial in reducing HVAC energy usage intensity (EUI). Lighting control and plug load management advancements are examined for reducing electricity demand. This review highlights the gap between academic research and practical applications, emphasising the need for comprehensive field studies to provide long-term performance data. Current regulatory frameworks influencing the net zero transition are discussed, with recommendations for policy actions and future research. This study links net zero performance with climate adaptation objectives for existing office buildings and provides recommendations for future research, retrofit planning, and policy development. Full article

Cool roof and green roof have been acknowledged as effective heat mitigation strategies for fighting against the urban heat island (UHI). However, empirical data in hot–humid climate are still insufficient. Experimental conventional, cool and green roofs (three types) were established to comprehensively investigate the thermal performances in Hong Kong under typical summer conditions, as retrofitting strategies for an office building. The holistic vertical thermal behavior was investigated. The comparative cooling potentials were assessed. The results reveal a “vertical thermal sequence” in peak temperatures of each substrate layer for the conventional, cool and green roofs on a sunny day. However, local reversion in the thermal sequence may occur on a rainy day. Green roof-plot C (GR_C) demonstrates the highest thermal damping effect, followed by plot B (GR_B), A (GR_A) and the cool roof (CR) in summer. On a sunny day, the thermal dampening effectiveness of the substrates in the three green roofs is consistent: drainage > soil > water reservoir > root barrier. The holistic vertical thermal profiling was constructed in a high-rise office context in Hong Kong. The diurnal temperature profiles indicate all roof systems could effectively attenuate the temperature fluctuations. The daily maximum surface temperature reduction (SDMR) was introduced for cooling potential characterization of the cool roof and green roofs with multiple vegetation types. On a sunny day, the cool roof and green roofs all showed significant cooling potential. SDMR on the concrete tile of the best performing system was GR_C (26 °C), followed by GR_B (22.4 °C), GR_A (20.7 °C) and CR (13.3 °C), respectively. The SDMR on the ceiling ranked as GR_C, GR_B, GR_A and CR, with 2.9 °C, 2.4 °C, 2.1 °C and 2.1 °C, separately. On a rainy day, the cooling effect was still present but greatly diminished. A critical insight of a “warming effect at the ceiling” of the green roof was revealed. This research offers critical insights for unlocking rooftop cooling potential, endorsing cool roof and green roof as pivotal solutions for sustainable urban environments. Full article

This study examines the environmental and economic impacts of transitioning RORO Pax ships from diesel to green and blue hydrogen fuel, focusing on the Jazan case study vessel. It evaluates the environmental and economic effects for both retrofitted and new vessels. Findings reveal that hydrogen-powered PEMFC engines achieve a 99.13% reduction in NOx emissions and reduce both SO_x and CO₂ emissions to minimum values. The analysis indicates that retrofitting with blue hydrogen can achieve a lifetime emission reduction of approximately 134 kton, yielding a net benefit of USD 4.46 per ton of emissions reduced. Newbuilding options present a more favorable financial profile at USD 19.31 per ton, surpassing green hydrogen’s USD 16.61 per ton. The study highlights the economic infeasibility of retrofitting existing vessels due to insufficient operational life, while hydrogen fuel becomes viable for sustainable new builds after 6 to 10 years, potentially resulting in annual cost savings of USD 2 to USD 3 million and competitive hydrogen production costs of up to USD 0.30 per kWh. Full article

