Search Results (6,114)

Rubberized concrete is a novel green building material that enhances many features when rubber particles are incorporated into cement mortar, simultaneously yielding economic benefits through the recycling of waste tires. This study applies styrene–butadiene latex (SBL) for toughening treatment. The investigation delves into the mechanism by which SBL improves the interface between rubber and cement, encompassing macroscopic mechanical properties, microscopic structural characteristics, and nano-scale interfacial interactions. Macroscopic mechanical tests reveal a significant increase in flexural strength, shear strength, and compressive strength of the composite concrete upon the introduction of SBL and rubber. Specifically, the compressive strength improved by 8.8%, shear strength by 13.7%, and flexural strength by 18.9% at 28 days. Through electron microscopy observation of corresponding polymer cement concrete sections, observations reveal that SBL reinforces both interfaces and elucidates its bonding impact at the micro-level interface. Molecular dynamics (MD) modeling of SBL/rubber/CSH is employed at the nanoscale to compute and examine the local structure, dynamic behavior, and binding energy of the interface. The findings indicate that SBL mitigates interface impacts, enhances interface hydrogen bonds, van der Waals interactions, $\mathrm{C}\mathrm{a}-\mathrm{H}$ coordination bonds, and stability, consequently improving interfacial adhesion and fortifying the feeble interface bonding between organic polymers (rubber) and inorganic silicates (CSH). Full article

Relatively uniform consumption of a large amount of electrical energy intended for the current operation of the equipment of multi-story student dormitories indicates several actions aimed at renovation of these dormitories using photovoltaic installations producing electricity to replace the energy supplied from external networks. The research allowed for parameterization of input and output data, defining several innovative parametric and discrete models used in modernization processes and constituting the basis for optimizing energy renovations in terms of the substitutability of grid energy, payback periods, and investment costs. A new method developed to renovate dormitories was supported by an application elaborated in the visual parametric Rhino/Grasshopper design environment. This application enables automatic uploading of various meteorological data files and programming the loads, properties, and operation of the designed photovoltaic installation. This method results in a single optimal solution concerning a building renovation process, which allows for fully automated execution of the above activities. The developed models were configured based on a real renovated multi-story residence student hall located on the Central European Plain, for which a 34.3% balance of the replaced grid energy was carried out. The optimizing processes concerning the geometric properties and orientation of photovoltaic panels resulted in −30° of azimuth, 210 m² of total surface area, and 14° of tilt of photovoltaic panels distributed on the south façade, with 193 m² of surface area, 42° of tilt of panels arranged on the east façade, and an optimal payback period of 99 months. The invented algorithm, parametric models, computer programs, simulations, and optimizing calculations fill the gap in variant-optimized modelling and simplify the design processes of renovations of multi-story residence halls. These objects provide a basis for expanding the method to include other types of dormitory modernizations. Full article

Saudi Arabia has a large part of the country’s power consumption in the building area, mainly operated by cooling demands under extreme climatic conditions, where the summer temperature is more than 45 °C and solar radiation peaks are more than 1200 W/MIC. Facing this challenge, this research examines the translation of biometric principles in the design of adaptive building construction for dry areas. We present a comprehensive, four-phase method structure: removing thermoregulatory and shading strategies from desert vegetation; computer display simulation using EnergyPlus 9.7.0 and CFD (ANSYS Fluent 2022 R2); and the development of an implementation guideline. Our findings achieve three central insights. First, the dynamic factor system, such as the electrochromic glazing tested in our student project, reduced the use of HVAC energy by 30%, while advanced materials, such as the polycarbonate panel, demonstrated notable thermal stability. Secondly, the synergy between cultural knowledge and technical performance proved to be decisive; vernacular-inspired Mushrabias improved generic louver not only in thermal efficiency but also in user acceptance, which increased the 97% approval rate in post-acquisition surveys. Finally, we demonstrate that scalability is economically viable, indicating a seven-year payback period for simulation, phase-transit material (PCM), which aligns with the budgetary realities of public and educational projects. By fusing the plant-induced strategies with rigorous computational modeling and real-world applications, the work provides actionable guidelines for permanent failure design in the warm-dry climate. It underlines that maximizing energy efficiency requires the cohesion of thermodynamic principles with the craft traditions of local architecture, an approach directly aligned with the Saudi Green Initiative and the ambitions of global carbon neutrality goals. Full article

