Harnessing hybrid digital twinning for decision-support in smart infrastructures

POSITION PAPER Harnessing hybrid digital twinning for decision-support in smart infrastructures Huangbin Liang 1 , Beatriz Moya 2,3 , Eugene Seah 4 , Ashley Ng Kwok Weng 5 , Dominique Baillargeat 2 , Jonas Joerin 1 , Xiaozheng Zhang 6 , Francisco Chinesta 2,3 and Eleni Chatzi 1,7 1 Singapore-ETH Centre, Singapore, Singapore 2 CNRS@CREATE, Singapore, Singapore 3 PIMM Laboratory, ENSAM Institute of Technology, Paris, France 4 Meinhardt Group, Singapore, Singapore 5 CETIM-Matcor, Singapore, Singapore 6 TÜV SÜD, Singapore, Singapore 7 Department of Civil Environmental and Geomatic Engineering, ETH Zurich, Zurich, Switzerland Corresponding author: Beatriz Moya; Email: [email protected] Received: 05 August 2024; Revised: 01 April 2025; Accepted: 01 May 2025 Keywords: decision-making; hybrid digital twin; resilience support; smart infrastructures; infrastructural management Abstract Digital Twinning (DT) has become a main instrument for Industry 4.0 and the digital transformation of manufacturing and industrial processes. In this statement paper, we elaborate on the potential of DTas a valuable tool in support of the management of intelligent infrastructures throughout all stages of their life cycle. We highlight the associated needs, opportunities, and challenges and discuss the needs from both the research and applied perspectives. We elucidate the transformative impact of digital twin applications for strategic decision-making, discussing its potential for situation awareness, as well as enhancement of system resilience, with a particular focus on applications that necessitate efficient, and often real-time, or near real-time, diagnostic and prognostic processes. In doing so, we elaborate on the separate classes of DT, ranging from simple images of a system, all the way to interactive replicas that are continually updated to reflect a monitored system at hand. We root our approach in the adoption of hybrid modeling as a seminal tool for facilitating twinning applications. Hybrid modeling refers to the synergistic use of data with models that carry engineering or empirical intuition on the system behavior. We postulate that modern infrastructures can be viewed as cyber-physical systems comprising, on the one hand, an array of heterogeneous data of diversified granularity and, on the other, a model (analytical, numerical, or other) that carries information on the system behavior. We therefore propose hybrid digital twins (HDT) as the main enabler of smart and resilient infrastructures. Impact Statement We advocate for the adoption of Hybrid Digital Twinning (HDT) as a main enabler for transforming strategic decision-making and enhancing system resilience within the domain of infrastructure. In clarifying the modus operandi of DT technologies, this paper highlights the strengths and potential of digital twin technologies and aspires to lay the foundations for the development of next-generation digital twins for smart infrastructures. This study summarizes the insights gained from a round-table discussion on Decision Support for Infrastructural Asset © The Author(s), 2025. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. H.L. and B.M. contributed equally to this work. Data-Centric Engineering (2025), 6: e43 doi:10.1017/dce.2025.10015 Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. Management, which was held as a joint initiative of the Future Resilient Systems (FRS) program at the Singapore-ETH Centre and the DESCARTES interdisciplinary excellence program at CNRS@CREATE. 1. Introduction Engineering infrastructures form the backbones of our society. Under the mandate of Industry 4.0, the digital revolution has brought about a paradigm shift in how we design, produce, and interact with physical assets (Oztemel and Gursev, 2020). While digitization has been broadly adopted in the context of manufacturing and production technologies and the handling of industrial assets, it remains underutilized in large-scale built environments, such as infrastructures. Building Information Modelings (BIMs) dominate the field, primarily serving as static images for the design and construction phases (Sacks et al., 2020). However, also on this scale, the concept of Digital Twinning (DT) has the potential not only to deliver information on the state of the system “as is,”but also to inform decision support frameworks further. These frameworks operate throughout the structural life cycle, namely from the stage of manufacturing/construction, to the stage of operation under standard as well as extreme loads and hazards, and finally to the decommissioning phase. To add value, DT representations should enable a closed-loop exchange between digital and physical assets. This involves extracting information garnered from operating physical systems (e.g., by means of monitoring) and distilling this information via the use of digital representation. Finally, this analysis would be exploited to act on the physical asset to protect critical infrastructure and guarantee its resilience (Argyroudis et al., 2022). Infrastructure resilience is used here as the main criterion based on which strategic decision-making can be made. It can be defined as the ability to anticipate, prepare for, and adapt to environmental changes, as well as cope with, respond to, and recover rapidly from extreme disruptions (Cimellaro et al., 2016). Numerous studies in recent years have focused on infrastructure resilience under adverse environmental impacts and exposure to extreme events. These studies put forth frameworks for quantifying and enhancing resilience across scales, from components and individual assets, to interconnected networks (Ouyang et al., 2012; Cimellaro et al., 2016; Dhar and Khirfan, 2017; Koliou et al., 2020; Blagojevićet al., 2023; Liang et al., 2023). This