Search Results (866)

Buildings are the primary energy consumption layer of Renewable Energy Communities (RECs) and a key target for net-zero policy under the EPBD recast. This scoping review applies the PRISMA-ScR framework to map Digital Twin (DT) architectures for building-scale and community-scale energy management in REC configurations. A Scopus search yielded a final analytical corpus of 102 studies, coded through an eight-dimensional thematic matrix covering lifecycle phases, digitalization objectives, enabling technologies, DT capability dimensions, and data realism. DT is the dominant enabling technology (55.9%), followed by IoT (23.5%) and machine learning (22.5%). Research is concentrated in the Planning and Design phase (77.5%) and markedly underrepresented in Implementation and Commissioning (16.7%). Notably, only 10.8% of studies integrate real-time operational data, exposing a significant gap between simulation-based research and the deployment conditions required under current EPBD mandates. The evidence base supports building energy monitoring, demand forecasting, and flexible grid operation but remains limited for retrofit verification, standardized net-zero KPIs, and operational workflows in existing stock. Critical DT capability gaps persist in Data Services (7.8%) and User Experience (18.6%). Overall, DT architectures show genuine potential for grid-interactive, net-zero building management, yet the field presents unresolved structural challenges for large-scale real-world deployment. Full article

The Architecture, Engineering, and Construction (AEC) industry has significant potential to improve productivity, quality, and sustainability of its projects through emerging digital technologies. Advances in technology and the complexity of what new graduates need to learn have resulted in persistent training gaps and have highlighted new needs to be addressed in education. One of the new needs is the level of learners’ awareness of new technologies and their adoption practices. This research examines how current education and training practices in the selected sample of the Australian AEC sector support or hinder the development of digital capabilities. The set of technologies considered in this study focuses on Artificial Intelligence (AI), Building Information Modelling (BIM), Digital Twins (DTs), Virtual and Augmented Reality (VR/AR), and the Internet of Things (IoT). A mixed-method design integrates a structured survey of industry professionals and students, along with semi-structured interviews of industry and academic stakeholders, to evaluate exposure, self-rated capability, training participation, organisational support, and perceptions of graduate preparedness. Findings show comparatively higher maturity in BIM, but limited capability in other technologies, inconsistent formal training, and barriers linked to time, cost, organisational priorities, and rapid technological change. Qualitative findings and interpretation of preparedness-related survey responses indicate that stakeholders place greater value on transferable, interdisciplinary digital competencies than on narrow tool-specific proficiency. The research delivers statistically robust findings and actionable recommendations that address the identified barriers and promote the development of a skilled workforce in the AEC industry. Full article

Cognitive Digital Twins (CDTs) are regarded as an evolved version of existing Digital Twin (DT) systems and are capable of certain cognitive abilities. However, the various introduced definitions and characteristics of CDTs, and different understandings of “cognition”, create conceptual ambiguity around CDTs. This paper critically reviews key definitions, application domains, capabilities, and proposed architectures of CDTs. Following PRISMA 2020 guidelines, a systematic review methodology is conducted across Scopus and Web of Science to map existing definitions, cognitive capabilities, and application domains of CDTs. Studies that explicitly implement or conceptualise a DT and explicitly mention cognitive, intelligent, autonomous, or AI-driven properties are included. Conversely, conference papers, book chapters, editorial pieces, review articles, and non-English publications are excluded from this review. The results of 59 reviewed studies present bibliometric metadata and a thematic analysis of early and recent definitions and applications of CDTs across various domains, such as manufacturing, which is the most studied discipline in terms of CDT implementation. Findings show that the understanding of cognitive enhancement has shifted toward the semantic enrichment of DT systems, with a significant emphasis on knowledge-driven approaches. The discussion focuses on identifying key differences between DTs and CDTs and synthesising existing definitions. The key contribution of this study is a unified definition of CDT, a mapping of cognitive capabilities and application domains, and a future research agenda. The review is not registered. The review is limited to journal articles, and the enabling CDT technologies, along with their implementations, are not addressed within this paper. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

Bridge maintenance research has increasingly expanded toward Digital Twin (DT), Structural Health Monitoring (SHM), Artificial Intelligence (AI), sensing technologies, and object-based information management. As maintenance paradigms shift from reactive to preventive and prescriptive approaches, digital twins have gained attention as a means of integrating fragmented technological components. However, the growing emphasis on AI- and DT-based analytics raises questions about how object-based information structures, sensing systems, SHM, AI-based analytics, and interoperability mechanisms are thematically connected and structurally associated. This study conducted a scientometric analysis of publications retrieved from the Web of Science (WoS) database without year restrictions. To avoid predetermining the importance of any single information-modeling technology, the main search query excluded BIM-related terms and combined the bridge domain, DT-related technology layer, and maintenance domain. After applying document type, language, and research-area filters, 406 records were screened by title and abstract. Six records that were not directly related to bridge DT maintenance research were excluded, resulting in a final analytical corpus of 400 records. Among these, 77 records were identified as the BIM-related subset for sensitivity analysis. Using VOSviewer-based bibliographic coupling as the core method, supported by keyword co-occurrence, density and overlay visualization, and CiteSpace analysis, this study examined contemporary research structures and historical intellectual bases. The results show that bridge DT development is not detached from existing technological foundations but reflects the cumulative convergence of object-based information modeling, sensing, SHM, AI-based analytics, and interoperability mechanisms within integrated DT architectures. Full article

