Search Results (1,957)

Post-blast operations in tunnel construction represent a critical bottleneck due to mandatory downtime and hazardous environmental conditions. This study addresses these challenges by developing and validating an integrated cyber–physical architecture that coordinates an autonomous Unmanned Aerial Vehicle (UAV) and an Autonomous Wheel Loader (AWL) under the supervision of a Digital Twin acting as central operational digital interface. Specifically, this technology was designed to access the tunnel, evaluate post-blasting conditions, and initiate operations during mandatory exclusion periods for personnel. The system was validated in a realistic, Global Navigation Satellite System (GNSS)-denied tunnel environment emulating post-detonation visibility constraints. The results demonstrate that the aerial agent successfully navigated and mapped the excavation front in less than 8 min, establishing a shared coordinate system for the ground machinery. Through this collaborative workflow, the autonomous deployment enabled operations to commence 50% to 80% earlier than conventional manual procedures. Furthermore, the system reduced daily operational time by approximately 8%, with an estimated return on financial investment between one and seven months. Overall, the proposed framework eliminates human exposure during high-risk inspections and transforms the fragmented excavation cycle into a continuous, data-driven process. Full article

Dynamic response measurements support bridge monitoring and structural assessment because they are obtainable under operational loading and are sensitive to changes in stiffness, boundary conditions, and mass distribution. This article presents a structured scoping review of dynamic-response-based bridge monitoring and assessment. It covers damage-sensitive indicators, stiffness/capacity proxy inference, interpretation under operational and extreme loading, sensing with acquisition (contact, and indirect/drive-by), and data processing, machine learning and digital-twin integration for decision support. Evidence was identified through targeted searches in Scopus and The Lens with duplicate resolution in Zotero. The cited studies are compiled into a traceable evidence inventory linked to method families and decision objectives. The synthesis shows that global modal properties enable change screening but are highly confounded by environmental/operational variability. Localization and state characterization typically require denser or higher-fidelity sensing and signal conditioning. Finally, capacity-related inference using calibrated conversion models or machine learning (ML) surrogates remains context-bounded and validation-dependent. This review provides an end-to-end pipeline, evidence-maturity rubric, and conservative failure-mode checks with escalation logic that tie SHM outputs to inspection and analysis rather than direct condition declarations for bridge owners. This review is intentionally scoped and does not claim PRISMA-style comprehensiveness. Full article

Digital Twin (DT) technology is increasingly recognised as a promising approach for predictive and optimised railway maintenance; however, its current applications remain fragmented and lack systematic evaluation across railway domains. This study aims to critically review DT-enabled monitoring, analysis, and maintenance decision-support systems in railway engineering, while identifying key research gaps and future directions. A DT is defined in this study as an integrated cyber–physical system comprising a physical asset, its virtual representation, and continuous bidirectional data exchange enabling real-time monitoring, prediction, and decision-making. A systematic and transparent review methodology was adopted to select 34 representative peer-reviewed studies published between 2020 and 2025, focusing explicitly on DT applications in railway infrastructure and operations. Among these, a subset of 10 key studies was further analysed in greater depth based on their level of technical implementation, data integration capability, and relevance to predictive maintenance applications, which cover multiple domains, including track systems, rolling stock, bridges, and communication networks. Results show that DT-based approaches can enhance fault detection, enable condition-based and predictive maintenance, and reduce reliance on manual inspections. However, significant limitations remain. Most studies are conceptual or pilot-scale, with limited validation under real operating conditions. Key challenges include a lack of standardisation and interoperability, constraints in real-time scalability, data governance and cybersecurity issues, and insufficient integration of multi-source sensing and advanced analytics. This review provides a structured synthesis of current DT implementations in railway systems and highlights critical gaps that must be addressed to enable scalable, reliable, and fully integrated DT-driven maintenance frameworks. Full article