Buildings in Indonesia’s tropical climate face significant barriers to energy efficiency due to high cooling loads and electricity intensity. Previous studies have primarily addressed technical optimization or policy frameworks, but few have provided an integrated and data-driven evaluation model for tropical conditions. This study develops an Internet of Things (IoT) and EDGE-based hybrid framework to support the transition toward Net-Zero-Energy Buildings (NZEBs) while maintaining occupant comfort. The research combines real-time IoT monitoring at the LLDIKTI Region XIII Office Building in Banda Aceh with simulation-based assessment using Excellence in Design for Greater Efficiencies (EDGE). Baseline energy performance was established from architectural data, historical electricity use, and live monitoring of HVAC systems, lighting, temperature, humidity, and CO₂ concentration. Intervention scenarios—including building envelope enhancement, lighting optimization, and adaptive HVAC control—were simulated and validated against empirical data. Results demonstrate that integrating IoT-driven control with passive design measures achieves up to 31.49% reduction in energy use intensity, along with 24.7% improvement in water efficiency and 22.3% material resource savings. These findings enhance indoor environmental quality and enable adaptive responses to user behavior. The study concludes that the proposed IoT–EDGE framework offers a replicable and context-sensitive pathway for achieving net-zero energy operations in tropical office buildings, with quantifiable environmental benefits that support sustainable public facility management in Indonesia. Full article

Building Information Modeling (BIM) has established itself as a strategic and indispensable tool for designing and implementing smart buildings within the context of smart cities. This study explores the potentialities and challenges of using BIM across the main stages of the smart building lifecycle: design, construction, and operation and maintenance. We conducted comprehensive, detailed, and interpretative literature research to extract the main concepts and knowledge, enabling us to identify the main potentialities and challenges and classify them by life-cycle phase for smart buildings. Potentialities and challenges were prioritized based on the number of projects that cited them. The inclusion criteria for identifying potentialities and challenges were based on their key attributes: significant impact, information modeling potential, integration capability with other tools and methods, and improved performance in processes and services across all life cycle phases and BIM dimensions. The findings reveal that the main potentials include optimizing information management, reducing operating costs, enhancing environmental sustainability, and enhancing decision-making processes. Furthermore, the study highlights BIM’s role in integrating technologies such as IoT, augmented reality, and energy simulations, contributing to the development of more sustainable and functional buildings. However, challenges to its full adoption persist, including financial constraints, interoperability issues between systems, a lack of specialized technical skills, and organizational resistance to change. The dependence on advanced technological infrastructure and robust connectivity poses an additional challenge, especially in developing countries, where such resources may be scarce or inconsistent. Finally, this study suggests that future research should explore the integration of BIM with emerging technologies, such as artificial intelligence and digital twins, further expanding its applicability in the smart urban context. Full article

The aluminum electrolysis industry is a typical high-energy-consumption and high-carbon-emission sector, and its low-carbon transformation is crucial for achieving “dual-carbon” goals. However, aluminum electrolysis is constrained by thermodynamic safety limits, and conventional dispatch models also often overlook carbon emission trading and the integrated utilization of waste heat. To address these challenges, a low-carbon economic dispatch method considering production safety constraints is proposed in the paper for integrated energy systems in aluminum electrolysis, aiming to enhance wind power utilization and ensure operational safety. First, a load model incorporating thermodynamic safety constraints is developed, and a thermal dynamics equation of electrolytic cells is established to characterize the temperature dynamics of aluminum loads. Then, a bi-level optimization framework for the power–aluminum system is constructed: the upper level minimizes grid power-supply costs by coordinating thermal, wind, and photovoltaic generation, while the lower level maximizes enterprise profit, balancing production safety and economic efficiency to achieve coordination between the system and enterprise layers. Finally, a tiered carbon trading mechanism and waste heat heating model are integrated into the framework, combined with a second-order RC building thermal inertia model to realize coordinated optimization among electricity, heat, and carbon flows. The simulation results demonstrate that the proposed method effectively reduces carbon emissions while ensuring electrolytic cell safety: with carbon trading, emissions decrease by 7.2%; when incorporating waste heat utilization reduces boiler heating emissions, they decrease by 74.7%; and further considering building thermal inertia increases wind power utilization to 99.6%, achieving the coordinated optimization of electricity–heat–carbon systems. Full article

