Search Results (4,190)

This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’s construction sector. The suggested methodology utilizes BIM for dynamic energy simulations and design visualization, PMBOK for integrating sustainability into project-management processes, and ML for predictive modeling and real-time energy optimization. Implementing an integrated model that incorporates building-management strategies and machine learning for both commercial and residential structures can offer stakeholders a thorough solution for forecasting energy performance and environmental impact. This is particularly essential in arid climates owing to specific conditions and environmental limitations. Using a simulation-based methodology, the framework was evaluated based on two representative case studies: (i) a commercial complex and (ii) a residential building. The neural network (NN), reinforcement learning (RL), and decision tree (DT) were implemented to assess performance in energy prediction and optimization. Results demonstrated notable seasonal energy savings, particularly in spring (15% reduction for commercial buildings) and fall (13% reduction for residential buildings), driven by optimized heating, ventilation, and air conditioning (HVAC) systems, insulation strategies, and window configurations. ML models successfully predicted energy consumption and greenhouse gas (GHG) emissions, enabling targeted mitigation strategies. GHG emissions were reduced by up to 25% in commercial and 20% in residential settings. Among the models, NN achieved the highest predictive accuracy (R² = 0.95), while RL proved effective in adaptive operational control. This study highlights the synergistic potential of BIM, PMBOK, and ML in advancing green project management and sustainable construction. Full article

This study investigates the electro-thermal transient response of a photovoltaic–thermal (PV/T)–heat pump system under dynamic disturbances to optimize operational stability. A dynamic model integrating a PV/T collector and a heat pump was developed by the transient heat current method, enabling high-fidelity simulations of step perturbations: solar irradiance reduction, compressor operation, condenser water flow rate variations, and thermal storage tank volume changes. This study highlights the thermal storage tank’s critical role. For V_tank = 2 m³, water tank volume significantly suppresses the water tank and PV/T collector temperature fluctuations caused by solar irradiance reduction. PV/T collector temperature fluctuation suppression improved by 46.7%. For the PV/T heat pump system in this study, the water tank volume was selected between 1 and 1.5 m³ to optimize the balance of thermal inertia and cost. Despite PV cell electrical efficiency gains from PV cell temperature reductions caused by solar irradiance reduction, power recovery remains limited. Compressor dynamic performance exhibits asymmetry: the hot water temperature drop caused by speed reduction exceeds the rise from speed increase. Load fluctuations reveal heightened risk: load reduction triggers a hot water 7.6 °C decline versus a 2.2 °C gain under equivalent load increases. Meanwhile, water flow rate variation in condenser identifies electro-thermal time lags (100 s thermal and 50 s electrical stabilization), necessitating predictive compressor control to prevent temperature and compressor operation oscillations caused by system condition changes. These findings advance hybrid renewable systems by resolving transient coupling mechanisms and enhancing operational resilience, offering actionable strategies for PV/T–heat pump deployment in building energy applications. Full article

A key requirement in containing contagious diseases, like the COVID-19 pandemic, is the ability to efficiently carry out mass diagnosis over large populations, especially when testing resources are limited and rapid identification is essential for outbreak control. Some of the leading testing procedures, such as those utilizing qualitative polymerase chain reaction, involve using dedicated machinery which can simultaneously process a limited amount of samples. A candidate method to increase the test throughput is to examine pooled samples comprised of a mixture of samples from different patients. In this work, we study pooling-based tests which operate in a one-shot fashion, while providing an indication not solely on the presence of infection, but also on its level, without additional pool-tests, as often required in COVID-19 testing. As these requirements limit the application of traditional group-testing (GT) methods, we propose a multi-level GT scheme, which builds upon GT principles to enable accurate recovery using much fewer tests than patients, while operating in a one-shot manner and providing multi-level indications. We provide a theoretical analysis of the proposed scheme and characterize conditions under which the algorithm operates reliably and at affordable computational complexity. Our numerical results demonstrate that multi-level GT accurately and efficiently detects infection levels, while achieving improved performance and less pooled tests over previously proposed oneshot COVID-19 pooled-testing methods. Our simulations show that the efficient method proposed in this work can correctly identify the infected items and their infection levels with high probability at the known upper bound (for a maximum likelihood decoder in GT) on the number of tests. We also show that the method works well in practice when the number of infected items is not assumed to be known in advance. Full article