analysis is typically conducted in the pre-incident phase using simulated scenarios with stochastic deterioration/fragility and restoration models, without accounting for information that is gathered from the actual system over time. Our primary focus lies on decision-making in the context of during-incident and post-incident phases, which usually require fast (sometimes even realtime) decision-making. The premise for such an investigation assumes the availability of data from infrastructural assets and systems. This is nowadays justified by the growing availability of information, which includes not just digitized logs with inspection information on structural systems, but also the increasing use of sensing technologies to monitor these systems, on both a periodic (e.g., Non Destructive Evaluation) and continuous (e.g., Structural Health Monitoring) evaluation (Kamariotis et al., 2024). Currently, there is no integrated framework for quantifying and enhancing infrastructure resilience based on the fusion of such data within DT techniques. Hence, the objective of this paper is to •clarify the current landscape in terms of available DT representations, •define Hybrid Digital Twins (HDTs) as a class of DTs that is particularly suited for infrastructural assets, when viewed under the prism of cyber-physical systems, •illustrate the potential application of HDTs in support of decision-making for performant and resilient infrastructures, •and finally, highlight the associated challenges and opportunities in this respect. 2. Motivation for integrating HDTs in infrastructural management DT refers to the development the creation of virtual representations of physical assets that integrate sensor data, system simulations, and analytics. This integration provides decision-makers with e43-2 Huangbin Liang et al. Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. unprecedented, often real-time, insights into the condition and behavior of physical assets, which refer to any physical object, system, or infrastructure holding economic value to an organization(e.g., building, bridge, wind energy structures). Unlike traditional periodical and reactive decision-making methods, the integration of DT introduces predictive analytics, which are informed based on real-time and historical data collected from the physical asset in operation. This forecasting potential supports proactive management of operations, maintenance, and resilience against risks and hazards. A significant limitation of purely data-driven Digital Twin (DT) models is their lack of generalizability and interpretability. This issue often arises from the insufficiency of representative data, which can lead to overfitting and poor performance in unseen scenarios. Additionally, the absence of physical knowledge integration in these models can hinder the ability to interpret model predictions and accurately capture intricate dynamics needed for accurate predictions, limiting their effectiveness in decision support for infrastructure management. Aiming for greater accuracy and effective decision-making, we use the term hybrid digital twinning (HDT) to refer to an advanced form of digital twinning that explicitly incorporates a physics-based model of the system (which can be numerical, analytical, or empirical) within a process that further feeds from data. The integration of physics-based models fundamentally distinguishes HDTs from purely data-driven DTs by enhancing predictive capabilities and ensuring physically grounded predictions. HDTs uniquely enable the generalization of predictions beyond the positions of sensor observations, facilitating virtual sensing of system responses in critical, unmonitored locations (Papatheou et al., 2023; Vettori et al., 2023). This generalization capability is essential for managing assets under extreme and changing conditions, where reliance solely on available sensor data would be insufficient. By grounding predictions in physical principles, HDTs enhance interpretability, ensuring that predicted outcomes can be validated and trusted, which is particularly crucial for critical decisionmaking processes. Consequently, HDTs empower decision-makers to respond swiftly to disruptions and adapt to dynamic conditions by providing insights into both monitored and unmonitored parts of the system, supporting a proactive and transparent decision-making process. This transparency, essential for accountability and trust, is vital in sectors where decisions have significant economic and safety impacts. This integrated approach allows decision-makers to develop a scalable framework, which is adaptable across dimensions such as asset size and degrees of freedom, interdependency, and throughout the life cycle of assets. Adaptability refers to the capacity of the framework to remain effective whether applied to a single asset or when scaled up to encompass an entire network of assets, and its potential to be consistently implemented throughout all phases, from design and construction to operation and end-oflife management. During the manufacturing and construction phase, HDT technology facilitates the integration of real-time data and model-based predictive analytics, allowing for the optimization of design processes and the embedding of resilience measures tailored to anticipated operational challenges. As assets transition into the operational phase, HDT technology continuously learns operational strategies in real time to effectively manage emerging risks and minimize downtime, especially under extreme conditions. Finally, in the decommissioning phase, HDT technology provides a data-rich basis for executing cost-effective strategies by leveraging the comprehensive historical data accumulated over the operational lifetime of the asset. An HDT embodies a closed-loop, dynamic, possibly real-time, datadriven approach to asset management that not only accounts for complex interdependencies and curbs assessment uncertainty but also operates based on the current state of the system rather than its initial deployment conditions. Despite the clear advantages of the use of DTs and, in particular, HDTs in the context of infrastructure management and resilience, their adoption has been slow in practice. This reluctance often stems from the diverse interpretations and lack of clarity surrounding the definition and applicability of a DT/HDT, as well as the relative lack of standards and protocols for formally framing the use of such tools. This statement paper aims to clarify the definition and potential use of HDTs within the domain of smart infrastructures, exploring the need to enhance their utility and maximize their uptake. Data-Centric Engineering e43-3 Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. 