The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article

Consumer wellbeing systems are characterized by conceptual fragmentation, heterogeneous data sources, and multilevel interactions across economic, psychological, social, and environmental domains. Existing monitoring approaches remain largely unidimensional and lack integrative system architectures capable of supporting real-time, adaptive analysis. This paper proposes a Federated Digital Twin (FDT) framework for Consumer Wellbeing Systems, designed to integrate decentralized, multimodal data while preserving autonomy and privacy. The proposed architecture builds on a five-dimensional digital twin model and extends it through federated interoperability, data fusion, adaptive learning, simulation capabilities, and human-in-the-loop mechanisms. The framework enables the synchronization of observed, self-reported, contextual, and synthetic data across distributed environments, supporting system-level modeling, prediction, and optimization. As an illustrative application, the paper examines Shopping Wellbeing and Shopping–Life Balance as sub-systems within broader wellbeing ecosystems, demonstrating how federated digital twins can unify fragmented theoretical constructs into a coherent, dynamic monitoring structure. The study contributes a system-oriented conceptual architecture for modeling complex human-centric wellbeing ecosystems and outlines implications for systems design, governance, and future interdisciplinary research. Full article

Material considerations are often neglected when developing digital twins, particularly at the relevant length scales that drive material and structural performance. For reinforced composite materials, the microscale has the largest impact on nonlinear material behavior and progressive damage, and thus accurately representing the disordered microstructure of a composite due to processing and manufacturing is critical to developing the material digital twin in the multiscale hierarchy. Automating microstructure characterization is typically done by either training convolutional neural network models using a pretrained encoder or using prompt-based segmentation tools. In this work, a toolset for developing segmentation models is presented, combining these two methods to enable rapid annotation, training, and deployment of microscopy segmentation models for automated material digital twin development without user knowledge of machine learning. Additionally, a Bayesian optimization framework is developed for generating statistically equivalent representative volume elements (SRVE) to a segmented microstructure using a random microstructure generator that implements soft body dynamics. Progressive failure analysis of random, statistically equivalent, and ordered microstructures is compared to the segmented microstructure subject to transverse loading to demonstrate the importance of accurately representing the driving material length scale of a composite digital twin. Ordered microstructures over-predicted crack initiation and ultimate strength and strain. Random and optimized RVE microstructures better agreed with the segmented simulation results, with no significant difference observed between the two methodologies. The improvement in predicted macroscale behavior for models that capture disordered microstructures due to manufacturing processes demonstrates the importance of capturing microstructure features in composites modeling and indicates that SRVEs that capture microstructural features of the physical material can be used in material digital twin development. Further, the toolsets provided in this work allow for rapid development of composite material digital twins without user expertise in machine learning. This has enabled the development of an integrated workflow to automatically characterize and idealize composite microstructures and generate representative geometric models for efficient micromechanics analysis. Full article

The industrial metaverse is a digital twin space that integrates the real world with virtual environments through bidirectional synchronization. It supports critical services, such as time-sensitive machine control and large-scale collaboration, which require Time-Sensitive Networking and scalable Data Distribution Services. DDS, developed by the Object Management Group, provides excellent scalability and diverse QoS policies but struggles to guarantee transmission delay and jitter for time-critical applications. TSN, based on IEEE 802.1 standards, addresses these challenges by ensuring time-criticality. However, current research lacks comprehensive integration mechanisms for DDS and TSN, particularly from the viewpoints of semantics and system framework. Additionally, there is no adaptive QoS mapping converting the abstract DDS QoS policies to the sophisticated TSN QoS parameters. This paper presents a novel DDS-over-TSN framework that incorporates three key functions to address these challenges. First, Cross-layer QoS Mapping automates correspondences between DDS and TSN parameters, deriving technical constraints from standard documentation through retrieval-augmented generation. Second, Semantic Priority Estimation extracts substantial priority levels by utilizing language model embedding vectors as high-dimensional feature extractors. Third, Adaptive Resource Allocation performs dynamic bandwidth distribution for each priority level through reinforcement learning. Simulation results reveal over 99% mapping accuracy and 97% consistency in priority extraction. The applied Deep Reinforcement Learning paradigm allocated 99% of required resources to high-priority classes and reduced resource wastage by 15% compared to conventional methods. This methodology meets industrial requirements by ensuring both deterministic real-time performance and efficient resource isolation. Full article

