Search Results (3,505)

With the acceleration of urbanization and the continuous expansion of building stock, building maintenance plays a critical role in ensuring structural safety, extending service life, and promoting sustainable development. In recent years, the application of artificial intelligence (AI) in building maintenance has expanded significantly, markedly improving detection accuracy and decision-making efficiency through predictive maintenance, automated defect recognition, and multi-source data integration. Although existing studies have made progress in predictive maintenance, defect identification, and data fusion, systematic quantitative analyses of the overall knowledge structure, research hotspots, and technological evolution in this field remain limited. To address this gap, this study retrieved 423 relevant publications from the Web of Science Core Collection covering the period 2000–2025 and conducted a systematic bibliometric and scientometric analysis using tools such as bibliometrix and VOSviewer. The results indicate that the field has entered a phase of rapid growth since 2017, forming four major thematic clusters: (1) intelligent construction and digital twin integration; (2) predictive maintenance and health management; (3) algorithmic innovation and performance evaluation; and (4) deep learning-driven structural inspection and automated operation and maintenance. Research hotspots are evolving from passive monitoring to proactive prediction, and further toward system-level intelligent decision-making and multi-technology integration. Emerging directions include digital twins, energy efficiency management, green buildings, cultural heritage preservation, and climate-adaptive architecture. This study constructs, for the first time, a systematic knowledge framework for AI-enabled building maintenance, revealing the research frontiers and future trends, thereby providing both data-driven support and theoretical reference for interdisciplinary collaboration and the practical implementation of intelligent maintenance. Full article

Methods, algorithms, and models for the creation and practical application of digital twins (3D models) of agricultural crops are presented, illustrating their condition under different levels of atmospheric CO₂ concentration, soil, and meteorological conditions. An algorithm for digital phenotyping using machine learning methods with the U²-Net architecture are proposed for segmenting plants into elements and assessing their condition. To obtain a dataset and conduct verification experiments, a prototype of a software and hardware complex has been developed that implements the process of cultivation and digital phenotyping without disturbing the microclimate inside the chamber and eliminating the subjectivity of measurements. In order to identify new data and confirm the data published in open scientific sources on the effects of CO₂ on crop growth and development, plants (ten species) were grown at different CO₂ concentrations (0.015–0.03% and 0.07–0.09%) with a 10-fold repetition. A model has been built and trained to distinguish between cases when plant segments need to be combined because they belong to the same leaf (p-value = 0.05), and when they belong to a separate leaf (p-value = 0.03). A knowledge base has been formed, including: 790 3D models of plants and data on their physiological characteristics. Full article

Occupant modeling has emerged as a critical component in human-centric building design and operation, offering detailed insights into energy performance, comfort optimization, and behavior-driven control strategies. This study systematically examines occupant modeling (OM) in building design through a review of 312 articles, highlighting critical gaps between theoretical frameworks and real-world applications. Key dimensions of occupant modeling, including methodological classification, data frameworks, application scenarios and model selection strategies, are examined. The interpretability, advantages and disadvantages of 5 modeling methods are demonstrated, and the tools, algorithms and applications are analyzed. In addition, common input, output and application scenarios are sorted out and the data streams are presented. Results have shown that hybrid models represent breakthroughs but require validation beyond idealized scenarios. Meanwhile, with 88.7% of output derived from simulated results, risking self-reinforcing biases despite empirical inputs. Standardized protocols for model validation and hybrid modeling frameworks are urgently needed. To support model selection, a decision-oriented framework is proposed, integrating modeling goals, data characteristics, behavioral complexity, and platform interoperability. Future priorities include merging high explanatory methods with powerful predictive methods, advancing BIM-IoT symbiosis for adaptive digital twin, expanding to interdisciplinary projects, and establishing ethical data governance to align technical advancements with equitable, occupant-centric design. Full article

