Search Results (2,181)

One of the main obstacles to producing natural rubber-modified asphalt is the difficulty of mixing Technical Specification Natural Rubber (TSNR) or its compounds with asphalt, leading to long mixing times and high costs. This study aims to evaluate the use of 60/70 penetration asphalt as a plasticizer to accelerate the mixing process and improve the rheological properties of modified asphalt using Technical Specification Natural Rubber (TSNR). The production process for technical specification natural rubber-modified asphalt involves two stages: the production of the technical specification natural rubber compound (CTSNR) and the production of CTSNR-based modified asphalt (CTSNRMA). The CTSNR production process begins with mastication of technical specification natural rubber (TSNR), followed by the addition of activators (zinc oxide, stearic acid), accelerators (Mercaptobenzothiazole sulfenamide (MBTS)), antioxidants (2,2,4-Trimethyl-1,2-dihydroquinoline (TMQ)), and 60/70 penetration asphalt as a plasticizer (at concentrations of 30%, 40%, and 50%). After homogeneous mixing for 30–60 min, the CTSNR is diluted 5–10 mm for the next mixing stage with hot asphalt at 160–170 °C. The best results of this study showed that CTSNR-modified asphalt with 4% rubber content and 50% plasticizer (CTSNRM-450) successfully reduced the mixing time to 16 min, making it more efficient than the traditional method, which takes up to 180 min. The addition of asphalt plasticizer decreased penetration to 35.6 dmm and increased the softening point to 55.4 °C. The CTSNRMA-440 formula, with 4% rubber content and 40% plasticizer, produced the best results in terms of storage stability, meeting the ASTM D5892 standard with a softening-point difference of 0.95 °C, which is well below the threshold of 2.2 °C. The CTSNRMA-440 sample achieved a Performance Grade (PG) of 76, suitable for hot-climate conditions, with a significant reduction in mixing time, greater stability, and increased resistance to high temperatures. Full article

Resistance against water penetration is one of the key indicators of concrete durability in humid and pressurized environments. An intelligent model based on the XGBoost machine-learning algorithm was developed to predict the water penetration depth, using 1512 independent experimental measurements. The influential variables included water pressure, pressure duration, thermal cycles, fiber content, curing, and compressive strength. The investigated concrete specimens and field-tested structures in this study were exposed to arid and hot climatic conditions, and the proposed model was developed within this environmental context. To accurately simulate the water transport behavior, a cylindrical-chamber test was employed, enabling non-destructive and in-situ evaluation of structures. Correlation analysis revealed that compressive strength had the strongest negative influence (r = −0.598), while free curing exhibited the strongest positive influence (r = +0.654) on penetration depth. After hyperparameter optimization, the XGBoost model achieved the best performance (R² = 0.956, RMSE = 1.08 mm, MAE = 0.81 mm). Feature importance analysis indicated that penetration volume, pressure, and curing were the most significant predictors. According to the partial dependence analysis, both pressure and duration exhibited an approximately linear increase in penetration depth, while a W/C ratio below 0.45 and curing markedly reduced permeability. Microstructural interpretation using MIP, XRD, and SEM tests supported the physical interpretation of the trends identified by the machine-learning model. The results demonstrate that machine-learning-models can serve as fast and accurate tools for assessing durability and predicting permeability under severe environmental conditions. Finally, the permeability of several real structures was evaluated using the machine-learning approach, showing excellent prediction accuracy. Full article

Forest fire susceptibility prediction is essential for effective management, yet considerable uncertainty persists under future climate change, especially in climate-sensitive plateau regions. This study integrated MODIS fire data with climatic, topographic, vegetation, and anthropogenic variables to construct an Extreme Gradient Boosting (XGBoost) model for the Yunnan Plateau, a region highly prone to forest fires. Compared with Support Vector Machine and Random Forest models, XGBoost showed superior ability to capture nonlinear relationships and delivered the best performance, achieving an AUC of 0.907 and an overall accuracy of 0.831. The trained model was applied to climate projections under SSP1-2.6, SSP2-4.5, and SSP5-8.5 to assess future fire susceptibility. Results indicated that high-susceptibility periods primarily occur in winter and spring, driven by minimum temperature, average temperature, and precipitation. High-susceptibility areas are concentrated in dry-hot valleys and mountain basins with elevated temperatures and dense human activity. Under future climate scenarios, both the probability and spatial extent of forest fires are projected to increase, with a marked expansion after 2050, especially under SSP5-8.5. Although the XGBoost model demonstrates strong generalizability for plateau regions, uncertainties remain due to static vegetation, coarse anthropogenic data, and differences among climate models. 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