The state of charge (SOC) serves as a critical indicator for evaluating the remaining driving range of electric vehicles (EVs), and its prediction is of significance for alleviating range anxiety and promoting the development of the EVs industry. This study addresses two key challenges in current SOC prediction technologies: (1) the scarcity of multi-step prediction research based on real driving conditions and (2) the poor performance in multi-scale temporal feature extraction. We innovatively propose the Wavelet-Guided Temporal Feature Enhanced Informer (WG-TFE-Informer) prediction model with two core innovations: a wavelet-guided convolutional embedding layer that significantly enhances anti-interference capability through joint time-frequency analysis and a temporal edge enhancement (TEE) module that achieves the collaborative modeling of local microscopic features and macroscopic temporal evolution patterns based on sparse attention mechanisms. Building upon this model, we establish a multidimensional SOC energy consumption prediction system incorporating battery characteristics, driving behavior, and environmental terrain factors. Experimental validation with real-world operating data demonstrates outstanding performance: 1-min SOC prediction accuracy achieves a mean relative error (MRE) of 0.21% and 20-min SOC prediction exhibits merely 0.62% error fluctuation. Ablation experiments confirm model effectiveness with a 72.1% performance improvement over baseline (MRE of 3.06%) at 20-min SOC prediction, achieving a final MRE of 0.89%. Full article

Building archetypes are essential for advancing architectural theory and energy modeling, providing a foundation for scalable assessments of building performance and sustainability worldwide. In Saudi Arabia, educational buildings, especially those in public universities, are predominantly constructed using standardized and repetitive design templates, such as courtyard and prototype models, which have significant implications for energy efficiency, indoor environmental quality, and sustainability outcomes. Despite their prevalence, there is a notable lack of systematic research on the classification and distribution of these archetypes within the Saudi context, particularly regarding their impact on energy consumption and sustainable campus planning. This study addresses this gap by systematically collecting and analyzing data from 29 public universities across Saudi Arabia, employing GIS mapping to document building characteristics including age, region, urban context, masterplan typology, and architectural design. A cumulative weighting factor was applied to quantify the representativeness of archetypes, while chi-square tests and effect size metrics assessed the statistical concentration and significance of observed patterns. The results reveal a pronounced dominance of a small number of archetypes, especially standardized courtyard and identical design models, across the national stock, with the top 10% of archetype ranks accounting for the majority of buildings. This high degree of standardization enables efficient modeling, benchmarking, and targeted energy interventions, while also highlighting the need for greater contextual adaptation in future campus planning. While this study does not directly simulate building energy performance, it establishes a national-scale typological foundation that can support future simulation, benchmarking, and policy design. The developed GIS-based framework primarily serves managerial and planning objectives, offering a standardized reference for facility management, retrofitting prioritization, and strategic energy-efficiency planning in Saudi public universities. Full article

Buildings with electrified heat pump systems, onsite photovoltaic (PV) generation, and energy storage offer strong potential for demand flexibility. This study compares two storage configurations, thermal energy storage (TES) and battery energy storage (BESS), to evaluate their impact on cooling performance and cost savings. A Model Predictive Control (MPC) framework was developed to optimize system operations, aiming to minimize costs while maintaining occupant comfort. Results show that both configurations achieve substantial savings relative to a baseline. The TES system reduces daily operating costs by about 50%, while the BESS nearly eliminates them (over 90% reduction) and cuts grid electricity use by more than 65%. The BESS achieves superior performance because it can serve both the controllable heating, ventilation, and air conditioning (HVAC) system and the home’s broader electrical loads, thereby maximizing PV self-consumption. In contrast, the TES primarily influences the thermal load. These findings highlight that the choice between thermal and electrical storage greatly affects system outcomes. While the BESS provides a more comprehensive solution for whole-home energy management by addressing all electrical demands, further techno-economic evaluation is needed to assess the long-term feasibility and trade-offs of each configuration. Full article