Large-scale aerospace components (e.g., wings, fuselage sections, wing boxes, and rocket segments) feature large dimensions, low stiffness, complex interfaces, and strict assembly tolerances. Traditional rigid tooling and manual alignment struggle to meet the demands of high precision, efficiency, and flexibility in modern aerospace manufacturing. This paper presents a structured literature review on the automated assembly of large-scale aerospace components, summarizing advances in three core domains: pose adjustment and positioning mechanisms, digital measurement technologies, and trajectory planning and control. Particular emphasis is placed on two cross-cutting themes: measurement uncertainty analysis and flexible assembly, which are critical for high-quality docking. The review classifies pose adjustment mechanisms into four categories (NC positioners, parallel kinematic machines, industrial robots, and novel mechanisms) and digital measurement into five branches (vision metrology, large-scale metrology, measurement field construction, uncertainty analysis, and auxiliary techniques). It also outlines five trajectory planning and control routes, covering traditional methods, multi-sensor fusion, digital twins, flexible assembly, and emerging intelligent approaches. The analysis reveals that current research suffers from fragmentation among mechanism design, metrology, and control, with insufficient integration of uncertainty propagation and flexible deformation modeling. Future systems will rely on heterogeneous equipment collaboration, uncertainty-aware closed-loop control, high-fidelity flexible modeling, and digital twin-driven decision-making. This review provides a unified framework and a technical reference for developing reliable, flexible, and scalable automated assembly systems for next-generation aerospace structures. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

With the increasing penetration of converter-based devices, harmonic distortion has become a major challenge for power quality monitoring in large-scale power systems. This study presents a systematic review of methods for modeling harmonic sources and their applicability to real-time monitoring of power distribution systems. The review was conducted following PRISMA guidelines, considering literature published between 2000 and 2026. Searches were performed across Scopus, IEEE Xplore, Web of Science, ScienceDirect, and MDPI using predefined keywords. A total of 128 peer-reviewed journal articles were included. Potential sources of bias were qualitatively assessed, including selection, retrieval, and classification bias; however, residual bias may still arise from database selection, keyword design, and study classification. A structured comparative framework is introduced, based on a six-dimension coverage scoring scheme and maturity analysis, enabling consistent evaluation across both methodological and deployment aspects. The robustness of this framework was evaluated using leave-one-out and perturbation analyses, indicating low variability in coverage scores and stable rankings across both corpora. A taxonomy of harmonic source modeling approaches is proposed. Comparative synthesis indicates that measurement-based approaches, particularly those leveraging distribution-level PMUs, show strong potential for real-time monitoring. Key challenges include D-PMU placement, data integration, and computational scalability. Future work should focus on physics-informed AI and digital twin-based monitoring. Full article

The growing digitalization of supply chains and increasing sustainability requirements create the need for structured tools that assess organizational readiness for Cognitive Twin (CT) adoption. However, existing digital twin and sustainability maturity models rarely integrate technological architecture, governance, and circularity within a unified framework. To address this gap, the study proposes the Supply Chain Twin Sustainability–Cognitive Maturity Model (SCT-SCMM), a novel framework that explicitly integrates governance structures, sustainability objectives, and a hierarchical system architecture into the assessment of Cognitive Twin readiness. Unlike existing models, the proposed framework captures the interdependencies between technological capabilities, decision intelligence, and governance mechanisms across multiple system layers, providing a systemic perspective on sustainable digital transformation. The framework structures organizational readiness through five interdependent layers: Physical, Control, Communication, Decision-making, and Governance, and defines staged maturity levels reflecting progression toward sustainable cognitive autonomy. This layered architecture enables the simultaneous evaluation of operational automation, digital intelligence, and institutional governance as co-evolving dimensions of Cognitive Twin adoption. The model was developed through a structured literature review and operationalized using a hybrid multi-criteria and fuzzy-based evaluation approach, enabling the evaluation of complex socio-technical systems under uncertainty. The framework was applied in an automated product-to-human warehouse case study to evaluate technological, sustainability, and governance readiness. The results demonstrate the model’s ability to identify maturity gaps, reveal inter-layer dependencies, and prioritize transformation pathways toward more resilient and circular logistics systems. By integrating governance, sustainability, and system architecture into a single maturity model, SCT-SCMM extends existing digital twin maturity approaches and provides a transparent decision-support tool for guiding staged Cognitive Twin adoption in next-generation sustainable supply chains. Full article

