Miriam Di Matteo^*, Domiziana Vespasiano, Gianluigi Lo Basso, Costanza Vittoria Fiorini, Andrea Vallati

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.064353 - 27 January 2026

Abstract Decarbonising the building sector, particularly residential heating, represents a critical challenge for achieving carbon-neutral energy systems. Efficient solutions must integrate both technological performance and renewable energy sources while considering operational constraints of existing systems. This study investigates a hybrid heating system combining a natural gas boiler (NGB) with an air-to-water heat pump (AWHP), evaluated through a combination of laboratory experiments and dynamic modelling. A prototype developed in the Electrical and Energy Engineering Laboratory enabled the characterization of both heat generators, the collection of experimental data, and the calibration of a MATLAB/Simulink model, including emissions and… More >

Nur Afikah Juhari, Khairul Nizam Tahar^*

Revue Internationale de Géomatique, Vol.35, pp. 1-11, 2026, DOI:10.32604/rig.2026.070991 - 19 January 2026

Abstract The use of Unmanned Aerial Vehicles (UAVs) in photogrammetry has grown rapidly due to enhanced flight stability, high-resolution imaging, and advanced Structure from Motion (SfM) algorithms. This study investigates the potential of UAVs as a cost-effective alternative to Terrestrial Laser Scanners (TLS) for 3D building reconstruction. A 3D model of Bangunan Sarjana was generated in Agisoft Metashape Professional v.2.0.2 using 492 aerial images captured at flying altitudes of 40, 50, and 60 m. Ground control points were established using GNSS (RTK-VRS), and Total Station measurements were employed for accuracy validation. The results indicate that the 60 More >

Manaliben Amin^*

Journal on Artificial Intelligence, Vol.8, pp. 1-18, 2026, DOI:10.32604/jai.2026.073895 - 07 January 2026

Abstract Artificial intelligence (AI) is steadily making its way into pharmaceutical validation, where it promises faster documentation, smarter testing strategies, and better handling of deviations. These gains are attractive, but in a regulated environment speed is never enough. Regulators want assurance that every system is reliable, that decisions are explainable, and that human accountability remains central. This paper sets out a Human-in-the-Loop (HITL) AI approach for Computer System Validation (CSV) and Computer Software Assurance (CSA). It relies on explainable AI (XAI) tools but keeps structured human review in place, so automation can be used without creating… More >

Samira Rastbod¹, Mehdi Jahangiri^2,*, Behrang Moradi¹, Haleh Nazari¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070089 - 27 December 2025

Abstract Curtain wall systems have evolved from aesthetic façade elements into multifunctional building envelopes that actively contribute to energy efficiency and climate responsiveness. This review presents a comprehensive examination of curtain walls from an energy-engineering perspective, highlighting their structural typologies (Stick and Unitized), material configurations, and integration with smart technologies such as electrochromic glazing, parametric design algorithms, and Building Management Systems (BMS). The study explores the thermal, acoustic, and solar performance of curtain walls across various climatic zones, supported by comparative analyses and iconic case studies including Apple Park, Burj Khalifa, and Milad Tower. Key challenges—including… More > Graphic Abstract

Jia Liu¹, Hao Chen¹, Hang Gu¹, Yushan Pan^2,3, Haoran Chen¹, Erlin Tian⁴, Min Huang⁴, Zuhe Li^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.068162 - 10 November 2025

Abstract Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning, disaster emergency response, and resource management. However, existing methods face challenges such as spectral similarity between buildings and backgrounds, sensor variations, and insufficient computational efficiency. To address these challenges, this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network (MewCDNet), which integrates the advantages of Convolutional Neural Networks and Transformers, balances computational costs, and achieves high-performance building change detection. The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction, integrates multi-level feature maps through a multi-scale fusion… More >

Victor De Araujo^1,2,3,*, Pedro Jardim^3,4, Poliana Pessôa³, Juliano Vasconcelos^2,5, Matheus Souza⁶, José Garcia⁷, Jozef Švajlenka⁸, André Christoforo^3,1

Journal of Renewable Materials, Vol.13, No.12, pp. 2479-2524, 2025, DOI:10.32604/jrm.2025.02025-0127 - 23 December 2025

Abstract Finger-joint lumber is a sustainable building product commercialized as a structural solution for beams, pillars and other thin flat load-bearing elements. This study aims to study finger-joint lumber and its industry to promote this engineered wood product. The first research stage assessed the collection of publications on finger-joint lumber available globally, in which a structured protocol was developed to prospect studies based on two complementary methodologies: PRISMA 2020 using Scopus and Web of Science databases, and Snowball using both forward and backward models to complete with additional literature. The second research stage assessed finger-joint lumber… More >

Saeed Asadi¹, Hajar Kazemi Naeini¹, Delaram Hassanlou², Abolhassan Pishahang³, Saeid Aghasoleymani Najafabadi⁴, Abbas Sharifi⁵, Mohsen Ahmadi^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1259-1301, 2025, DOI:10.32604/cmes.2025.070528 - 26 November 2025

Abstract The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control.… More >

Abdullah Alariyan¹, Rawand A. Mohammed Amin², Ameen Mokhles Youns³, Mahmoud Alhashash⁴, Favzi Ghreivati⁵, Ahed Habib^6,*, Maan Habib⁷

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1387-1410, 2025, DOI:10.32604/sdhm.2025.069821 - 17 November 2025

Abstract Artificial intelligence (AI) is transforming the building and construction sector, enabling enhanced design strategies for achieving durable and sustainable structures. Traditional methods of design and construction often struggle to adequately predict building longevity, optimize material use, and maintain sustainability throughout a building’s lifecycle. AI technologies, including machine learning, deep learning, and digital twins, present advanced capabilities to overcome these limitations by providing precise predictive analytics, real-time monitoring, and proactive maintenance solutions. This study explores the benefits of integrating AI into building design and construction processes, highlighting key advantages such as improved durability, optimized resource efficiency,… More >

Kavita Bodke^1,*, Sunil Bhirud¹, Keshav Kashinath Sangle²

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1547-1562, 2025, DOI:10.32604/sdhm.2025.069239 - 17 November 2025

Abstract Structural Health Monitoring (SHM) systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity. There is a need for more efficient techniques to detect defects, as traditional methods are often prone to human error, and this issue is also addressed through image processing (IP). In addition to IP, automated, accurate, and real- time detection of structural defects, such as cracks, corrosion, and material degradation that conventional inspection techniques may miss, is made possible by Artificial Intelligence (AI) technologies like Machine Learning (ML) and Deep Learning… More > Graphic Abstract

Yating Xu¹, Mansheng Xiao^1,*, Mengxing Gao¹, Zhenzhen Liu¹, Zeyu Xiao²

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1635-1656, 2025, DOI:10.32604/sdhm.2025.068822 - 17 November 2025

Abstract During the operation, maintenance and upkeep of concrete buildings, surface cracks are often regarded as important warning signs of potential damage. Their precise segmentation plays a key role in assessing the health of a building. Traditional manual inspection is subjective, inefficient and has safety hazards. In contrast, current mainstream computer vision–based crack segmentation methods still suffer from missed detections, false detections, and segmentation discontinuities. These problems are particularly evident when dealing with small cracks, complex backgrounds, and blurred boundaries. For this reason, this paper proposes a lightweight building surface crack segmentation method, HL-YOLO, based on… More >