Embodied energy flows link production systems with the energy sector, reflecting dependencies and structural risks under globalization and regional coordination. Guangdong, China’s most manufacturing-intensive, open, and energy-consuming province, is a central hub in both global value chains and domestic production networks, playing a pivotal role in national energy security. Understanding Guangdong’s embodied energy flows is essential for revealing the transmission of energy across multi-level spatial systems and the resilience of China’s energy infrastructure. This study integrates international (EXIOBASE) and Chinese inter-provincial input–output data to build a province-level nested global MRIO model, combined with Structural Path Analysis (SPA), to characterize Guangdong’s manufacturing embodied energy flows in domestic and international dual circulation from 2002 to 2017. Our findings confirm Guangdong’s pivotal bridging role in embodied energy transfers. First, flows are dual-directional and dominated by international transfers. Second, energy efficiency has improved, narrowing the intensity gap between export- and domestic-oriented industries. Third, flows have diversified spatially from concentration in developed regions toward developing regions, with domestic inter-provincial flows more dispersed. Finally, embodied energy remains highly concentrated across sectors, with leading industries shifting from labor- and capital-intensive to capital- and technology-intensive sectors. This research offers vital empirical evidence and policy reference for enhancing national energy security and optimizing spatial energy allocation. Full article

Switzerland’s energy transition toward net-zero greenhouse gas emissions by 2050 presents a critical challenge in managing winter electricity demand, particularly due to the widespread electrification of space heating and domestic hot water. In this study, we assess how targeted measures in the building sector can influence heat demand and thereby also the winter electricity gap. To this end, we extended the existing PowerCheck simulation tool by incorporating a detailed bottom-up representation of the Swiss building stock. We model hourly heat and electricity demand across 60 building categories, defined by climate zone, usage type, and insulation standard. Twelve future scenarios are developed based on variations in four key parameters: building renovation rate, hot water heat recovery, heat sources used by heat pumps, and ambient temperature trends. Our results indicate that renovation of old buildings to current insulation standards has by far the greatest effect out of the studied parameters. Increasing the annual thermal renovation rate of building shells from the currently planned 1.1% to 2% can reduce the winter electricity gap from 10.7 TWh to 6.0 TWh, a 44% reduction. Conversely, achieving only a low renovation rate of 0.5% could increase the gap to 13.9 TWh. Additional measures, such as greater use of ground-source instead of air-source heat pumps and implementation of hot water recovery, offer further potential for reduction. These findings underscore the importance of early and sustained investment in thermal renovation of building shells for achieving Switzerland’s 2050 net-zero climate targets. Full article

Modern engineering increasingly operates within socio-technical networks, such as the interdependence of energy grids, transport systems, and building codes, where decisions must be reliable and transparent. Large language models (LLMs) such as GPT promise efficiency by interpreting domain-specific queries and generating outputs, yet their predictive nature can introduce biases or fabricated values—risks that are unacceptable in structural engineering, where safety and compliance are paramount. This work presents a dataset that embeds generative AI into validated computational workflows through the Model Context Protocol (MCP). MCP enables API-based integration between ChatGPT (GPT-4o) and numerical solvers by converting natural-language prompts into structured solver commands. This creates context-aware exchanges—for example, transforming a query on seismic drift limits into an OpenSees analysis—whose results are benchmarked against manually generated ETABS models. This architecture ensures traceability, reproducibility, and alignment with seismic design standards. The dataset contains prompts, GPT outputs, solver-based analyses, and comparative error metrics for four reinforced concrete frame models designed under Ecuadorian (NEC-15) and U.S. (ASCE 7-22) codes. The end-to-end runtime for these scenarios, including LLM prompting, MCP orchestration, and solver execution, ranged between 6 and 12 s, demonstrating feasibility for design and verification workflows. Beyond providing records, the dataset establishes a reproducible methodology for integrating LLMs into engineering practice, with three goals: enabling independent verification, fostering collaboration across AI and civil engineering, and setting benchmarks for responsible AI use in high-stakes domains. Full article