Currently, solar photovoltaic (PV) systems are a priority for end-use decarbonization, aimed at reducing reliance on fossil fuels. However, PV systems are typically exposed to outdoor conditions, making them more susceptible to aging and damage. In this paper, a predictive maintenance approach that integrates digital twin technology with the copula-based model is proposed. This integration enables accurate simulation of the PV system’s condition and precise representation of the correlation between the power output of the digital twin and that of the actual system. Given the power output of the digital twin, predictive maintenance is performed based on the conditional cumulative distribution function (CDF) of the actual power output, which is derived from the copula model. A comprehensive case study was conducted to evaluate the performance of the proposed approach named OCAD (Optimal Copula-based Anomaly Detector), which achieved an accuracy of 92.51% and an F1-score of 92.13%. This significantly outperforms conventional models, including SVM, KNN, and ANN, demonstrating the effectiveness of the proposed predictive maintenance strategy. Full article

The growing demand for sustainable and energy-efficient materials has positioned aerogels as promising candidates for advanced insulation applications. Among them, polyurethane (PU) aerogels are attracting increasing interest due to their thermal insulation properties and mechanical versatility. However, their development commonly relies on trial-and-error experimentation, which is time-consuming and resource-intensive. This study presents a Digital Twin (DT) framework to support PU aerogel design and reduce the experimental workload. A pilot-scale DT was developed using data from 21 synthesis experiments, including process configuration, parameter mapping, model development, and process analysis. Two predictive models were evaluated, with the Support Vector Regression (SVR) model showing good agreement with the experimental data (R² = 0.964) and being selected to estimate aerogel density within the parameter range studied. The DT framework enabled the identification of synthesis conditions associated with lower density, which may contribute to improved thermal insulation performance. These results illustrate the potential of DT-assisted modelling to support material development, improve process understanding, and guide more efficient experimentation in PU aerogel synthesis. Overall, this work highlights a data-driven approach for advancing sustainable and scalable aerogel manufacturing. Full article

With the continuous advancement of industrial intelligence, the application of hydraulic robots is becoming increasingly widespread, and the demand for their health diagnosis and maintenance is becoming more urgent. By integrating digital twin (DT) and deep learning technologies, this paper presents an intelligent fault diagnosis method for hydraulic robots based on multi-source information fusion. Firstly, a fault diagnosis architecture and solution for hydraulic robots based on DT technology are proposed. Secondly, a DT model of the hydraulic robot, which incorporates a 3D model and an attribute model with virtual–physical synchronization capabilities, is established, and a calibration method for the twin model is explored. Next, for four typical faults—leakage in the hydraulic system, valve sticking, damping hole blockage, and filter blockage—fault mechanism analysis and evolution process simulation are conducted on the established DT model. A multi-source high-quality dataset, covering normal operating conditions and multiple fault scenarios, is constructed to drive the data twin model. Finally, a feature extraction method combining Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanisms is proposed. This is followed by using a Random Forest (RF) classifier to achieve accurate fault diagnosis for various hydraulic system failures. The experimental results validate the effectiveness and practicality of this method. Full article

This paper proposes a standards-aligned hybrid artificial intelligence–digital twin (DT) framework for predictive maintenance (PdM) in the maritime domain under conditions of data scarcity and heterogeneous sensor environments. The proposed framework adopts a DT-ready reference architecture centered on an ISO 19848-aligned data contract enabling consistent signal naming across vessels and equipment. On this foundation, the prognostics module is designed as a Domain-Knowledge Enhanced LSTM (DK-LSTM), a constraint-regularized sequence model in which three domain-informed constraints—(i) RUL non-negativity, (ii) monotonic degradation, and (iii) operating-range upper bounds—are formulated within the learning objective. Constraints (i) and (iii) are active throughout, while constraint (ii) is reserved for future work due to the structural limitation of batch-sort approximation in single-output architectures. An asymmetric safety penalty further suppresses hazardous over-predictions. Scenario-based virtual experiments are conducted using the NASA C-MAPSS turbofan degradation benchmark, evaluated under (1) sensor missingness via masking indicators and (2) structural domain shift comprising operational-condition shift (E3a: FD001 → FD002) and fault-mode shift (E3b: FD001 → FD003). Through systematic ablation of loss weights and stabilization techniques across multi-seed verification (seeds 0, 42, 123), the final stabilized configuration (DK-LSTM-v4) demonstrates robust safety-critical prediction in zero-shot domain-shift scenarios: 43.7% NASA Score improvement over the strongest baseline (GRU) under E3a and 20.8% improvement under E3b. The model trades modest in-domain performance for substantial cross-domain robustness, aligning with the core requirement of safety-critical maritime and defense applications where target-domain training data is unavailable. Full article

