Search Results (710)

Driven by the “dual carbon” goal and the strategy for cultivating new productive forces, China’s economy is undergoing a crucial transformation from high-speed growth to high-quality development. As a typical high-energy consumption and high-emission sector, the green and low-carbon transformation of the building materials industry directly affects the optimization of the national energy structure and the realization of ecological goals. However, traditional building material enterprises generally face practical challenges such as low resource utilization efficiency, insufficient digitalization and greening integration of the industrial chain, and weak green innovation momentum. The transformation actions of a single entity are difficult to break through systemic bottlenecks, and it is urgently necessary to establish a dynamic evolution mechanism involving multiple entities in collaboration. This paper aims to explore the evolutionary rules and stability of digital green (DG) transformation strategies of building materials enterprises (BMEs) under multi-agent interactions involving government, universities, and consumers. Centering on BMEs, a four-party evolutionary game model among the government, enterprises, universities, and consumers is constructed, and the evolutionary processes of strategic behaviors are characterized through replicator dynamic equations. Using MATLAB R2022 (Version number: 9.13.0.2049777) bnumerical simulations, this study investigates how key parameters, such as government subsidies, penalty intensity, and consumers’ green preferences, affect the transformation pathways of enterprises. The results reveal that the DG transformation behavior of BMEs is significantly influenced by governmental policy incentives and universities’ knowledge innovation. Stronger subsidies and penalties enhance enterprises’ willingness to adopt proactive DG strategies, while consumers’ green preferences further accelerate transformation through market mechanisms. Among multiple strategic combinations, active DG transformation emerges as the main evolutionarily stable strategy. This study provides a systematic multi-agent collaborative analysis framework for the transformation of BME DG, revealing the mechanisms by which policies, knowledge, and market demands influence enterprise decisions. Thus, it offers theoretical and decision-making references for the green and low-carbon transformation of the building materials industry. Full article

Urbanization and climate change have intensified the need for advanced methods to simulate building energy performance within realistic urban environmental contexts. This study presents a microclimate-informed framework for developing representative building energy prototypes that enable the estimation of energy use for buildings sharing similar microclimatic conditions and building-level characteristics. The framework is demonstrated using Des Moines, Iowa, as a case study. The framework combines high-resolution microclimate modeling with geospatial analysis to quantify the influence of urban form and vegetation on building energy use. Localized weather files were generated using the Weather Research and Forecasting (WRF) model to capture spatial variations in microclimate across the city. Detailed three-dimensional models of buildings and trees were developed from Light Detection and Ranging (LiDAR) point cloud data and integrated with building attributes, including construction materials and heating and cooling systems, to generate representative building typologies use them to build a similarity-based lookup table. Urban energy simulations were conducted using the Urban Modeling Interface (UMI). To demonstrate the effectiveness of the framework, simulations were conducted for two building prototypes according to the framework. Results show that monthly energy use intensity (EUI) of a representative cluster compared to randomly selected buildings differs by 10% to 19%, with both positive and negative deviations observed depending on building template and month. Thus, the proposed framework shows great promise to capture comparable energy performance trends across buildings with similar construction characteristics and urban context and minimize computational demands for doing so. While evapotranspiration effects are not explicitly modeled in the current framework, they are recognized as an important microclimatic process and will be incorporated in future work. This study demonstrates that the proposed framework provides a scalable and computationally efficient approach for urban-scale energy analysis and can support data driven decision making for climate-responsive urban planning. Full article

Data scarcity limits robust assessment of urban overheating and its implications for building energy use, especially in complex-terrain cities such as those in mountain environments. In this context, Land Surface Temperature (LST) from thermal remote sensing can be used to map urban hotspots at high spatial resolution. Nevertheless, it does not provide the full set of hourly atmospheric variables required to run building energy simulations aimed at quantifying their impact and defining mitigation measures. Given these premises, this study proposes a methodology combining satellite-derived LST with ground meteorological measurements to assess Urban Heat Island (UHI) patterns and quantify how measured weather data selection affects urban building energy modeling (UBEM) outcomes. After selecting as a case study Bolzano, an Alpine city in Northern Italy, ECOSTRESS LST (2019–2025, May–August) was first processed and quality-screened to (1) compute ΔLST (urban–rural) and (2) identify diurnal and spatial overheating patterns across the building stock. Second, four measured weather datasets—one rural station and three urban stations located in the city core, in the industrial district, and in the urban edge—were used as boundary conditions in an EnergyPlus-based UBEM parametric campaign for 253 residential buildings, covering multiple envelope insulation levels and window-to-wall ratios. Results show strong diurnal asymmetry in surface overheating, with the largest contrasts in the afternoon and prominent industrial hotspots. Ground measurements confirm persistent intra-urban microclimatic differences, and the choice of measured weather dataset causes systematic shifts in simulated cooling demand and thermal comfort. The study highlights the need for weather data selection strategies based on microclimatic context rather than simple proximity, improving representativeness in UBEM applications for Alpine and other heterogeneous urban environments. Full article