Proper daylighting in educational buildings improves students’ mood and health. However, daylighting can be affected by multiple environmental features, and comprehensive investigations remain limited. This study examined how various classroom environmental features affect students’ mood perception in six classrooms of three middle schools in eastern China. Eighteen environmental features across six dimensions were assessed through field studies and software simulations, and 557 valid mood responses were collected from 243 students through questionnaires. Traditional machine learning and deep learning models were used to predict students’ mood perception with the environmental features, with SHapley Additive exPlanations (SHAP) applied to interpret the contributions of different features. Results showed that Random Forest achieved a relatively high accuracy of 82% in the binary classification of mood perception prediction. Among all features, Exterior view evaluation (EVE) had the largest impact and showed a strong interaction with floor level. Higher floors and EVE ≥ 3 were associated with more positive moods. Beneficial conditions for mood perception also included horizontal desktop illuminance above 300 lx, frontal eye-level illuminance below 400 lx, left-side eye-level illuminance within 300–1000 lx, and proximity to windows below 2.5 m. These findings provide new insights and practical guidance for designing healthier classroom environments to promote adolescent mental health, thereby contributing to sustainable educational environments that integrate human well-being with energy-efficient daylighting design. Full article

As a key zero-carbon energy carrier, the accurate measurement of liquid hydrogen flow in its industrial chain is crucial. However, the ultra-low temperature, ultra-low density and other properties of liquid hydrogen can introduce calibration errors. To enhance the measurement accuracy and reliability of liquid hydrogen flow, this study investigates the heat and mass transfer within a 1 m³ non-vented storage tank during the calibration process of a liquid hydrogen flow standard device that integrates combined dynamic and static gravimetric methods. The vertical tank configuration was selected to minimize the vapor–liquid interface area, thereby suppressing boil-off gas generation and enhancing pressure stability, which is critical for measurement accuracy. Building upon research on cryogenic flow standard devices as well as tank experiments and simulations, this study employs computational fluid dynamics (CFD) with Fluent 2024 software to numerically simulate liquid hydrogen flow within a non-vented tank. The thermophysical properties of hydrogen, crucial for the accuracy of the phase-change simulation, were implemented using high-fidelity real-fluid data from the NIST Standard Reference Database, as the ideal gas law is invalid under the cryogenic conditions studied. Specifically, the Lee model was enhanced via User-Defined Functions (UDFs) to accurately simulate the key phase-change processes, involving coupled flash evaporation and condensation during liquid hydrogen refueling. The simulation results demonstrated good agreement with NASA experimental data. This study systematically examined the effects of key parameters, including inlet flow conditions and inlet liquid temperature, on the flow characteristics of liquid hydrogen entering the tank and the subsequent heat and mass transfer behavior within the tank. The results indicated that an increase in mass flow rate elevates tank pressure and reduces filling time. Conversely, a decrease in the inlet liquid hydrogen temperature significantly intensifies heat and mass transfer during the initial refueling stage. These findings provide important theoretical support for a deeper understanding of the complex physical mechanisms of liquid hydrogen flow calibration in non-vented tanks and for optimizing calibration accuracy. Full article

Balconies are widely recognized for enhancing urban livability, making them attractive elements to incorporate in building renovation projects. However, their impact on energy performance remains insufficiently studied, particularly in temperate climates, like the Mediterranean, where both heating and cooling demands must be considered. This article evaluates the energy impacts of integrating open balconies into housing retrofits on the space conditioning demand of dwellings through spatialized analysis at the urban block scale. Focusing on Barcelona’s Eixample district, a parametric Urban Building Energy Modeling (UBEM) was employed to assess how balcony design interacts with urban morphology (orientation, obstructions), building features (window-to-wall ratio, WWR), and balcony length. Results reveal a seasonal trade-off at the block scale: balconies increase heating demand (0.1–1.6 kWh/m²·yr) by reducing winter solar gain but decrease cooling demand (0.1–3.8 kWh/m²·yr) through summer shading. Net effects vary by unit position, with south-facing and moderately glazed dwellings benefiting the most. Deeper balconies (1.5–2 m) amplify both effects, while optimal depth depends on the window-to-wall ratio. Under future climates, retrofits combining insulation and balconies mitigate rising cooling demands more effectively than insulation alone, reducing block-level demand by up to 16%. Although balconies alone show modest energy savings at the block scale, they enhance localized thermal resilience. The study highlights the need for integrated retrofit strategies that balance thermal insulation with solar protection to address both current and future energy challenges while enhancing occupant well-being. Full article