DigitalTwinning(DT)hasbecomeamaininstrumentforIndustry4.0andthedigitaltransformationof manufacturingandindustrialprocesses. Inthisstatementpaper,weelaborateonthepotentialofDigital Twinning as 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 digitaltwinapplicationsforstrategicdecisionmaking, discussingitspotentialforsituationawareness, 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 weelaborateontheseparateclassesofDT,rangingfromsimpleimagesofasystem,allthewayto interactivereplicasthatarecontinuallyupdatedtoreflectamonitoredsystemathand. Werootour approach in the adoption of hybrid modelling as a seminal tool for facilitating twinning applications. Hybridmodellingreferstothesynergisticuseofdatawithmodelsthatcarryengineeringorempirical intuition on the system’s behaviour. We postulate that modern infrastructures can be viewed as cyber-physicalsystemscomprising,ontheonehand,anarrayofheterogeneousdataofdiversified granularity and, on the other, a model (analytical, numerical, or other) that carries information on the system’sbehaviour. Wethus proposeHybridDigital Twins(HDT)as themainenabler ofsmartand resilient infrastructures. ∗Both authors contributed equally to this work. Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint Keywords hybrid digital twin ·decision making ·smart infrastructures ·resilience support 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 development of next-generation digital twins for smart infrastructures. This study summarises the insights gained from a round-table discussion on Decision Support for Infrastructural Asset Management, which was held as a joint initiative of the Future Resilient Systems (FRS) program at the Singapore-ETH Centre and the DESCARTES interdisciplinary program of excellence at CNRS@CREATE. 1 Introduction Engineeringinfrastructuresformthebackbonesofoursociety. UnderthemandateofIndustry4.0,thedigitalrevolution hasbroughtaboutaparadigmshiftinhowwedesign,produce,andinteractwithphysicalassetsOztemelandGursev [2020]. While digitisation has been more broadly applied in the context of manufacturing and production technologies andthehandlingofindustrialassets,itremainsstilllessexploitedinthecontextoflarge-scalebuiltenvironments,i.e.,in thecontextofinfrastructures. There, thefieldhasbeendominatedbytheadventonBuildingInformationModels(BIM) which, however, remain more of static images of a system, finding application primarily during the phase of design and construction Sacks et al. [2020]. Nevertheless, also at this scale, the concept of Digital Twinning has the potential notonlytodeliverinformationonthestateofthesystem“as-is”,buttofurtherinformdecisionsupportframeworks thatoperatethroughoutthestructurallife-cycle,namelyfromthestageofmanufacturing/construction,tothestageof operationunderstandardaswellasextremeloadsandhazards,andeventuallythedecommissioningphase. Ingenerating value,DTrepresentationsoughttoallowforaclosedloopexchangebetweenthedigitalandphysicalasset,byextracting information garnered from operating physical systems (e.g. by means of monitoring), distilling this information via use of the digital representation, and finally exploiting this analysis for acting onto the physical asset with a view to protecting critical infrastructure and guaranteeing its resilience Argyroudis et al. [2022]. Infrastructureresilienceishereemployedasamaincriteriononthebasisofwhichstrategicdecisionmakingcanbe accomplished. Itcanbedefinedasthecapabilityofanticipating,preparingfor,andadaptingtoenvironmentalchangesas well as coping with, responding to, and recovering 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. Such studies put forth resilience quantification and enhancement frameworks across scales, extending from the component, to the asset, and further to the interconnected networks level Ouyang et al. [2012], Cimellaro et al. [2016], Dhar and Khirfan [2017], Koliou et al. [2020], Liang et al. [2023], Blagojević et al. [2023]. Such an analysis is typically conducted in the pre-incident phase on the basis of simulated scenarios that employ stochastic deterioration/fragility models and restoration models, without thus accounting for information that is gathered from the actualsystemovertime. Here,weconcernourselvesprimarilywithdecisionmakinginthecontextofthe during-incident andpost-incident phases that usually require fast (sometimes even real-time) decision making. The premise for such an investigationliesintheassumptionofavailabilityofdatafromsuchinfrastructuralassetsandsystems. Thisisnowadays justified by the information that is increasingly becoming available, not only in terms of digitised logs that contain inspection information on structural systems, but also due to adoption of sensing technologies for monitoring such systems on both a periodic (e.g. Non Destructive Evaluation) and continuous (e.g. Structural Health Monitoring) basis Kamariotis et al.