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

Digital Twin (DT) technologies are increasingly adopted in the automotive domain to support real-time monitoring, predictive analytics, and connected decision-making across vehicles, drivers, and road infrastructure. However, research on Vehicle, Driver, and Road Digital Twins (VDTs, DrDTs, and RDTs) remains fragmented, with heterogeneous definitions, architectural assumptions, and integration strategies. This paper presents a critical review of seventy-six studies published between 2008 and 2025, examining how these three DT domains are modeled, evaluated, and connected within intelligent mobility scenarios. The review synthesizes recurring architectural patterns, communication and computing choices, and the role of interoperability and standardization in multi-twin systems. It also highlights open challenges involving distributed coordination, semantic alignment, real-time operation, and driver-aware adaptation. Based on this analysis, the paper presents a unified conceptual framework for connected automotive digital twins and discusses key directions for building scalable and safety-aware mobility services. Full article

Large-span bridges are critical components of transportation networks. Environmental variability, material degradation, and cumulative fatigue continuously affect their long-term performance. Digital Twin (DT) technology has emerged as a promising paradigm for integrating sensing, modeling, and data analytics. Most existing DT implementations in civil infrastructure rely on dense sensor networks, assume near-complete observability, and primarily serve as passive visualization or diagnostic tools, limiting their scalability and practical applicability. This paper proposes a DT framework specifically designed for the monitoring and management of existing large-span bridges under sparse sensing conditions. The framework adopts an information-centric perspective in which limited physical measurements are complemented by full-field state reconstruction through the integration of physics-based modeling, data-driven learning, and uncertainty-aware inference. A synchronized reference configuration, termed State 0, is introduced as the initial basis for tracking structural changes over time, while allowing conditional re-baselining through a Dynamic State 0 (DS0) when verified reassessment justifies it. On this basis, the proposed DT is formulated as an adaptive and decision-oriented cyber–physical system that supports optimization-based recommendations for sensing, inspection, and maintenance planning. Full article

The transformative role of Digital Twins (DTs) in the Architecture, Engineering, and Construction (AEC) sector lies in their capacity to generate dynamic, data-driven representations of physical assets that support design, construction, and lifecycle management. To achieve their full potential, DTs must integrate accurate geometric models with continuously updated information reflecting real-world conditions. This information is inherently multidisciplinary and heterogeneous, encompassing structural, environmental, operational, and monitoring data characterized by different spatial and temporal scales. Integrating these diverse datasets into a unified DT environment presents significant challenges related to data heterogeneity, interoperability, varying resolutions, data quality, and uncertainty. This paper presents a PRISMA-based systematic literature review of data fusion techniques applied to DTs within the AEC sector, with particular emphasis on geotechnical and underground infrastructure. A Scopus search conducted on 31 March 2026 retrieved 10,124 records. After sequential screening, 1916 geotechnical-related records were retained for quantitative characterization, 719 records were assessed for eligibility, 454 reports were retained for manual assessment, and 82 studies were finally included in the detailed qualitative review. Existing approaches are classified according to their integration paradigms, methodological foundations, and application domains. Particular attention is given to applications in Geotechnical Engineering, where DTs must integrate sparse, indirect, and highly uncertain subsurface data. Geological conditions are characterized by strong spatial variability, limited observability, material heterogeneity, and epistemic uncertainty, which introduce additional complexities for data fusion compared to surface infrastructure systems. By synthesizing current developments and identifying methodological trends and research gaps, this review provides a structured framework to support the selection and adaptation of data fusion strategies for geotechnical DTs and other complex AEC applications operating under high uncertainty. Full article

The application of digital twin (DT) technology to intelligent machining shows promise, but its effectiveness in predicting vibration and assessing surface quality has not been thoroughly validated for widespread industrial use. This study presents a physics-informed predictive digital twin framework operating in an offline or near-real-time predictive configuration for vibration prediction and surface roughness modeling in milling processes. Impact hammer testing was conducted to extract the dominant modal properties of the spindle–tool assembly, which were embedded into a Simulink-based dynamic framework to predict tool vibration under varying cutting conditions. Full-immersion slot milling experiments on AL6061 were performed for validation. Within all datasets, including training phase and validation phase, the predicted vibration amplitudes exhibit a coefficient of determination $R^2=0.94$ with measured values. The overall MAPE and RMSE are about 10.39% and 0.234, respectively. Power-law regression-based surface roughness prediction models were subsequently established using cutting parameters and both measured and DT-predicted vibration features through logarithmic transformation and least-squares fitting. The results show that the roughness prediction model using vibration features predicted by the digital twin model achieved a correlation coefficient of approximately $R^2=0.84$, with MAPE = 9.57% and RMSE = 0.16 μm, which is comparable to the predictive model based on experimentally measured vibration. These results indicate that, within the investigated machining conditions, the digital twin can provide vibration features suitable for surface roughness prediction, demonstrating its potential as a virtual sensing approach. This work advances digital twin applications from process monitoring toward predictive, quality-oriented machining systems and provides a foundation for adaptive parameter updating in intelligent manufacturing environments. Full article