This study conducts a simulation-based assessment of a recently commissioned office building in the Republic of Korea, representing a typical public office facility. The building was modeled using EnergyPlus 23.1.0 after construction, although no validation was performed due to the absence of metered consumption data. Previous approaches relying on simplified methods such as the Radiant Time Series (RTS), which neglect dynamic building behavior, have often led to overestimated cooling and heating loads. This has emerged as a major obstacle in designing energy-efficient buildings within the context of compact and smart cities pursuing carbon neutrality. Consequently, the trend in building performance analysis is shifting toward dynamic simulations and digital twin-based design methodologies. Furthermore, electrification of buildings without adequate thermal load assessment may also contribute to overdesign, irrespective of urban environmental characteristics. From an urban planning standpoint, there is a growing need for performance criteria that reflect occupant behavior and actual usage patterns. However, dynamics-based building studies remain scarce in the Republic of Korea. In this context, the present study demonstrates that passive design strategies, implemented through systematic changes in envelope materials, HVAC operational standards, and compliance with ASHRAE 90.1 criteria, can significantly enhance thermal comfort and indoor air quality. The simulation results show that energy consumption can be reduced by over 36.21% without compromising occupant health or comfort. These findings underscore the importance of thermal load understanding prior to electrification and highlight the potential of LEED-aligned passive strategies for achieving high-performance, low-energy buildings. Full article

Urban planning in arid climates must overcome numerous nonclimatic constraints that often result in outdoor thermal discomfort. This is particularly evident in Béchar, a city in southern Algeria known for its long, intense summers with temperatures frequently exceeding 45 °C. This study investigates the influence of urban morphology on thermal comfort and explores architectural and digital solutions to enhance energy performance in buildings. This research focuses on Béchar’s city center, where various urban configurations were analyzed using a multidisciplinary approach that combines typomorphological and climatic analysis with numerical simulations (ENVI-met 3.0 and TRNSYS 16). The results show that shaded zones near buildings have lower thermal loads (under +20 W/m²), while open areas may reach +100 W/m². The thermal comfort rate varies between 22% and 60%, depending on wall materials and occupancy patterns. High thermal inertia materials, such as stone and compressed stabilized earth blocks (CSEBs), reduce hot discomfort hours to under 1700 h/year but may increase cold discomfort. Combining these materials with targeted insulation improves thermal balance. Key recommendations include compact urban forms, vegetation, shading devices, and high-performance envelopes. Early integration of these strategies can significantly enhance thermal comfort and reduce energy demand in Saharan cities. Full article

In order to explore new wall panel materials and structural systems suitable for prefabricated buildings, this study proposes a polypropylene fiber-reinforced concrete sandwich wall panel (PFRC sandwich wall panel) and a polypropylene fiber-reinforced concrete sandwich wall panel with glass fiber grid (G-PFRC sandwich wall panel). A comparative investigation was conducted using finite element analysis to numerically simulate the mechanical response of these composite wall panels under impact loads. The simulation results were compared with those of an unreinforced concrete sandwich wall panel with glass fiber grid (G-UC sandwich wall panel). Key findings include: (1) Compared with the G-UC sandwich wall panel, the G-PFRC sandwich wall panel exhibited 19.3% lower peak deformation and 23.7% reduced residual deformation; (2) Relative to the standard PFRC sandwich wall panel, the G-PFRC sandwich wall panel demonstrated 16.5% smaller peak deformation and 27.9% less residual deformation under impact loads; (3) Damage analysis revealed that the G-PFRC sandwich wall panel developed fewer cracks with lower damage severity compared to both the PFRC and G-UC sandwich wall panels. Parametric studies further indicated that the G-PFRC sandwich wall panel maintains superior deformation resistance and impact performance across varying impact heights and impact masses. The synergistic combination of polypropylene fiber with a glass fiber grid significantly enhances the impact resistance of composite sandwich panels, providing valuable theoretical insights for engineering applications of these novel wall systems in prefabricated construction. Full article