This study provides a detailed technical and sustainability comparison of the conventional CNC machining and additive manufacturing routes for an aerospace bearing bracket. The work integrates material selection, process parameterization, build simulation, and environmental–economic assessment within a single framework. For the CNC route, machining of Al 7175-T7351 is characterized through process sequencing, tooling requirements, and waste generation. For the Laser Powder Bed Fusion (LPBF) route, two build strategies, single-part distortion-minimized and multi-part volume-optimized, are developed using Siemens NX for orientation optimization and Atlas3D for thermal and recoater collision simulations. The mechanical properties of Al 7175-T7351 and Scalmalloy^® are compared to justify material selection for aerospace applications. Both the experimental and simulation-derived process metrics are reported, including the build time, support mass, energy consumption, distortion tolerances, and buy-to-fly (B2F) ratio. CNC machining exhibited a B2F ratio of 1:7, with cradle-to-gate CO₂ emissions of ~11,000 g and an energy consumption exceeding 100 kWh per component. In contrast, both LPBF strategies achieved a B2F ratio of 1:1.2, reducing CO₂ emissions by over 90% and energy consumption by up to 63%. Build volume optimization further reduced the LPBF unit cost by over 50% relative to the CNC machining. Use-phase analysis in an aviation context indicated estimated lifetime fuel savings of 776,640 L and the avoidance of 2328 tons of CO₂ emissions. The study demonstrates how simulation-guided build preparation enables informed sustainability-driven decision-making for manufacturing route selection in aerospace applications. Full article

Metal additive manufacturing (AM) generates complex microstructures through extreme thermal gradients and rapid solidification, critically influencing mechanical performance and industrial qualification. This review synthesizes recent advances in cellular automata (CA) and phase-field (PF) modeling to predict grain-scale microstructure evolution during AM. CA methods provide computational efficiency, enabling large-domain simulations and excelling in texture prediction and multi-layer builds. PF approaches deliver superior thermodynamic fidelity for interface dynamics, solute partitioning, and nonequilibrium rapid solidification through CALPHAD coupling. Hybrid CA–PF frameworks strategically balance efficiency and accuracy by allocating PF to solidification fronts and CA to bulk grain competition. Recent algorithmic innovations—discrete event-inspired CA, GPU acceleration, and machine learning—extend scalability while maintaining predictive capability. Validated applications across Ni-based superalloys, Ti-6Al-4V, tool steels, and Al alloys demonstrate robust process–microstructure–property predictions through EBSD and mechanical testing. Persistent challenges include computational scalability for full-scale components, standardized calibration protocols, limited in situ validation, and incomplete multi-physics coupling. Emerging solutions leverage physics-informed machine learning, digital twin architectures, and open-source platforms to enable predictive microstructure control for first-time-right manufacturing in aerospace, biomedical, and energy applications. Full article

Heating, ventilation, and air-conditioning (HVAC) systems dominate energy consumption in hot-climate buildings, where maintaining occupant comfort under extreme outdoor conditions remains a critical challenge, particularly under emerging time-of-use (TOU) electricity pricing schemes. While deep reinforcement learning (DRL) has shown promise for adaptive HVAC control, existing approaches often suffer from comfort violations, myopic decision making, and limited robustness to uncertainty. This paper proposes a comfort-first hybrid control framework that integrates Soft Actor–Critic (SAC) with a Cross-Entropy Method (CEM) refinement layer, referred to as SACEM. The framework combines data-efficient off-policy learning with short-horizon predictive optimization and safety-aware action projection to explicitly prioritize thermal comfort while minimizing energy use, operating cost, and peak demand. The control problem is formulated as a Markov Decision Process using a simplified thermal model representative of commercial buildings in hot desert climates. The proposed approach is evaluated through extensive simulation using Saudi Arabian summer weather conditions, realistic occupancy patterns, and a three-tier TOU electricity tariff. Performance is assessed against state-of-the-art baselines, including PPO, TD3, and standard SAC, using comfort, energy, cost, and peak demand metrics, complemented by ablation and disturbance-based stress tests. Results show that SACEM achieves a comfort score of 95.8%, while reducing energy consumption and operating cost by approximately 21% relative to the strongest baseline. The findings demonstrate that integrating comfort-dominant reward design with decision-time look-ahead yields robust, economically viable HVAC control suitable for deployment in hot-climate smart buildings. Full article