This paper introduces a structured methodology for bridging the gap between theoretical modeling and high-fidelity simulation of multirotor Unmanned Aerial Systems (UAS) through the construction of digital twins in PX4 v1.12 Software-in-the-Loop (SITL) environments. A key challenge addressed is the absence of standardized procedures for translating physical UAV characteristics into simulation-ready parameters, which often results in inconsistencies between virtual and real-world behavior. To overcome this, we propose a hybrid parametric identification pipeline that combines analytical modeling with experimental characterization. Critical parameters—such as inertial properties, thrust and torque coefficients, drag factors, and motor response profiles—are obtained through a combination of physical measurements and theoretical derivation. The proposed methodology is demonstrated on a custom-built heavy-lift quadrotor, and the resulting digital twin is validated by executing autonomous missions and comparing simulated outputs against flight logs from real-world tests. Full article

Efficient coordination among software modules is essential for biomimetic robotic systems, much like the interaction among organs in a biological organism. However, implementing inter-process or inter-module communication in autonomous systems remains a complex and time-consuming task, particularly for new researchers. Simplifying inter-module communication is the central focus of this study. To address this challenge, we propose the DigitalTwinPort framework, a novel communication abstraction inspired by the port-based connectivity of embedded hardware systems. Unlike middleware-dependent solutions such as ROS, the proposed framework provides a lightweight, object-oriented structure that enables unified and scalable communication between software modules and networked devices. The concept is implemented in C++ and validated through an autonomous surface vehicle (ASV) developed for the RobotX Challenge. Results demonstrate that the DigitalTwinPort simplifies the development of distributed systems, reduces configuration overhead, and enhances synchronization between digital and physical components. This work lays the foundation for future digital twin architectures in embodied Internet systems, where software and hardware can interact seamlessly through standardized digital ports. Full article

As climate change mitigation intensifies, retrofitting existing buildings has emerged as a critical and cost-effective strategy to improve energy performance, resilience, and sustainability. This systematic literature review (SLR) analysed 97 peer-reviewed articles published between 2015 and 2025, retrieved from the Scopus database using a title-based search strategy combining keywords related to building performance and retrofit actions. A five-stage screening process was employed to refine results based on publication type, discipline relevance, and research alignment. VOSviewer was used for scientometric mapping, complemented by descriptive and content analyses, to identify six thematic clusters: envelope optimisation, energy economics, environmental quality, system efficiency, passive retrofitting, and digital/data-driven planning. The review also applies a PESTEL framework to evaluate retrofit benefits across political, economic, social, technological, environmental, and legal dimensions. Finally, seven future research directions are proposed, including digital twin (DT) integration, artificial intelligence (AI) adoption, circular economy (CE) principles, stakeholder engagement, and climate-resilient design. By consolidating fragmented research, this study provides actionable insights for scholars, practitioners, and policymakers, establishing building retrofitting as a strategic pathway toward sustainable and climate-responsive urban development. Full article

Anaerobic digestion (AD) is increasingly recognized as a key technology for renewable energy generation and sustainable waste management within the circular economy. However, its performance is highly sensitive to feedstock variability and environmental fluctuations, making stable operation and high methane yields difficult to sustain. Conventional monitoring and control systems, based on limited sensors and mechanistic models, often fail to anticipate disturbances or optimize process performance. This review discusses recent progress in electrochemical, optical, spectroscopic, microbial, and hybrid sensors, highlighting their advantages and limitations in artificial intelligence (AI)-assisted monitoring. The role of soft sensors, data preprocessing, feature engineering, and explainable AI is emphasized to enable predictive and adaptive process control. Various machine learning (ML) techniques, including neural networks, support vector machines, ensemble methods, and hybrid gray-box models, are evaluated for yield forecasting, anomaly detection, and operational optimization. Persistent challenges include sensor fouling, calibration drift, and the lack of standardized open datasets. Emerging strategies such as digital twins, data augmentation, and automated optimization frameworks are proposed to address these issues. Future progress will rely on more robust sensors, shared datasets, and interpretable AI tools to achieve predictive, transparent, and efficient biogas production supporting the energy transition. Full article