The urgent need for sustainable building design calls for advanced optimization methods that simultaneously address economic and environmental objectives, particularly those involving mixed discrete-continuous variables such as insulation material, heating source, and insulation thickness. While nature-inspired metaheuristics have shown promise in engineering optimization, their application to building envelope design remains limited, especially in handling discrete choices efficiently within a multi-objective framework. Inspired by the natural process of rainwater runoff and drainage basin dynamics, this study presents a novel hybrid approach integrating the Multi-Purpose Flow Direction Algorithm (MOFDA) with One-Hot Encoding to optimize external wall insulation. This bio-inspired algorithm mimics how water seeks optimal paths across terrain, enabling effective navigation of complex design spaces with both categorical and continuous variables. The model aims to minimize total lifecycle costs and CO₂ emissions across Türkiye’s six updated climatic regions. Pareto-optimal solutions are created using MOFDA, after which the Complex Proportional Assessment (COPRAS) method, weighted by Shannon Entropy, selects the most balanced designs. The results reveal significant climate-dependent variations: in the warmest region, the cost-optimal thickness is 3.3 cm (Rock Wool), while the emission-optimal reaches 17.3 cm (Glass Wool). In colder regions, emission-driven scenarios consistently require up to 40 cm insulation, indicating a practical limit of current materials. Under balanced weighting, fuel preferences shift from LPG in milder climates to Fuel Oil in harsher climates. Notably, Shannon Entropy assigned a weight of 88–92% to emissions due to their wider variability across the Pareto front, underscoring the environmental priority in data-driven decisions. This study demonstrates that the bio-inspired MOFDA framework, enhanced with One-Hot Encoding, effectively handles mixed discrete-continuous optimization and provides a robust, climate-aware decision tool for sustainable building design, reinforcing the value of translating natural flow processes into engineering solutions. Full article

Urbanization and climate change have intensified the urban heat island (UHI) effect, increasing the demand for sustainable cooling solutions. Greenery, particularly in urban settings, has gained attention as a passive design strategy to enhance urban thermal comfort. This study systematically reviews peer-reviewed literature published in the last decade to assess the effectiveness of greenery in mitigating urban heat. Using a precise selection process, studies indexed in Web of Science (WOS), ScienceDirect, and Scopus were analyzed to identify key findings, methodologies, and gaps in existing research. The results highlight the impact of green facades, green walls, and urban greenery on surface and air temperature reduction, energy efficiency, and microclimate regulation. Furthermore, the study examines variations in performance based on climate zones, vegetation types, and urban configurations. Findings suggest that while greenery significantly improves urban thermal comfort, further research is needed to standardize assessment methods and optimize implementation strategies. This review contributes to the growing body of knowledge on nature-based solutions and provides insights for policymakers, urban designers, and researchers aiming to integrate greenery into sustainable urban planning. Full article

Exposure to hot microclimate constitutes a serious threat to human health, especially in environments where collective protection measures cannot be implemented. Despite technological advances, personal cooling solutions remain insufficient for long-term use. Thermoelectric modules (TEMs) offer a promising pathway for developing cooling garments. This paper deals with temperature variations in a cooling set composed of a TEM and an evaporative heat sink, for different supply currents. A special methodology was adopted that included the use of a skin model placed in a climatic chamber, and temperature sensors that allowed temperatures at several points to be recorded. After 30 min of operation, the cold side temperature of the TEM was approximately 3 °C to 4.5 °C lower than when the heat sink was absent and the TEM was not supplied. This is close to what thermal comfort requires and may become too small for longer operation or less favourable climatic conditions. Enhanced heat dissipation from the hot side is therefore essential for enabling TEMs to function effectively in wearable colling systems, which makes research on heat sinks other than evaporative ones necessary. Full article