Alcoholic fermentation in winemaking is a complex bioprocess governed by physicochemical parameters such as temperature, density, pH, CO₂ and redox potential, which critically affect yeast metabolism and wine quality. This review provides an integrated analysis of fermentative dynamics and emerging sensorization technologies, highlighting how their combined implementation enables real-time monitoring and advanced control in precision enology. Advances in conventional physicochemical sensors, spectroscopic techniques (NIR/MIR/UV-Vis) and non-conventional devices (e-noses, electronic tongues) integrated into IoT platforms enable continuous data acquisition, overcoming traditional manual sampling limitations. Predictive modeling, including kinetic models, machine learning approaches (e.g., Random Forest, XGBoost) and model predictive control (MPC/NMPC), supports anomaly detection, optimization of enological interventions and energy-efficient thermal management, while virtual sensors based on Kalman filters improve the estimation of non-measurable states (e.g., biomass, ethanol kinetics). Despite current challenges in calibration and interoperability, these innovations foster sustainable and reproducible winemaking under climate variability and pave the way for digital twins and semi-autonomous fermentation systems. Full article

Production greenhouse complexes increasingly require automation and digitalization to address rising labor costs, improve productivity, and support sustainable resource use. However, most existing solutions target isolated tasks and lack a unified framework for continuous monitoring and production-oriented accounting at facility scale. This paper proposes a system-level architecture that integrates robotic monitoring platforms, AI-based perception, and cloud-based data management into a coherent operational framework. The robotic monitoring platforms operate on rails and concrete surfaces and are capable of elevating cameras and sensors up to 5 m to support plant-health assessment, environmental monitoring, and production accounting. Aggregated data are incorporated into a digital twin that supports spatial traceability, historical analysis, and decision support. The proposed approach enables continuous inspection, improves early detection of crop stress, reduces repetitive manual scouting, and supports targeted interventions. The framework provides a scalable foundation for sustainable, data-driven greenhouse management and practical deployment of robotic monitoring systems in industrial production environments. Full article

The world evolves continuously across space and time. Massive volumes of data are generated through sensing, simulation, remote observation, and human activities, capturing dynamic processes in environmental, social, economic, and engineered systems. Critical insights are embedded within these large-scale spatiotemporal datasets. Spatiotemporal Data Science provides a conceptual and methodological framework for analyzing such data by integrating spatiotemporal thinking, computational infrastructure, artificial intelligence, and domain knowledge. The field advances methods for data acquisition, harmonization, modeling, visualization, and decision support, enabling applications in natural disaster response, public health, climate adaptation, infrastructure resilience, and geopolitical analysis. By leveraging emerging technologies—including generative Artificial Intelligence (AI), large-scale cloud platforms, Graphics Processing Unit (GPU) acceleration, and digital twin systems—Spatiotemporal Data Science enables scalable, interoperable, and solution-oriented research and innovation. It represents a critical frontier for scientific discovery, engineering advancement, technological innovation, education, and societal benefit. Spatiotemporal Data Science is a transdisciplinary field that studies and models dynamic phenomena across space and time by integrating spatial theory, temporal reasoning, artificial intelligence, and scalable computational infrastructure. It enables the development of adaptive, predictive, and increasingly autonomous systems for understanding and managing complex real-world processes. Full article

This paper presents a multimodal transformer-based framework for the joint prediction of indoor thermal comfort and energy efficiency using real-world building management system (BMS) datasets. Unlike traditional comfort models that rely on fixed physical assumptions and subjective surveys, the proposed approach adopts physics-guided, data-driven learning to capture nonlinear and time-dependent interactions among environmental conditions, HVAC operation, and occupancy-related variables. Thermal comfort labels are computed using the PMV–PPD formulation defined by ASHRAE Standard 55, assuming standard metabolic rate and clothing insulation due to the lack of direct measurements in routine BMS data. A temperature-driven baseline HVAC energy proxy is derived using change-point regression. The proposed transformer architecture fuses multivariate temporal sequences to jointly predict both comfort and energy baseline targets through a dual-head regression formulation. The model is validated on two complementary datasets representing steady-state and dynamically perturbed thermal conditions. The proposed approach consistently outperforms linear regression, random forest, and LSTM baselines, achieving mean absolute errors below 0.03 and $R^2$ values exceeding 0.98 with corresponding RMSE values below 0.035 for both targets. Residual and calibration analyses confirm stable, unbiased prediction behavior across wide temperature ranges. The results highlight the strong potential of attention-based multimodal learning for future comfort-aware building energy optimization and digital twin integration. Full article