In Notre-Dame de Paris’ digital twin, the massive data is characterized by its variability in terms of production and documentation. The question of provenance appears as the missing link in digital heritage data and a fortiori in the provenance of knowledge. The problem can be formulated as follows: the heterogeneity of data means variability as multi-device, multitemporal, multiscalar, with spatial granularity, and multi-layered and semantic complexity. The objective of this article is to improve the quality and consistency of paradata and to bridge the practical gap between mass 3D digitization and mass data enrichment in the data lineage of cultural heritage digital collections. FAIR principles, provenance, and context are keys in the data management workflows. We propose an innovative solution to integrate provenance and context seamlessly into these workflows, enabling more cohesive and reliable data enrichment. In this article, we use both conceptual modeling and quick prototyping: we posit that existing conceptual models can be used as complementary modules to document the provenance and context of research activity metadata. We focus on three models, namely the W7, the PROV ontology, and the CIDOC CRM. These models express different aspects of data and knowledge provenance. The use case from Notre-Dame de Paris’ research demonstrates the validity of the proposed hybrid modular conceptual modeling to dynamically manage the Provenance Level of Detail in cultural heritage data. Full article

Digital Twins provide comprehensive capabilities to solve critical logistics problems such as visibility, monitoring, optimization, prediction, and simulation. This study explores the existing DT readiness assessment models in SCs and logistics, discovers their limitations, and proposes a conceptual model based on an organization’s internal and external attributes to strategize DT implementation in logistic functions. The results showed that the existing readiness assessment models have weaknesses and drawbacks, motivating the researchers to develop a new logistic DT readiness assessment model. This study identified six main organizational dimensions directly affecting measuring overall logistics’ DT readiness, which are management readiness, personnel readiness, information readiness, organization readiness, product readiness, and process flow readiness. Their relationship is mediated by Technology Integration and moderated by Supply Chain Complexity, which was tested using partial least squares structural equation modeling to show the importance of strategizing DT implementation in logistics. Full article

In smart home projects, building data management at the design stage increasingly relies on digital-twin assets delivered via game engines. Without a clear governance workflow, however, these practices tend to generate non-standard building data on the consumption side, causing broken data chains and increasing construction and management risks. To address this problem, this study proposes a traceability-oriented governance workflow that strengthens IfcGUID compliance and automatically detects and converts inconsistent digital-twin assets into IFC-compliant, auditable data, thereby reducing data chain breakage and improving cross-system traceability in building data management. The workflow uses IfcGUID as a cross-system primary key and is evaluated in a virtual smart home project through a pre-test–repair–post-test experiment at the design stage. We examine four indicators of IfcGUID quality—completeness, validity, uniqueness, and stability—together with a bridge recognition rate that reflects game engine interoperability on the consumption side. The results show that all four IfcGUID indicators converge towards 1 after applying the workflow, and the bridge recognition rate approaches 100%, indicating that the risk of data chain breakage, measured on an IFC basis, is substantially reduced. Within existing toolchains, this workflow provides design teams, visualization teams, clients, and auditors with a low-cost and reproducible path for standardizing design-stage digital-twin assets and establishing a traceable, auditable baseline for cross-system interoperability and lifecycle building data management and data reuse. Full article

Building cycling-friendly street environments is crucial for promoting sustainable urban mobility. However, existing studies exploring the influence of the built environment on cycling have paid limited attention to the three-dimensional characteristics of street landscapes and have mostly relied on linear assumptions. To address these gaps, this study employs street view imagery and interpretable machine learning methods to investigate the nonlinear and interaction effects of street landscape elements on residents’ cycling preferences in Xiamen Island, China. The results reveal that the visual indices of buildings, sky, vegetation, and roads are the most influential variables affecting cycling preferences. These factors exhibit pronounced nonlinear relationships with cycling preference. For instance, buildings exhibit a threshold effect, with positive influences on cycling preference when the building index is below 0.12 and negative effects when it exceeds 0.12. A low sky index significantly suppresses cycling preference, whereas higher values offer only limited additional benefits, with an optimal range of 0.1–0.25. Vegetation contributes positively only at relatively high levels, suggesting that its index should ideally exceed 0.3. The road index shows a V-shaped relationship: values between 0.15 and 0.25 reduce cycling preference, whereas values below 0.15 or above 0.25 enhance it. Moreover, clear interaction effects among these variables are observed, suggesting that the combined visual composition of the streetscape plays an important role in shaping cycling preferences. These findings deepen the understanding of how street landscape characteristics influence cycling behavior and provide nuanced, practical insights for designing cycling-friendly streets and promoting sustainable travel in urban environments. Full article