This follow-up study investigates the long-term environmental sustainability and remediation outcomes of the UPPER (‘Urban Productive Parks for Sustainable Urban Regeneration’-UIA04-252) project in Latina, Italy, focusing on Nature-Based Solutions (NbS) applied to urban green infrastructure. By integrating proximal and satellite-based remote sensing methodologies, the research evaluates persistent improvements in vegetation health, soil moisture dynamics, and overall environmental quality over multiple years. Building upon the initial monitoring framework, this case study incorporates updated data and refined techniques to quantify temporal changes and assess the ecological performance of NbS interventions. In more detail, ground-based data from meteo-climatic, air quality stations and remote satellite data from the Sentinel-2 mission are adopted. Ground-based measurements such as temperature, humidity, radiation, rainfall intensity, PM10 and PM2.5 are carried out to monitor the overall environmental quality. Updated satellite imagery from Sentinel-2 is analyzed using advanced band ratio indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Moisture Index (NDMI). Comparative temporal analysis revealed consistent enhancements in vegetation health, with NDVI values significantly exceeding baseline levels (NDVI 2022–2024: +0.096, p = 0.024), demonstrating successful vegetation establishment with larger gains in green areas (+27.0%) than parking retrofits (+11.4%, p = 0.041). However, concurrent NDWI decline (−0.066, p = 0.063) indicates increased vegetation water stress despite irrigation infrastructure. NDMI improvements (+0.098, p = 0.016) suggest physiological adaptation through stomatal regulation. Principal Component Analysis (PCA) of meteo-climatic variables reveals temperature as the dominant environmental driver (PC2 loadings > 0.8), with municipality-wide NDVI-temperature correlations of r = −0.87. These multi-scale findings validate sustained NbS effectiveness in enhancing vegetation density and ecosystem services, yet simultaneously expose critical water-limitation trade-offs in Mediterranean semi-arid contexts, necessitating adaptive irrigation management and continued monitoring for long-term urban climate resilience. The integrated monitoring approach underscores the critical role of continuous, multi-scale assessment in ensuring long-term success and adaptive management of NbS-based interventions. Full article

Existing buildings consume a large portion of the total current energy production, especially in developing countries such as Egypt. Increasing energy demand, coupled with decreasing availability and increasing cost of conventional non-renewable energy resources, have encouraged a “building green” retrofit trend in order to maximize the energy performance of the built environment. This paper outlines the development of an Energy Retrofit Decision-Support System (ERDSS) for hot, arid climates that models building retrofit scenarios and determines the impact of each retrofit measure on the overall energy consumption of a proposed building retrofit program. The methodology combines building an energy simulation with a database-driven, budget-constrained optimization framework based on the Savings-to-Investment Ratio (SIR) to evaluate and prioritize retrofit measures. In addition, ERDSS determines the impact of each retrofit measure on the overall energy consumption of a proposed building retrofit program, ranks the retrofit measures according to their Savings-to-Investment Ratio (SIR) and uses optimization to develop a suggested retrofit program for a given budget. ERDSS is applied on a case study of an education building in New Cairo, Egypt, in order to illustrate the performance of the framework. Results show that savings for the commissioned retrofit, standard retrofits, and deep retrofits reached 15 percent, 35 percent, and 45 percent, respectively. Full article

This study addresses the challenge of rapid identification and assessment of localized damage to building envelopes under resource-constrained conditions—specifically, the absence of specialized inspection equipment—with a particular focus on the detrimental effects of such damage on thermal performance and energy efficiency. An efficient detection methodology tailored to small-scale maintenance scenarios is proposed, leveraging the YOLOv11 object detection architecture to develop an intelligent system capable of recognizing common envelope defects in contemporary residential buildings, including cracks, spalling, and sealant failure. The system prioritizes the detection of anomalies that may induce thermal bridging, reduced airtightness, or insulation degradation. Defects are classified according to severity and their potential impact on thermal behavior, enabling a graded, integrated repair strategy that holistically balances structural safety, thermal restoration, and façade aesthetics. By explicitly incorporating energy performance recovery as a core objective, the proposed approach not only enhances the automation of spatial data processing but also actively supports the green operation and low-carbon retrofitting of existing urban building stock. Characterized by low cost, high efficiency, and ease of deployment, this method offers a practical and scalable technical pathway for the intelligent diagnosis of thermal anomalies and the enhancement of building energy performance. It aligns with the principles of high-quality architectural development and sustainable building governance, while concretely advancing operational energy reduction in the built environment and contributing meaningfully to energy conservation goals. Full article

Base isolation is known as a useful and popular technique for seismic upgrading of reinforced concrete buildings. Predicting damage levels based on relative inter-story drift plays an important role for designing optimal base isolation systems. However, the existing codes usually rely on the acceleration spectrum for calculating the relative inter-story drift, and they do not provide an accurate estimation of the relative inter-story drift. Consequently, to cover the research gap, machine learning algorithms are being trained and used for identification of damage levels in retrofitted reinforced concrete buildings. More than 7000 datasets were derived by using nonlinear time-history and incremental dynamic analysis. A total of 48 reinforced concrete buildings with different stories and bay numbers were designed based on an older version of existing building codes, and then, base isolation systems were designed for the seismic retrofit. The machine learning algorithms used here were Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and an Artificial Neural Network. Based on the results, four of the mentioned algorithms have the capability of predicting the damage level with an accuracy of more than 85%, with the best performance being reached by extreme gradient boosting with an accuracy of 89%. Finally, the most important parameters affecting the damage levels of retrofitted reinforced concrete buildings were derived. Full article