3. Hybrid digital twins—HDTs The implementation of digital twins presents its own set of challenges. Data integration, modeling complexity, transparency, communication among agents, and ethical concerns relating to automated decision-making are significant challenges that must be addressed to ensure an actionable application. However, the first step is to propose a framework for cross-disciplinary understanding that sets the foundation for any future development. 3.1. Definition and interpretation of digital twins The concept of digital twin (DT) finds its roots in NASA’s Apollo XIII project, where digital simulators and a physical replica were connected to the real spaceship to receive information from it to update its operating condition and propose mission rules based on its state, especially in critical conditions (Shafto et al., 2010). As reported in this document, this was the case with the explosion of the oxygen tanks that damaged the engine during the mission, a situation in which the simulators helped to evaluate damage and solutions to perform informed crisis management. With the surge of Industry 4.o, DTs became a go-to term in several fields; however, the definition of the term may still appear blurred and unclear (Wright and Davidson, 2020). Certain sources ((Alam and Saddik, 2017; Hughes, 2018; Platenius-Mohr et al., 2020) to name a few) define DTs as models, simulators, replicas of existing phenomena, i.e., digital replicas of real assets. Although partially correct, this definition lacks an essential element, namely the interaction with the physical asset. More recent frameworks within the engineering context describe a DTas a process that defines a closed loop between the physical entity and the digital replica (AIAA, 2020; McClellan et al., 2022). This requires a digital workflow of information, parametrized models, diagnostic and prognostic algorithms, and control tools, often aggregated in a visualization layer, which generates value for the user and facilitates decisions. The origin of the DTconcept may be traced back to a presentation by Michael Grieves at the University of Michigan in 2002, which aimed to establish the so-called Product Lifecycle Management (PLM) framework (Grieves, 2002). However, the first known definition for the DT is considered to be the one published by NASA in (Shafto et al., 2010). In this definition, a DT is claimed to be an integrated multiphysics, multiscale, probabilistic simulation that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin for recommending changes in mission profile to increase both the life span and the probability of mission success, already signifying the key aspect of two-way interaction between the physical and digital counterpart. Following this spirit, similar descriptions have been assigned to DTs (Glaessgen and Stargel, 2012; Saddik, 2018; Xu et al., 2019; Liu et al., 2021; Kenett and Bortman, 2022). The recent AIAA position paper (AIAA, 2020) defines a digital twin as: A set of virtual information constructs that mimics the structure, context and behaviour of an individual/unique physical asset, or a group of physical assets, is dynamically updated with data from its physical twin throughout its life cycle and informs decisions that realise value. We discern three main characteristics of a DT in the various definitions offered: •A physical asset from which information is extracted, implying the presence of a monitoring system. •A digital (virtual) representation of the physical element, represented by a model that captures the behavior of the physical counterpart. Here we distinguish four levels of description: component, asset, system, and process. •A oneor two-way information flow process, depending on the application, that links the digital and physical counterparts to ensure continuous tracking of the behavior of the physical asset. This is used to update the status of the digital replica, offering valuable augmented information on the state of the system, and allows for acting on it with improved confidence margins. A one-way process is also called a “digital shadow”(Bergs et al., 2021). e43-4 Huangbin Liang et al. Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. Dynamic Data-Driven Application Systems (DDDAS) (Blasch et al., 2013), is proposed as a framework for the dynamic update of simulators (models) with data obtained from sensor networks and monitoring devices. Although this framework focuses on the aspect of updating a digital mirror (essentially) of the operating physical system, the purpose of DTs extends beyond computational modeling and updating to include performance and condition assessment, analysis, and optimization of physical assets throughout their life cycle. In the life cycle of infrastructure systems, we can distinguish five main phases: design, construction, operation, maintenance, and decommissioning. Each of these phases can be coupled with digital twins, accompanying the evolution of the system and enhancing its management and optimization throughout its life cycle. Following (Grieves and Vickers, 2017), in this work, we define DTs using a classification in three essential categories (classes), according to the purpose served by the twin throughout the life cycle: •the Digital Twin Prototype (DTP) •the Digital Twin Instance (DTI) •the Digital Twin Aggregate (DTA) The first DT class we refer to here is the Digital Twin Prototype (DTP), which reflects a virtual representation of a physical object, encompassing the essential information sets needed to characterize and fabricate a physical counterpart (for instance, requirements, 3D models, lists of materials, processes, services, and disposal procedures). This class is typically used during the design phase and is closely associated with the features and goals of Building Information Modeling (BIM) (Definition, 2014). In the work of Grieves and Vickers (2017), a Digital Twin Instance (DTI) is described as a specific physical asset to which a digital counterpart remains linked throughout the life of that physical product. Here, we adopt the interpretation of McClellan et al. (2022) in relation to the notion of an instance and define a DTI as the DTof an individual instance of the product, once it is manufactured and equipped with sensors that generate data. This implies that the DTI embodies the notion of information flow between the physical and digital counterparts. The Digital Twin Aggregate (DTA) (Grieves and Vickers, 2017; McClellan et al., 2022) is described as the aggregation and analysis of data from numerous DTIs, allowing for review and possible intervention regarding a set of assets. Essentially, it describes a computing construct that allows to gather and analyze data from various DTIs to gain insights with respect to a broader range of physical products or processes. A DTA can aggregate instances ranging from different DTIs of components comprising an assembly, to multiple instances from similar systems that have aggregated a collected behavior. In the latter, DTA relates to the concept of learning from fleets or populations (Worden et al., 2020), reflecting a more massive collection of data, which can enhance predictive and prognostic capabilities at the system level. Each class of DTs will require different levels of depth, abstraction, and enrichment to properly accompany the original twin throughout various phases of the asset’s life cycle. Figure 1 has now been revised to illustrate these DT classes, delineating the systematic application of DTPs, DTIs, and DTAs across various stages of physical assets. The figure employs the example use case of wind turbine operations: DTPs aid in the design phase by simulating and refining turbine structures. Multiple DTIs represent real-time operational units equipped with sensors, facilitating ongoing monitoring and immediate adjustments. The DTA synthesizes insights from individual DTIs to guide system-wide performance assessments and predictive maintenance strategies, enhancing overall operational efficiency and the longevity of the assets. Based on the description for each DT and the needs specific to each phase of the life cycle, varying levels of detail, abstraction, and enhancement will be necessary to effectively accompany the original twin. This evolutionary spirit of DTs is reflected in Figure 2. In these definitions, information flow is assumed to be available throughout the asset’s life. Models that do not continuously follow a physical asset are merely snapshots, not true DTs. In engineering, Real-Time Digital Twins (RTDTs) are digital representations updated online, in real or near real-time, as data become available. Data-Centric Engineering e43-5 Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. DTs are powered by the use of simulators/models that provide representations of complex systems, processes, or phenomena of interest. Currently, BIM (Building Information Modeling) representations seem to prevail in terms of adoption in practice, despite them largely comprising geometric representations and metadata repositories of built objects. This observation is primarily evidenced by insights gathered from industry roundtables, where experienced practitioners emphasized the robustness and integration capabilities of BIM in the construction and engineering sectors. Whereas BIMs, as mainly adopted today, are closer to what one would define as “as-designed geometric models,”effective DTs require more computational capabilities. Such more efficient models can be obtained via the use of structural (finite element) models and well-established formulations such as fluid mechanics, transient dynamics, and degradation models. To make such models actionable within a twinning framework, it is necessary to deliver reliable, yet reduced-order representations that can incorporate physics in a way that is manageable for the process at hand. Reduced Order Models (ROMs) significantly contribute by offering swift emulations of a monitored system with manageable computational expenses (Frangos et al., 2010; Chinesta et al., 2011; Amsallem et al., 2012; Farhat et al., 2018; Kapteyn et al., 2020; Vlachas et al., 2021; Agathos et al., 2022,2024; Idrissi et al., 2022). ROMs are mathematical representations of complex systems that aim to provide simplified but accurate predictions of system behavior. When incorporating physics principles, such ROMs are often referred to as intrusive (Chinesta and Cueto, 2014). Although there are nonintrusive, that is, purely data-driven techniques that employ data from simulations or experiments to bypass physics (Ibáñez et al., 2018; Hernandez et al., 2021), the imposition of physics biases is often desirable to ensure interpretability (Vlachas et al., 2012; Bacsa et al., 2023; Liu et al., 2025). Figure 1. Life cycle integration of digital twin technologies for physical assets, using the example of wind farm management. DTPs aid in the design and decommissioning phases by simulating and optimizing turbine structures and the decommissioning process, while multiple DTIs represent real-time operational units equipped with sensors, facilitating ongoing monitoring and immediate adjustments. The DTA synthesizes insights from individual DTIs to guide system-wide performance assessments and predictive maintenance strategies, enhancing overall operational efficiency and longevity of the assets. DTI and DTA can evolve on a temporal scale depending on the frequency of the collecting data, where Real-Time Digital Twins (RTDTs) are specific DTs that are updated in a more frequent, real-time manner. e43-6 Huangbin Liang et al. Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. Accompanying the real asset along its useful life requires the capacity of adaptation and re-engineering along its different phases, with flexible configurations that may have to respond to previously unseen conditions. In this regard, McClellan et al. (McClellan et al., 2022) also highlight the role of current developments such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and data analytics to correctly fill the gap between the simulation model, usually defined by known physics, and the real behavior perceived as a manner to extend the capabilities of the original ROMs that reproduce the physics of the real asset. AI-informed ROMs strongly depend on data quality and availability. To overcome this limitation, new techniques driven by physical knowledge may find patterns and reconstruct missing information. This involves embracing the smart data regime, which involves the right information, at the right moment, and right place. ML and DL can synergistically be combined with hybrid models, enhancing their explainability and predictive potential (Montáns et al., 2019; Champaney et al., 2022; Kenett, 2024). Such an instance has emerged in physics-enhanced or physics-informed modeling, which capitalizes on the fusion of physics principles, data, and ML, with this mixing assigning different weights to the mixed components, as explained in (Haywood-Alexander et al., 2023). Physics-informed digital twins (PIDT) are those digital twin representations that incorporate domain-specific knowledge of physics principles and laws, offering interpretable models that effectively capture the system’s inherent dynamics (Kapteyn and Figure 2. Landscape of the DT paradigm. The HDT includes hybrid modeling to enrich simulations with aspects of physics and machine learning (ML) to accurately mimic the behavior of real systems. Such a construct offers higher interpretability. Finally, cognitive digital twin (CDT) would combine previous technologies with scene understanding and autonomous decision-making. As a result, the DT progressively increases in complexity and opportunities. Data-Centric Engineering e43-7 Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. Willcox, 2020; Liu et al., 2025). While PIDTs require more development effort, they provide transparency and fidelity, making them well-suited for applications where understanding and certifiability are essential. The choice between these approaches depends on the specific requirements of the problem at hand, which balance predictive power with interpretability and reliability. Some versatile examples are those that employ known descriptions of the system, such as partial differential equations, or algorithms founded in known physical laws (Tatsis et al., 2022; Vlachas et al., 2022; Haywood-Alexander and Chatzi, 2023; Zhang and Zhao, 2023; Yang et al., 2024), such as those of thermodynamics (Hernandez et al., 2022; Cueto and Chinesta, 2023), and preservation of physical quantities (Kirchdoerfer and Ortiz, 2016; Bacsa et al., 2023). Under this premise, we refer to hybrid digital twins (HDTs) as twin constructs that create a more comprehensive and accurate representation of a system or process. Here, accuracy reflects the ability of the digital twin to remain aligned with real-world behavior, including in previously unseen contexts or response to evolving loads and environments. As provided in Figure 2, HDTs integrate multiple modeling paradigms—combining physics-based (white-box) models that offer transparent insights into underlying physical mechanisms with data-driven ML (black-box) approaches that enhance predictive accuracy. The resulting grey-box models fuse interpretability with adaptability, enabling a richer and more robust digital representation of physical assets or systems. Specifically, HDTs may incorporate physics knowledge as a hard constraint(physics-guided or physics-encoded) by directly embedding differential equations within the neural network architecture, ensuring that predictions adhere to known physical laws. Alternatively, HDTs can treat physics knowledge as a soft constraint (physics-informed) by adding the residual of physics-based models to the loss function to guide the learning process or to refine the outputs of ML algorithms (Chinesta et al., 2020; Haywood-Alexander et al., 2023). This integration enhances both the explainability and transparency of the twins’outputs, while improving their capacity to adapt to varying loads and environments (Wagg et al., 2025). Furthermore, hybrid modeling allows interpretable diagnostics and generalization of their predictive ability of the twin, while maintaining computational efficiency. Purely physics-based models, while strong in interpretability, typically lack practical efficiency due to slower computational speeds required for precise simulations. HDTs thus present a compelling advantage by combining the strengths of both physics-based models and data-driven approaches to deliver more reliable predictions and enable real-time monitoring and decision support across a wide range of applications (Wagg et al., 2020). In this paradigm, there is an