With the fast development of the 5G technology and IoV, a vehicle has become a smart device with communication, computing, and storage capabilities. However, the limited on-board storage and computing resources often cause large latency for task processing and result in degradation of system QoS as well as user QoE. In the meantime, to build the environmentally harmonious transportation system and green city, the energy consumption of data processing has become a new concern in vehicles. Moreover, due to the fast movement of IoV, traditional GSI-based methods face the dilemma of information uncertainty and are no longer applicable. To address these challenges, we propose a T2VC model. To deal with information uncertainty and dynamic offloading due to the mobility of vehicles, we propose a MAB-based QEVA-UCB solution to minimize the system cost expressed as the sum of weighted latency and power consumption. QEVA-UCB takes into account several related factors such as the task property, task arrival queue, offloading decision as well as the vehicle mobility, and selects the optimal location for offloading tasks to minimize the system cost with latency energy awareness and conflict awareness. Extensive simulations verify that, compared with other benchmark methods, our approach can learn and make the task offloading decision faster and more accurately for both latency-sensitive and energy-sensitive vehicle users. Moreover, it has superior performance in terms of system cost and learning regret. Full article

Urban Green Spaces (UGS) are integral components of the built environment, significantly contributing to its ecological, social, and performance dimensions, including microclimate regulation, occupant well-being, and energy efficiency. This decadal review (2015–2025) systematically analyzes 70 high-impact studies to propose a “Symbiotic Intelligence” framework. This framework integrates Generative AI, ethical algorithms, and innovations from the Global South to revolutionize the planning, design, and management of UGS within building landscapes and urban fabrics. Our analysis reveals that Generative AI can optimize participatory design processes and generate efficient planning schemes, increasing public satisfaction by 41% and achieving fivefold efficiency gains. Metaverse digital twins enable high-fidelity simulation of UGS performance with a mere 3.2% error rate, providing robust tools for building environment analysis. Ethical algorithms, employing fairness metrics and SHAP values, are pivotal for equitable resource distribution, having been shown to reduce UGS allocation disparities in low-income communities by 67%. Meanwhile, innovations from the Global South, such as lightweight federated learning and low-cost sensors, offer scalable solutions for building-environment monitoring under resource constraints, reducing model generalization error by 18% and decreasing data acquisition costs by 90%. However, persistent challenges-including data heterogeneity, algorithmic opacity (with only 23% of studies adopting interpretability tools), and significant data gaps in the Global South (coverage < 15%)-hinder equitable progress. Future research should prioritize developing UGS-climate-building coupling models, decentralized federated frameworks for building management systems, and blockchain-based participatory planning to establish a more robust foundation for sustainable built environments. This study provides an interdisciplinary roadmap for integrating intelligent UGS into building practices, contributing to the advancement of green buildings, occupant-centric design, and the overall sustainability and resilience of our built environment. Full article

This paper presents the development and implementation of a consumer Digital Twin (DT) for energy demand prediction under the SENDER (Sustainable Consumer Engagement and Demand Response) project, funded by HORIZON 2020. This project aims to engage consumers in the energy sector with innovative energy service applications to achieve proactive Demand Response (DR) and optimized usage of Renewable Energy Sources (RES). The proposed DT model is designed to digitally represent occupant behaviors and energy consumption patterns using Artificial Neural Networks (ANN), which enable continuous learning by processing real-time and historical data in different pilot sites and seasons. The DT development incorporates the International Energy Agency (IEA)—Energy in Buildings and Communities (EBC) Annex 66 and Drivers-Needs-Actions-Systems (DNAS) framework to standardize occupant behavior modeling. The research methodology consists of the following steps: (i) a mock-up simulation environment for three pilot sites was created, (ii) the DT was trained and calibrated using the artificial data from the previous step, and (iii) the DT model was validated with real data from the Alginet pilot site in Spain. Results showed a strong correlation between DT predictions and mock-up data, with a maximum deviation of ±2%. Finally, a set of selected Key Performance Indicators (KPIs) was defined and categorized in order to evaluate the system’s technical effectiveness. Full article