The UK’s net-zero by 2050 commitment necessitates urgent housing sector decarbonisation, as residential buildings contribute approximately 17% of national emissions. Post-1950 construction prioritised speed over efficiency, creating energy-deficient housing stock that challenges climate objectives. Current retrofit policies focus primarily on technological solutions—insulation and heating upgrades—while neglecting broader sustainability considerations. This research advocates systematically integrating Circular Economy (CE) principles into residential retrofit practices. CE approaches emphasise material circularity, waste minimisation, adaptive design, and a lifecycle assessment, delivering superior environmental and economic outcomes compared to conventional methods. The investigation employs mixed-methods research combining a systematic literature analysis, policy review, stakeholder engagement, and a retrofit implementation evaluation across diverse UK contexts. Key barriers identified include regulatory constraints, workforce capability gaps, and supply chain fragmentation, alongside critical transition enablers. An evidence-based decision-making framework emerges from this analysis, aligning retrofit interventions with CE principles. This framework guides policymakers, industry professionals, and researchers in the development of strategies that simultaneously improve energy-efficiency, maximise material reuse, reduce embodied emissions, and enhance environmental and economic sustainability. The findings advance a holistic, systems-oriented approach, positioning housing as a pivotal catalyst in the UK’s transition toward a circular, low-carbon built environment, moving beyond isolated technological fixes toward a comprehensive sustainability transformation. Full article

This study investigates the integration of eco-design strategies in the energy renovation of mid-20th century heritage buildings, using the Antonio Rueda Residential Complex in Valencia (Spain) as a representative case study. The research addresses the reconciliation between heritage conservation and contemporary environmental objectives by evaluating the building in terms of its construction and current performance. The multidisciplinary working methodology consists of creating a BIM-based workflow (Revit + Autodesk Insight) to generate an analytical energy model, quantify Operational Carbon, and evaluate the impact of lighting inside the homes to simulate the impacts of the intervention strategies. This is justified as existing buildings are energy intensive and heavily dependent on fossil fuels, largely due to insufficient façade insulation, obsolete window systems, and limited solar protection. Nine refurbishment scenarios were developed, ranging from reversible improvements to the building envelope to volumetric extensions inspired by the principles of eco-design and circularity. Comparative simulations suggest that specific improvements could significantly reduce energy demand while remaining compatible with the architectural identity of the complex. Full article

Digital Twin technology is transforming how buildings are designed, operated, and optimized, serving as a key enabler of smarter, more energy-efficient, and sustainable built environments. By creating dynamic, data-driven virtual replicas of physical assets, Digital Twins support continuous monitoring, predictive maintenance, and performance optimization across a building’s lifecycle. This paper provides a structured review of current developments and future trends in Digital Twin applications within the building sector, particularly highlighting their contribution to decarbonization, operational efficiency, and performance enhancement. The analysis identifies major challenges, including data accessibility, interoperability among heterogeneous systems, scalability limitations, and cybersecurity concerns. It emphasizes the need for standardized protocols and open data frameworks to ensure seamless integration across Building Management Systems (BMSs), Building Information Models (BIMs), and sensor networks. The paper also discusses policy and regulatory aspects, noting how harmonized standards and targeted incentives can accelerate adoption, particularly in retrofit and renovation projects. Emerging directions include Artificial Intelligence integration for autonomous optimization, alignment with circular economy principles, and coupling with smart grid infrastructures. Overall, realizing the full potential of Digital Twins requires coordinated collaboration among researchers, industry, and policymakers to enhance building performance and advance global decarbonization and urban resilience goals. Full article