Machine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control—providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline–online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups. Full article

Energy efficiency in hospitals—where continuous operation with high internal gains and strict comfort needs—demands facade strategies tailored to climate. This study quantifies how the window-to-wall ratio (WWR) distribution and city-specific envelope properties affect the annual heating and cooling loads of a four-story, 3000 m² hospital in Turkey. Energy simulations were conducted using DesignBuilder (2021) with EnergyPlus under a controlled modeling framework, following ASHRAE healthcare guidelines for internal loads and TS 825:2024 for envelope compliance. Three locations were selected to span national variability: Bursa (Marmara—temperate/transition), Mersin (Mediterranean—hot–humid), and Kars (humid continental—cold). Scenario 1 (S1) assigned a graduated WWR on the south facade by floor—20%, 30%, 40%, and 50% from ground to top—while the north, east, and west facades were held at 20%, 30%, and 20%. Scenario 2 (S2) preserved the same geometry and WWR values but applied the graduated WWR to the north facade instead, keeping the south at 20%, east at 30%, and west at 20%. Within each city, opaque and glazing properties were kept constant across scenarios to isolate WWR–orientation effects. For every city–scenario combination, annual space-heating and space-cooling loads were computed, and window heat gains and losses were analyzed on the facade with variable WWR to support interpretation of performance mechanisms. The results indicate that S2 outperforms S1 in Mersin, S1 outperforms S2 in Kars, and S2 offers a moderate advantage in Bursa. Full article

Winter processes are increasingly recognised as important components of ecosystem carbon cycling, yet ¹³C-CO₂ fluxes from temperate forests and peatlands remain poorly quantified. This study quantified cold-season ¹³C-CO₂ fluxes in a Scots pine forest and a temperate fen in western Poland using manual closed chambers coupled with a Picarro G2201-i isotope analyser. Measurements were conducted during the cold half of the year and related to soil temperature, air temperature and, at the forest site, soil moisture. Median ¹³C-CO₂ fluxes were about twice as high in the forest (607 µg·m⁻²·h⁻¹) as in the fen (290 µg·m⁻²·h⁻¹), indicating stronger winter respiratory activity in the mineral soil than in the water-saturated peat. In the forest, ¹³C-CO₂ fluxes showed a weak, non-significant tendency to increase with temperature, whereas in the fen they were significantly negatively correlated with soil temperature and tended to peak near 0 °C, pointing to an important role of zero-curtain and freeze–thaw conditions. These plot-scale measurements provide rare constraints on winter ¹³C-CO₂ losses from temperate forest–peatland mosaics and highlight the need to represent cold-season isotopic fluxes in carbon–climate assessments. From a biogeochemical perspective, the findings emphasize that ¹³C losses during the cold season can occur as transient, high-intensity ‘hot moments’. Such episodic fluxes should therefore be explicitly incorporated into winter carbon accounting and isotopically enabled carbon–climate feedback assessments to improve the fidelity of annual net ecosystem exchange projections. Full article

Environmentally friendly sucrose-based poly(ester-urethane)s were synthesized and characterized, and their stability and degradation behavior were assessed under three different aging conditions: thermal, ultraviolet (UV), and hydrolytic treatment. The specimens underwent thermal treatment in both hot and cold climates to simulate a temperate continental climate. The samples were thoroughly characterized to assess chemical and structural changes (FT-IR, TGA, and DSC) and surface modifications (contact angle measurements and AFM and SEM analyses), providing insights into surface morphology and wettability alterations. Mechanical testing was also performed to evaluate the retention rate of the strength and the elongation after the aging process. The results showed that the introduction of sucrose into the main chain of the polyurethanes protected the ester and urethane groups from environmental degradation. The best stability in all three degradation environments was achieved by PCL-poly(ester urethane) due to its higher degree of crystallinity. PCL-based polyurethane exhibited a fracture strength retention rate exceeding 85% under all aging conditions, while the weight ratio remained practically unchanged after hydrolytic degradation. Thus, the obtained polyurethanes may support the advancement of sustainable, eco-friendly materials for future industrial applications. Full article