incipient subclass of DTs that is expected to lead the next developments in the domain: the cognitive digital twin (CDT) (Abburu et al., 2020; Unal et al., 2022). Cognition refers to the set of abilities that encompass sensing, thinking, and reasoning (Bundy et al., 2023). Although research applications that mimic cognition are still limited (the most common use case being large language models), the appropriate design of algorithms can lead to the integration of some of these abilities. The emerging concept of cognitive, or smart, digital twins (CDT) refers to systems that can interact with both physical and virtual environments to autonomously make smarter decisions based on context (Abburu et al., 2020; Zheng et al., 2022). Although both HDTs and CDTs use ML to enrich themselves, HDTs tend to use data and ML to fill in gaps in the knowledge of the system. In contrast, CDTs use data for complex interpretation—also called perception (Moya et al., 2023)— reasoning (autonomously making decisions about their performance), automatic calibration for improved decision-making (Arcieri et al., 2021), and interaction with the user. Although one of the outcomes can be the enrichment of HDTs, we expect CDTs to more comprehensively capture the relationship between data and physics models. The expert in the loop complements the cognitive and interoperability requirements of CDTs (Niloofar et al., 2023). The incorporation of the human cognitive dimension within the digital twin paradigm leverages the expertise and experiential knowledge, serving as a crucial facilitator in understanding the underlying rationale of decisions and their appropriateness within a specific context. Consequently, the expert-in-the-loop paradigm underscores the significance of model explainability, a salient feature during various interaction phases within a Cognitive Digital Twin (CDT). When extending prediction/estimation at the system level, DTs may require the incorporation of representations and simulations of interconnected systems or components (Heussen et al., 2011; Ouyang, e43-8 Huangbin Liang et al. Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. 2014; Schluse et al., 2018; Liang and Xie, 2021). Such representations are defined as System-Level Models. For example, energy system network models (Heussen et al., 2011; Ouyang et al., 2017) provide a detailed understanding of how energy flows through various components, helping to optimize energy consumption and identify potential inefficiencies. AR (augmented reality), VR (virtual reality), and DT technology connect the physical and digital worlds (Badías et al., 2019; Moya et al., 2022; Vettori et al., 2023), enhancing user interfaces to improve understanding, collaboration, and decision-making in various fields (Michalik et al., 2022).Specifically, AR allows users to overlay digital information onto the real world, enhancing the ability to understand complex systems and processes in situ. However, VR creates a completely immersive simulation environment that is ideal for training scenarios, safety drills, and visualization of scenarios that are either dangerous or impractical to replicate in the real world. Together, AR and VR enhance DTs by improving visualization, interaction, and simulation capabilities, allowing stakeholders to analyze potential outcomes in a controlled virtual setting, facilitating more informed decision-making. This proactive approach transforms industry practices in forecasting, troubleshooting, and optimizing operations, further establishing digital twins as essential in digital transformation. Virtual environments often use virtual sensing to simulate the behavior of sensors that exist in the real world.Although remote sensing facilitates the creation ofaccurate DTs of infrastructure systems (Dorafshan etal.,2018; Phillips and Narasimhan, 2019; Bado et al., 2022;Kaartinenetal.,2022),therearestillscenarios where it is impractical, expensive, or insufficient, such as the case of assessing the load and prediction of the performance of DTs of wind turbine blades(Vettorietal., 2022). These virtual sensors generate data within a virtual environment, which can then be used to simulate realistic scenarios, test algorithms for sensor data processing and analysis, and perform dynamic adaptation within virtual environments. 3.2. Role of Internet of Things, real-time data analytics The Internet of Things (IoT) involves sensor selection, deployment, acquisition, and connectivity. IoT represents not only the deployed sensing network, but also the purpose of connecting and transferring information. Most of the information comes in the form of time series or image-based representations, collected via appropriate compression schemes. IoTregimes often involve multiple and heterogeneous or multimodal data sources. Hence, DTs must be designed to flexibly tackle diversified types of data input, which is usually tackled via the aspect of fusion. Even though some measurements (strains, pressure, temperature) can be directly correlated to quantities of interest, this is not true for other sources, which deliver indirect information (such as vibration-based ones). Physically infused hybrid modeling is required to extract physical insights from diverse and indirect data. In this context, we revisit the previously introduced concept of RTDTs, which is based on real-time performance, reflecting a growing desire of the industry. It is important to properly define what real time implies in practice and to consider the appropriate time scale to assess the performance of the system and the required data flow rate. We define an RTDTas a digital twin that evolves synchronously to its physical counterpart, measuring and processing the changes that occur in the physical counterpart and correspondingly updating the virtual replica, and possibly implementing feedback (in the form of actions) to the physical asset, in an online fashion (Zipper and Diedrich, 2019). However, achieving perfectly synchronous, hard real-time response with minimal delays and high sampling rates can be inefficient, requiring excessive resources and infrastructure, and increasing risks of overhead and latency. Thus, “real-time” performance in a DT varies depending on its purpose, ranging from immediate to periodic updates, influenced by data collection rates and timing for related actions or decisions.” 