To meet the requirements of a prefabricated building with specific strength limitations and assembly rate criteria, the research proposes a Low-Strength Foamed Concrete Cold-Formed Steel (CFS) Framing Composite Enclosure Wall Panel (LFSW). The ABAQUS 2024 finite element analysis (FEA) combined with bending performance tests on five specimens were employed to examine crack propagation and failure modes of wall panels under wind load, investigating the influence mechanisms of foamed concrete strength, CFS framing wall thickness, CFS framing section height, and concrete cover thickness on the flexural performance of wall panels. The experimental results demonstrate that increasing the steel thickness from 1.8 mm to 2.5 mm enhances the ultimate load-carrying capacity by 46.15%, while enlarging the section height from 80 mm to 100 mm improves capacity by 26.67%. When the foamed concrete strength increased from 0.5 MPa to 1.0 MPa, the wall panel cracking load increased by 50%, while the ultimate load capacity changed by less than 5%. Increasing the concrete cover thickness from 25 mm to 35 mm enhanced the ultimate capacity by 7%, indicating that both parameters exert limited influence on the composite wall panel’s flexural capacity. Finite element simulations demonstrate excellent agreement with experimental results, confirming effective composite action between foamed concrete and CFS framing under service conditions. This validation establishes that the simplified analytical model neglecting interface slip provides better accuracy for engineering design, offering theoretical foundations and practical references for optimizing prefabricated building envelope systems. Full article

The architecture, engineering, and construction (AEC) industry is highly fragmented, yet decisions made during the design phase critically shape downstream sustainability performance. Unlike prior research that primarily weighted interactions by frequency, this study introduces an Interaction-Quality Index that evaluates the quality of design team interactions. This represents a novel approach, as it combines Social Network Analysis with Monte Carlo simulation to quantify how collaboration, coordination, and trust influence sustainable outcomes in construction projects. Through a structured literature review, three systemic interaction drivers; collaboration, coordination, and trust were identified. An interaction-quality index was then formulated, weighting each driver according to its relative impact on sustainable outcomes. Social Network Analysis coupled with Monte Carlo simulation validated the index in a real-world building project, demonstrating its usefulness in identifying critical interaction nodes and highlighting how improvements in collaboration, coordination, and trust can strengthen network cohesion and enhance sustainability-oriented decision-making. The proposed index offers construction managers a quantitative tool to integrate social dynamics into holistic sustainability strategies, advancing practice in line with systems-thinking approaches to sustainable construction management. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

Conventional project scheduling techniques often rely on manual trial-and-error methods, which can lead to inaccurate evaluations. This study presents a dynamic scheduling framework to dynamically adjust scheduling decisions based on real-time productivity and budget constraints, resulting in improvement in scheduling accuracy in project management. By integrating advanced computational tools, the proposed approach addresses complex scheduling challenges. The model integrates Building Information Modeling (BIM)-based 3D data, productivity and process simulation, and optimization techniques to provide a unified scheduling tool that supports informed decision-making while considering real-time constraints, including productivity performance and budget limitations. The results demonstrated notable improvements over conventional methods, including a 13% increase in scheduling accuracy relative to the actual total project cost and a 34.4% improvement in scheduling accuracy based on the actual project duration, compared to the contractor’s baseline. The framework dynamically adjusts schedules and budgets according to current project conditions. These findings demonstrate its reliability as a decision-making tool for construction project management. The study introduces an integrative scheduling framework that adapts to real-time project conditions and is validated against actual project data. The integration of BIM, system dynamics, process simulation, and ACOR optimization provides a novel approach to construction scheduling. This methodology improves project management efficiency by automating scheduling adjustments based on ongoing progress. Full article

Wind-induced erosion and extreme weather events pose growing risks to the structural integrity of masonry heritage buildings. However, current mitigation approaches often overlook ecological sustainability. This study investigates the wind-regulating effects of vegetation surrounding the Zhen Guo Tower, a 400-year-old masonry structure in Weihui, Henan Province, China. Using computational fluid dynamics (CFD) simulations, we first assess the protective performance of the existing vegetation layout and then develop and evaluate an optimized plant configuration. The results show that the proposed multilayered vegetation arrangement effectively reduces wind speeds by up to 13.57 m/s under extreme wind conditions, particularly within the 5–15 m height range. Wind protection efficiency improved by 28–68% compared to the baseline. This study demonstrates a replicable and ecologically integrated strategy for mitigating wind hazards in masonry heritage sites through vegetation-based interventions. Full article