Background: This study investigates how varying levels of digital interoperability affect coordination and performance in Indonesia’s decentralized air cargo system, reflecting the inefficiencies typical of fragmented digital infrastructures in developing economies. Methods: An Agent-Based Model (ABM) was developed to simulate interactions among shippers, freight forwarders, airlines, ground handlers, and customs agents along the CGK–SIN/HKG export corridor. Six simulation scenarios combined varying levels of digital adoption, operational friction, and behavioral adaptivity to capture emergent coordination patterns and threshold dynamics. Results: The simulation identified a distinct interoperability threshold at approximately 60%, beyond which performance improvements became non-linear. Once this threshold was surpassed, clearance times decreased by more than 40%, and capacity utilization exceeded 85%, particularly when adaptive decision rules were implemented among agents. Conclusions: Digital transformation in fragmented logistics systems requires both technological connectivity and behavioral adaptivity. The proposed hybrid framework—integrating Autonomous Supply Chains (ASC), Graph-Based Digital Twins (GBDT), and interoperability thresholds—provides a simulation-based decision-support tool to determine when digitalization yields system-wide benefits. The study contributes theoretically by linking behavioral adaptivity and digital interoperability within a unified modeling approach, and practically by offering a quantitative benchmark for policymakers and practitioners seeking to develop efficient and resilient logistics ecosystems. Full article

This study introduces a physics-based inverse rendering method for determining the reduced scattering and absorption coefficients of turbid materials with arbitrary shapes, using a single image as input. The approach enables fully spectrally-resolved reconstruction of the wavelength-dependent behaviour of the optical properties while also circumventing the specialised sample preparation required by established measurement techniques. Our approach employs a numerical solution of the Radiative Transfer Equation based on an inverse Monte Carlo framework, utilising an improved Levenberg–Marquardt algorithm. By rendering the edge effects accurately, particularly translucency, it becomes possible to differentiate between scattering and absorption from just one image. Importantly, the errors induced by only approximate prior knowledge of the phase function and refractive index of the material were quantified. The method was validated through theoretical studies on three materials spanning a range of optical parameters, initially using a simple cube geometry and later extended to more complex shapes. Evaluated via the CIE $\Delta E_{2000}$ colour difference, forward renderings based on the recovered properties were indistinguishable from those preset, which were obtained from integrating sphere measurements on real materials. The recovered optical properties showed less than 4% difference relative to these measurements. This work demonstrates a versatile approach for optical material characterisation, with significant potential for digital twin creation and soft-proofing in manufacturing. Full article

This study explores the transformative role of Cyber-Physical Power System (CPPS) Digital Twins (DTs) in enhancing the operational resilience, flexibility, and intelligence of modern power grids. By integrating physical system models with real-time cyber elements, CPPS DTs provide a synchronized framework for real-time monitoring, predictive maintenance, energy management, and cybersecurity. A structured literature review was conducted using the ProKnow-C methodology, yielding a curated portfolio of 74 publications from 2017 to 2025. This corpus was analyzed to identify key application areas, enabling technologies, simulation methods, and conceptual maturity levels of CPPS DTs. The study highlights seven primary application domains, including real-time decision support and cybersecurity, while emphasizing essential enablers such as data acquisition systems, cloud/edge computing, and advanced simulation techniques like co-simulation and hardware-in-the-loop testing. Despite significant academic interest, real-world implementations remain limited due to interoperability and integration challenges. The paper identifies gaps in standard definitions, maturity models, and simulation frameworks, underscoring the need for scalable, secure, and interoperable architectures and highlighting key areas for scientific development and real-life application of CPPS DTs, such as grid predictive maintenance, forecasting, fault handling, and power system cybersecurity. Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article