3.3. The smart data paradigm The data collection process can pose challenges that require a comprehensive framework for intelligent data collection, processing, and use. Table 1 summarizes primary sources of data used in the construction of DTs. System loads and response data are critical because they provide real-time feedback on infrastructure performance and condition, forming the basis for operational digital twins; external Data-Centric Engineering e43-9 Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. Open data exchange. Challenges in open data exchange include ambiguous data ownership, data privacy concerns (Wang et al., 2023), data quality and consistency variability, leading to potential disputes and limiting the availability of relevant data for digital twin systems. Security and trustworthiness of algorithms/data. Data may be corrupted, tampered with, or manipulated; algorithms used in DTs may exhibit bias and may not undergo thorough validation processes; the explainability of AI models is often limited. All these can lead to inaccurate representations and flawed decision-making outcomes (Amerirad et al., 2023). Standardization and certification of DT. Current digital twin standards, including the IFC and ISO series (ISO.ISO/TR 24464-2020; ISO.ISO 23247-2021; ISO.ISO 19650-1:2018; ISO.ISO 37100-2016; ISO.ISO/IEC AWI 30173; ISO.ISO/IEC AWI 30172), IEEE series (IEEE.IEEE SA-P2806.1; IEEE.IEEE SA-P3144), IEC series (IEC.IEC 61850-2024; IEC.IEC 62832-2020) and ITU series (ITU.ITU-TY.3090; Interoperability framework of digital twin systems in smart cities and communities), encounter limitations hindering their widespread adoption and effectiveness. One notable challenge is the lack of comprehensive coverage across industries and application domains, leading to interoperability issues. Additionally, the rapid evolution of digital twin technologies outpaces standard development, resulting in outdated guidance for emerging use cases. Achieving consensus among stakeholders and allocating resources for compliance also pose significant challenges, especially for smaller organizations or those with legacy systems (Bicevskis et al., 2017; Hong and Huang, 2017; Kirchen et al., 2017; Burns et al., 2019). Dealing with false positives/ responsibility for the decision. In a legal context, the attribution of responsibility becomes a crucial aspect, as stakeholders may question accountability for any adverse effects resulting from false positives or erroneous decisions. This challenge is exacerbated by the evolving nature of digital twin technologies, making it essential to navigate legal frameworks that may not have caught up with the rapid advancements. Human element/ ethics to alleviate dangers from automation. Balancing the advantages of automation with ethical considerations, such as fairness, accountability, and transparency, is essential to prevent dangers stemming from unchecked automation, and a robust framework is needed for the integration of human expertise and ethical guidelines into automated decision-making in DTs to mitigate risks and build trust. In addition, training users to understand and work with the twin is crucial for the appropriate interpretation and use of its information. Addressing these challenges requires collaborative efforts from stakeholders across industries, involving policymakers, standards organizations, technology providers, and end-users, to develop frameworks, standards, and best practices that promote the responsible and effective use of DTs for decision-making in a rapidly evolving technological landscape. 5.3. Opportunities Recent perspective papers have highlighted the limitations of current digital twin tools in urban planning, particularly regarding their focus on short-term goals versus the long-term focus of city planning policies (Batty, 2024; Bettencourt, 2024). They note issues such as staticity, limited aggregation capacity, and a primary focus on visualization. Emphasizing the need for improvement, they advocate for modeling multilevel and multidomain as well as multi-spatiotemporal scale networks better to capture interactions and the dynamic nature of urban environments facing various stressors. Furthermore, these papers underscore the importance of robust verification, validation, and uncertainty quantification methods to enhance the reliability and accuracy of digital twin models. In addition, authors in (Mohammadi and Taylor, 2021) discuss the importance of utilizing Smart City DT for disaster decision-making in cities facing various stressors. They emphasize the integration of fast and slow modes in decision-making processes and highlight the need for capturing, predicting, and adapting to urban dynamics at varying paces to effectively manage disaster-related mortality and economic losses. The ongoing standardization of DTs presents numerous opportunities for industries and stakeholders. Standardized frameworks and protocols facilitate seamless interoperability and integration, fostering collaboration and innovation while reducing implementation costs and risks through clear guidelines and e43-16 Huangbin Liang et al. Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. best practices. In addition, standardized data formats and communication protocols enhance data quality, consistency, and security, building trust and confidence. Finally, the demand for open platforms that integrate existing technologies is growing in the fastchanging tech landscape. (Robles et al., 2023). These platforms are designed to facilitate the integration of various data sources, sensors, devices, and applications within a smart city environment. Platforms like iTwinJS (Incorporated Bentley Systems) and Opentwins (Robles et al., 2023) exemplify the pivotal role of openness in fostering collaboration, innovation, and interoperability within the digital realm. Another example is the Digital Twin Platform (DTCC Platform), developed at the Digital Twin Cities Centre, that incorporates a DTCC builder (Logg et al., 2023) (Somanath et al., 2023), model and simulation, and visualization. An example of the implementation of the project is that of the city of Gothenburg (Gonzalez-Caceres et al., 2024). The study of automation may result in the replacement of human labor in a positive sense. Although human expertise is pivotal in the digital twin cycle, the proposed new technology can intervene to automate fast decision-making in crucial scenarios and improve the efficiency, safety, and well-being of potential human users. DTs must be built to empower the human, not the machine. The exploitation of AR, VR, or virtual spaces (metaverse) as facilitators can democratize access to information and insights, enabling a broader audience, including stakeholders with varying levels of technical expertise, to interact with and understand complex systems and data. This fosters cross-functional collaboration, accelerates decision-making processes, and improves the overall effectiveness of digital twin initiatives. 6. Conclusion This statement paper aims to set the foundations for the development of next-generation DTs and their application to smart infrastructures. We have identified challenges in the data acquisition and simulation that could be addressed through the so-called smart paradigms. The smart use of data enhances data collection and processing efficiency by selecting what, when, where, and at what scale to avoid problems derived from big data. This, combined with analytics enriched with physics, improves the interpretation and quality of the results. Additionally, hybrid modeling provides an effective strategy for integrating diverse modeling methodologies, including physics-based and data-driven approaches, thereby improving the precision, adaptability, and effectiveness in simulating complex real-world systems. Our analysis highlights the need to unify languages to improve communication among platforms and stakeholders handling various types of data. Furthermore, we advocate for exploring the integration of elements and agents within the digital twin framework to fully account for operational interactions and connections at different levels. Lastly, we recommend further investigation into the development of the smart digital twin framework to facilitate automation and intelligent decision-making processes that would enhance reaction to unpredictable, and possibly crucial, new scenarios. We advocate for a paradigm shift from traditional decision-making practices in infrastructure management towards more proactive, data-driven approaches. We propose developing digital twin–enabled decision-making frameworks throughout the project’s life cycle and discuss advanced applications including autonomous management, predictive maintenance, adaptive behavior, and resilience enhancement. Furthermore, we outline the future outlook for augmenting such digital twin–enabled decisionmaking frameworks by applying expert-guided paradigms, forming system-level perspectives, and considering unexpected extreme events, to make more informed and comprehensive decisions in support of infrastructure resilience. Acknowledgments. This position paper has been developed as part of a roundtable session on the theme of Digital Twinning and Decision Support for Asset Management. The roundtable was held in the context of joint collaboration between the Future Resilient Systems (FRS) of the Singapore-ETH Centre and the DESCARTES interdisciplinary program of excellence by CNRS@CREATE. All involved sector stakeholders, including TÜV SÜD, ARUP, MEINHARDT, CETIM-Matcor, NAVAL Group, Ministry of National Development (MND), Land Transport Authority (LTA), and GOVTECH, are acknowledged for their participation and active feedback. Data-Centric Engineering e43-17 Downloaded from https://www.cambridge.org/core. 09 Sep 2025 at 08:21:10, subject to the Cambridge Core terms of use. Author contribution. Conceptualization: H.L; B.M; F.C; E.C. Methodology: H.L; B.M; F.C; E.C. Project administration: F.C; E. C; D.B; J.J. Data curation: H.L; B.M; F.C; E.C. Resources: E.S; A.W; X.Z; F.C; E.C. Data visualization: H.L; B.M; E.C. Writing original draft: H.L; B.M. Supervision: F.C; E.C. Writing –review/editing: H.L; B.M; E.S; A.W; D.B; J.J; X.Z; F.C; E.C. All authors approved the final submitted draft. Competing interests. None. Data availability statement. In this manuscript, no data were produced or used to pursue the research stated. Funding statement. The research was conducted at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and the National Research Foundation Singapore, and CNRS@CREATE through the DESCARTES program; both research programs supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. E. Chatzi would also like to acknowledge the support of the InBlanc project, titled “INdustrialisation of Building Lifecycle data Accumulation, Numeracy and Capitalisation,”funded under the Horizon Europe programme with the Grant Agreement ID 101147225. B. Moya acknowledges support from the French government, managed by the National Research Agency (ANR), under the CPJ ITTI. Ethical standards. The research meets all ethical guidelines, including adherence to the legal requirements of the study country. 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