The growing demand for energy-efficient buildings necessitates innovative approaches to reduce energy consumption during early design stages. While traditional physics-based simulations remain time- and expertise-intensive, automated machine learning (AutoML) offers a promising alternative by enabling data-driven building performance prediction with minimal human intervention. This study conducts a benchmark evaluation of AutoML’s potential in building energy applications through three objectives: (1) a literature review revealing AutoML’s nascent adoption (10 identified studies) and primary use cases (heating/cooling prediction, energy demand forecasting); (2) a benchmark comparing three AutoML frameworks (AutoGluon, H2O, Auto-sklearn) against baseline and ensemble ML models using R², RMSE, MSE, and MAE metrics; and (3) SHAP (SHapley Additive exPlanations)-based interpretability analysis. Results demonstrate AutoGluon’s superior accuracy (R² = 0.993, RMSE = 2.280 kWh/m²) in predicting energy performance, outperforming traditional methods. Key influential features, including solar heat gain coefficient (SHGC) and U-values, were identified through SHAP, offering actionable design insights. The primary novelty of this work lies in its two-step methodology: a focused review to identify pertinent AutoML frameworks, followed by a comparative benchmarking of these frameworks against traditional ML for early-stage prediction. This process substantiates AutoML’s potential to democratize energy modeling and deliver practical, interpretable workflows for architectural design. Full article

Using Shanghai as a case study, this paper estimates multi-sector urban carbon emissions by integrating multi-source statistical data from 2000 to 2023 with IPCC guidelines. Via rolling-window time-series validation, XGBoost is the most reliable model. To better understand the underlying drivers, explainable machine-learning approaches, including SHAP and the Friedman H-statistic, are applied to examine the nonlinear effects and interactions of population scale, industrial energy efficiency, investment structure, and infrastructure. The results suggest that Shanghai’s emission pattern has gradually shifted from a scale-driven process toward one dominated by structural change and efficiency improvement. Building on an incremental framework, four scenarios, Business-as-Usual, Green Transition, High Investment, and Population Plateau, are designed to simulate emission trajectories from 2024 to 2060. The simulations reveal a two-stage pattern, with a period of rapid growth followed by high-level stabilisation and a weakening path-dependence effect. Population agglomeration, economic growth, and urbanisation remain the main contributors to emission increases, while industrial upgrading and efficiency gains provide sustained mitigation over time. Scenario comparisons further indicate that only the Green Transition pathway supports early peaking, a steady decline, and long-term low-level stabilisation. Overall, this study offers a data-efficient framework for analysing urban carbon-emission dynamics and informing medium- to long-term mitigation strategies in megacities. Full article

Agro-industrial facilities host processes and products that are highly sensitive to thermal fluctuations. Given the projected increase in air temperatures in tropical regions due to climate change, improving indoor thermal conditions in these facilities has become critically important. Conventional cooling systems are widely used to maintain adequate indoor temperatures; however, they are associated with high energy consumption. In this context, Ground Source Heat Pump (GSHP) technology emerges as a promising alternative to reduce cooling loads by exchanging heat with the ground. This study evaluates the reductions in cooling energy consumption and the return on investment of a GSHP system integrated with conventional cooling system, considering a prototype agro-industrial room located in two ecotones of the Brazilian Midwest: the Amazon Forest (AF) and Brazilian Savanna (BS). Building energy simulations were performed using EnergyPlus software v. 9 under current climate conditions and climate change scenarios for 2050 and 2080. Initially, the prototype room was conditioned using a conventional HVAC system; subsequently, a GSHP system was integrated to enhance energy efficiency and reduce energy demand. Under current conditions, cooling energy demand in the BS and AF ecotones is projected to increase by 16.5% and 18.3% by 2050, and by 24.5% and 23.5% by 2080, respectively. The payback analysis indicates that the average return on investment improves under future climate scenarios, decreasing from 14.5 years under current conditions to 10.13 years in 2050 and 9.86 years in 2080. The findings contribute to understanding the thermal resilience and economic feasibility of ground-coupled heat exchangers as a sustainable strategy for mitigating climate change impacts in the agro-industrial sector. Full article