This study evaluates the operational performance and carbon emissions of an air-source heat pump (ASHP) system based on a one-year field monitoring campaign conducted at a single-family detached house in Gongju, South Korea. The system, equipped with a 9 kW air-to-water ASHP, supplied both space heating (SH) and domestic hot water (DHW), achieving average coefficients of performance (COPs) of 2.27 for SH and 2.06 for DHW. To estimate nominal COPs, a bi-quadratic regression model was developed using manufacturer catalog data and compared against field measurements. The analysis revealed a significant performance decline during winter: a paired t-test using 7119 samples confirmed a statistically significant discrepancy under low-temperature conditions. Annual CO₂-equivalent (CO₂eq) emissions were also evaluated. Under current grid emission factors, the ASHP system emitted 1532 kgCO₂eq—approximately 8.6% more than a condensing gas boiler (1411 kgCO₂eq), primarily due to winter performance degradation and the relatively high carbon intensity of electricity. These findings underscore the importance of incorporating actual operating conditions, seasonal variability, and the national electricity emission factor when assessing ASHP performance and life cycle climate performance (LCCP). Full article

The microclimate of traditional blocks, a key component of urban fabric, directly affects the overall urban thermal environment. Creating a suitable microclimate is crucial for improving urban living quality. Field measurements, ENVI-met simulations, and the PET index were used to analyze the spatiotemporal variations and core drivers of thermal comfort. Temporally, five open space types showed a unimodal “rise–stabilization–fall” PET curve, with peak heat stress occurring at 11:00–14:00. Courtyards heated fastest, but green spaces had the most stable thermal environment because trees provided shading and transpiration for gentle cooling. Spatially, thermal comfort varied significantly. For example, green spaces rich in trees performed best (PET 5–8 °C lower than pure grassland), while squares and courtyards faced severe midday heat stress (PET mostly moderate or above). Alley comfort depended on aspect ratio and orientation—north–south alleys with an aspect ratio > 2 were 2–3 °C cooler than open spaces, but east–west or narrower alleys (aspect ratio < 1.5) and low-enclosed courtyard control apply to southern Hunan’s hot-humid zone. However, the synergistic principles can be extended to similar southern regions, providing technical reference for traditional block livability and climate-resilient cities. Full article

This work proposes the design, construction, and field test of a vacuum seawater desalination system (VSDS) driven by an evacuated tube solar collector (with a total absorption area of 1.86 m²) under tropical climatic condition (Thailand ambient at latitude 13°43′06.0″ N, longitude 100°32′25.4″ E). The VSDS prototype was designed and constructed to be driven by hot water, which is produced by two heat source conditions: (1) an electric heater for laboratory tests and (2) an evacuated tube solar collector for field tests under real climatic conditions. A comparative experimental study to assess the ability to produce fresh water between a conventional dripping/pipe feed column and spray falling film column is proposed in the first part of the discussion. This is to demonstrate the advantage of the spray falling film distillation column. The experimental method is implemented based on the batch system, in which the cycle time (distillation time) considered is 10–20 min so that heat loss via the concentrated seawater blow down is minimized. Later, the field test with solar irradiance under real climatic conditions is demonstrated to assess the freshwater yield and the system performance. The aim is to provide evidence of the proposed vacuum desalination system in real operation. It is found experimentally that the VSDS working with spray falling film provides better performance than the dripping/pipe feed column under the specified working conditions. The spray falling film column can increase the distillated freshwater volume from 1.33 to 2.16 L under identical cycle time and working conditions. The improvement potential is up to 62.4%. The overall thermal efficiency can be increased from 33.7 to 70.8% (improvement of 110.1%). Therefore, the VSDS working with spray falling film is selected for implementing field tests based on real solar irradiance powered by an evacuated tube solar collector. The ability to produce fresh water is assessed, and the overall performance via the average distillation rate and the thermal efficiency (or Gain Output Ratio) is discussed with the real solar irradiance. It is found from the field test with solar time (8.00–16.00) that the VSDS can produce a daily freshwater yield of up to 4.5 L with a thermal efficiency of up to 19%. The freshwater production meets the requirement for international standard drinking water criteria, indicating suitability for household/community use in tropical regions. This work demonstrates the feasibility of VSDS working under real solar irradiance as an alternative technology for sustainable fresh water. Full article