The reliability of the EDS (Electric Drive System) in electric vehicles is crucial to overall vehicle performance. This study addresses the challenge of acquiring high-fidelity internal load data in the early development phase due to the absence of prototypes, overcoming the limitations of traditional road tests, which are costly, time-consuming, and unable to measure gear meshing forces. A method based on a VPG (Virtual Proving Ground) is proposed to acquire internal loads of a dual-motor EDS, analyze the impact of typical virtual fatigue durability road conditions on critical components, and generate load spectra for test bench experiments. Through point cloud data-based road modeling and rigid-flexible coupled simulation, dynamic loads are accurately extracted, with pseudo-damage contributions from eight intensified road conditions quantified using pseudo-damage calculations, and equivalent sinusoidal load spectra generated using the rainflow counting method and linear cumulative damage theory. Compared to the limitations of existing VPG methods that rely on simplified models, this study enhances the accuracy of internal load extraction, providing technical support for EDS durability testing. Building on existing research, it focuses on high-fidelity acquisition of EDS loads and load spectrum generation, improving applicability and addressing deficiencies in simulation accuracy. This study represents a novel application of VPG technology in electric drive system development, resolving the issue of insufficient early-stage load spectra. It provides data support for durability optimization and bench testing, with future validation planned using real vehicle data. Full article

This paper addresses the issue of the poor collision avoidance effect of underactuated surface vehicles (USVs) during local path tracking. A virtual ship group control method is suggested by using Freiner coordinates and a model predictive control (MPC) algorithm. We track the planned path using the MPC algorithm according to the known vessel state and build a hierarchical weighted cost function to handle the state of the virtual vessel, to ensure that the vessel avoids obstacles while tracking the path. In addition, the control system incorporates an Extended Kalman Filter (EKF) algorithm to minimize the state estimation error by continuously updating the ship state and providing more accurate state estimation for the system in a timely manner. In order to validate the anti-interference and robustness of the control system, the simulation experiment is carried out with the “Yukun” as the research object by adding the interference of wind and wave of level 6. The outcome shows that the algorithm suggested in this paper can accurately perform the trajectory-tracking task and make collision avoidance decisions under six levels of external interference. Compared with the original MPC algorithm, the improved MPC algorithm reduces the maximum rudder angle output value by 58%, the integral absolute error by 46%, and the root mean square error value by 46%. The improved control algorithm reduces the maximum rudder angle output value by 42% and the maximum rudder angle output value by 10%. The control method provides a new technical choice for trajectory tracking and collision avoidance of USVs in complex marine environments, with a reliable theoretical basis and practical application value. Full article

In this paper, we present an extremely compact eleven-state microwave filter with four concentric split-ring resonators (SRRs). Reconfigurability is achieved by switching off either single or multiple SRRs, thereby obtaining different triple-band, dual-band, and single-band configurations from the initial quad-band topology. Switches are placed on the vertical branches of SRRs in order to minimize the additional insertion loss. As switching elements, we first use traditional RF switches—PIN diodes—and then examine the integration of non-volatile RF switches—memristors—into filter design. Memristors’ ability to remember previous electrical states makes them a main building block for designing circuits that are both energy-efficient and adaptive, opening a new era in electronics and artificial intelligence. As RF memristors are not commercially available, PIN diodes are used for experimental filter verification. Afterwards, we compare the filter characteristics realized with PIN diodes and memristors to present capabilities of memristor technology. Memristors require no bias, and their parasitic effects are modeled with low resistance for the ON state and low capacitance for the OFF state. Measured performances of all obtained configurations are in good agreement with the simulations. The filter footprint area is 26 mm × 29 mm on DiClad substrate. Full article

This study presents a simulation-based analysis of a steel modular building that integrates technologies that support the energy transition in the built environment. The focus is placed on the implementation of building-integrated photovoltaics (BIPVs), with photovoltaic modules incorporated into the façade and balcony railings. Several modern photovoltaic façade systems were examined. In addition, the study considers the application of photovoltaic glazing enhanced with active quantum coatings. Seven distinct BIPV modules were analysed, each characterised by unique features, with particular emphasis on the influence of colour in tinted variants. A performance degradation analysis was conducted for railing-mounted modules with varying glass tints. The simulation results were correlated with the building’s electricity demand. Full article