Tropical buildings face the coupled effects of four-high environmental factors, which accelerate thermal–humidity degradation, increase operational energy demands, and diminish building health attributes. This paper systematically integrates global research advancements to establish a theoretical framework for Tropical Low-Carbon Healthy Building Enclosures (TLHBEs) by linking materials, structures, and buildings across scales. It identifies three key scientific questions: (1) Establishing a multi-scale parametric design model that couples materials, structures, and architecture. (2) Elucidating experimental and simulated multi-scale equivalent relationships under the coupled effects of temperature, humidity, radiation, and salinity. (3) Design multi-objective optimization strategies balancing energy efficiency, comfort, indoor air quality, and carbon emissions. Based on this, a technical implementation pathway is proposed, integrating multi-scale unified parametric design, multi-physics testing and simulation, machine learning, and intelligent optimization technologies. This aims to achieve multi-scale parametric design, data–model fusion, interpretable decision-making, and robust performance prediction under tropical climatic conditions, providing a systematic technical solution to address the key scientific questions. This framework not only provides scientific guidance and engineering references for designing, retrofitting, and evaluating low-carbon healthy buildings in tropical regions but also aligns with China’s dual carbon goals and healthy building development strategies. Full article

Windows contribute 40–50% of envelope heat loss despite occupying only 1/8–1/6 of the surface area. Conventional frame retrofits rely on geometry optimization or cavity insulation yet remain limited by cost and invasiveness. This study introduces electrochemical polishing to reduce cavity surface emissivity of multi-cavity broken-bridge aluminum window frames to suppress radiative heat transfer, offering a non-invasive, low-cost retrofit strategy for existing building windows. Using a typical 75-series casement window, finite element analysis (MQMC) reveals that reducing cavity surface emissivity from 0.9 to 0.05 lowers frame U-values by 12.39–30.38% and whole-window U-values by 2.72–9.69%, with full-cavity treatment outperforming insulating-cavity-only by an average of 0.29 W/(m²·K). EnergyPlus simulations across multiple climate zones show 0.74–2.26% annual heating and cooling energy savings (with max reduction of 8.99 MJ/m²·yr) in severe cold and cold regions (e.g., Harbin, Beijing), but 1.25–3.04% penalties in mild and hot-summer zones due to impeded nighttime heat rejection. At an incremental cost of 62.5 CNY/window (6.6–7.4% increase), the static payback period is 4.1 years in Harbin. The approach mitigates thermal bridging more effectively than foam-filled frames in whole-window performance. This scalable, minimal-intervention technology aligns with low-carbon retrofit imperatives for existing aging windows, particularly in heating-dominated climates. Full article

This study investigates a Proxy-Calibration method for modeling Variable Refrigerant Flow (VRF) systems in TRNSYS, addressing the absence of a dedicated simulation component. The approach approximates part-load behavior through indoor-unit combination mapping, utilizing empirical data from a public office building in Seoul. Simulation results were compared with one year of monitored data. While indoor temperature trends showed moderate agreement (R² = 0.68), electricity consumption diverged significantly from actual measurements. The coefficient of variation in the root mean square error (CVRMSE) ranged from 95% to 118% for the boiler and 153% to 590% for the VRF system, indicating a substantial discrepancy well beyond standard calibration thresholds. These findings underscore the limitations of using static performance maps without explicit control logic. Consequently, this study defines the proposed method as an exploratory investigation; while it establishes a procedural framework for approximating VRF operation, rigorous energy prediction requires further refinement through empirical curve fitting and detailed control representation. Full article

The assessment of buildings’ energy performance plays a critical role in achieving global sustainability goals, particularly in reducing carbon emissions and improving energy efficiency. In this context, various modelling approaches have been developed to evaluate building energy performance. Among them, data-driven models, such as machine learning (ML) algorithms, have gained significant attention in recent years due to their scalability, fast development process, and high predictive accuracy. However, a key limitation of these models is their limited interpretability, which can negatively affect their application particularly in decision-making and retrofit planning processes. To address this issue, SHapley Additive exPlanations (SHAP) has emerged as a promising approach for interpreting complex ML models by quantifying the contribution of each input feature to the model’s predictions. As a result, this study developed an XGBoost ML model that predicts energy performance of residential buildings in the UK with an R² value of more than 0.98. After that, SHAP method was applied to explore and explain the effect of individual features on model outcomes, which highlighted that SHAP framework can be a strong complementary approach for enhancing the interpretability and practical applicability of black-box models in building energy performance analysis. Full article