A Building Information Modeling (BIM)-based Machine Learning (ML) framework was developed to predict the energy performance of office buildings at the early design stage. The framework provides a reproducible and data-driven workflow that shortens simulation time while maintaining accuracy. Revit and Insight were integrated with statistical modeling in Weka to create an automated and regionally adaptable process derived from BIM-generated data. A reduced-factorial Design of Experiments (DOE) guided the generation of 210 parametric simulations representing base, generalization, and stress-test models for Orlando, Florida. Each model combined geometric, envelope, system, and operational variations, forming a dataset of 14 independent parameters and two dependent energy metrics: Energy Use Intensity (EUI) and Operational Energy (OE). Four regression algorithms—Linear Regression (LR), M5P, SMOReg, and Random Forest (RF)—were trained and validated through 10-fold cross-validation. All models achieved R² values above 0.95, with the RF model reaching the highest overall accuracy under default parameter settings, with R² > 0.97 and mean absolute errors below 5% across both metrics, EUI and OE. Feature-importance analysis identified HVAC system type, window-to-wall ratio, and operational schedule as the most influential variables. Results confirm that BIM-ML integration enables rapid and reliable energy-performance prediction, supporting informed, energy-efficient design decisions in the earliest phases of the building lifecycle. Full article

To promote the resource utilization of high-titanium blast furnace slag (HTBFS) and advance the development of lightweight prefabricated structures, this study developed a lightweight HTBFS concrete composite beam (HTC composite beam) by replacing natural gravel and sand in concrete with HTBFS coarse and fine aggregates, and incorporating fly ash ceramsite to reduce self-weight. Symmetrically two-point bending tests were conducted on five HTC composite beams with different reinforcement ratios and precast heights, one Integrally cast HTC beam, and one ordinary concrete composite beam. The failure modes, load-carrying capacities, and deformation characteristics were evaluated. The loading process was also simulated using Abaqus, and the numerical results were compared with experimental data for validation. The results indicate that HTC composite beams satisfy the plane-section assumption; increasing the reinforcement ratio improves the load-carrying capacity, and the precast height has positive effect of HTC composite beams’ load-carrying. Compared with the ordinary concrete composite beam, the HTC composite beam exhibited a 12.30% higher load-carrying capacity, smaller deflection, and better deformation capacity. Multiple energy-based indices demonstrated that HTC composite beams possess favorable post-cracking plastic deformation capacity and stiffness retention. The difference between the finite element simulations and experimental results was less than 5%, confirming both the reliability of the numerical model and the accuracy of the experimental data. An economic analysis revealed that this structural system has significant potential for carbon reduction and cost savings, with an overall saving of approximately 141,000–500,000 CNY. These findings provide theoretical and engineering support for the application of HTC composite beams in prefabricated construction and have positive implications for reducing project costs and promoting the industrialization and low-carbon development of prefabricated buildings. Full article

Sustainable renovation is a critical aspect for designing energy-efficient buildings with reasonable cost and high indoor living standards. The objective of this paper is to investigate various renovation scenarios for an old, uninsulated building with a floor area of 100 m² located in Athens, aiming to determine the global optimal solution through a multi-criteria analysis. The multi-criteria analysis considers energy, economic, and thermal comfort criteria to perform a multi-lateral approach. Specifically, the criteria are: (i) maximization of the energy savings, (ii) minimization of the life cycle cost (LCC), and (iii) minimization of the mean annual predicted percentage of dissatisfied (PPD). These criteria are combined within a multi-criteria evaluation procedure that employs a global objective function for determining a global optimum solution. The examined retrofitting actions are the addition of external insulation, the replacement of the existing windows with triple-glazed windows, the addition of shading in the openings in the summer, the application of cool roof dyes, the use of a mechanical ventilation system with a heat recovery unit, and the installation of a highly efficient heat pump system. The interventions were examined separately, and the combined renovation scenarios were studied by including them in the external insulation because of their high importance. The present study encompassed the investigation of a baseline scenario and 26 different renovation scenarios, conducted through dynamic simulation on an annual basis. The results of the present analysis indicated that the global optimal renovation scenario, including the addition of external insulation, the installation of highly efficient heat pumps, and the use of shading in the openings in the summer, saved energy by 74% compared to the baseline scenario. The LCC was approximately EUR 33,000, the simple payback period of the renovation process was around 6 years, the annual CO₂ emissions avoidance reached 4.6 tn_CO2, and the PPD was at 9.7%. An additional sensitivity analysis for determining the optimal choice under varying weights assigned to the criteria revealed that this renovation design is the most favorable option in most cases. These results prove that the suggested renovation scenario is a feasible and viable solution that leads to a sustainable design from multiple perspectives. Full article