[2024]. Currently, nointegratedframework existsfor quantifying and enhancing infrastructural resilience based on the fusion of such data within digital twinning (DT) techniques. In this context, 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 HDTsin support of decision making for performant and resilient infrastructures, •and to, finally, highlight the associated challenges and opportunities in this respect. 2 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint 2 Motivation for integrating HDTs in Infrastructural Management Theintegrationofdigitaltwinningtechniquesintothemanagementofinfrastructurescanreshapetheprocessofstrategic planning for operation, maintenance and resilience enhancement by offering decision-makers unprecedented, and often real-time, insights into the status and behaviour of physical assets, which refer to any physical object, system, or infrastructure holding economic value to an organisation (e.g. building, bridge, wind energy structures). Unlike traditional periodical and reactive decision-making methods, the real-time dynamic insights provided by hybrid digital twinning(HDT)empowersdecision-makerstoattainamorecomprehensiveunderstandingoftheoverallexposureto risks and hazards. It is reminded that the term hybrid implies exploitation of a model of the system, which in turn implies amplified predictive potential, including tasks that relate to virtual sensing Vettori et al. [2023] Papatheou et al. [2023], meaning prediction of the response of the system in critical positions that lie beyond the positions of sensor observations/measurements. This allows for prompt response to - and recovery from - emerging disruptions and strategic adaptation to changing conditions and requirements (e.g. increase of operational loads of transport infrastructure assets). The integration of digital twining introduces predictive analytics, which are informed on the basis of evidence collected from the operating physical asset. This forecasting potential supports system readiness and thereby resilience, by providing decision-makers with the tools to navigate uncertainties with greater confidence and, if required, proactively plan system repair and intervention in both a short-term, e.g. in the face an impending hazard, and long-term horizon, e.g. when planning for maintenance policies under slow evolving deterioration effects. In this sense, a DT framework with extended predictive abilities, can not only fortify operational resilience but also foster a proactive and adaptive approach to infrastructure management. A main element of DTs, and HDTs, lies in the exploitation of telemetry andremote operationand controlfeatures,which allowto oversee andmanage assetsremotely, i.e., without the need for physical access. This functionality allows to streamline decision-making in challenging environments such as inaccessible (e.g. offshore) locations, enhancing operational efficiency and adaptability. In the context of resilience enhancement, such an integrated approach allows decision-makers to develop a scalable framework that can proactively plan and optimize the entire life performance of assets, contributing to long-term sustainability and adaptability. HDT embodies a data-driven, dynamic, possibly real-time, and closed-loop approach to asset management, allowing to account for complex interdependencies, curb assessment uncertainty, and operate on the basis of assessment of the system as-is and not as-deployed. In spite of the benefits in the use of DTs within the context of infrastructure management and resilience, the uptake has been slow in practice. A reason for this is associated with the diverse interpretations and lack of clarity surrounding the definition and applicability of a DT, 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 DTs within the domain of smart infrastructures and explore potential needs for expanding their usefulness and maximising their uptake. 3 Hybrid Digital Twins - HDTs The implementation of digital twins presents its own set of challenges. Data integration, modelling complexity, transparency, communication among agents, and ethical concerns relating to automated decision making are significant challenges that must be addressed for ensuring actionable application. However, the first step is to propose a framework for cross-disciplinary understanding that sets the foundation to any future development. 3.1 Definition and Interpretation of Digital Twins The concept of a Digital Twin (DTs) finds its roots in NASA’s Apollo XIII project, where digital simulators and a physicalreplicawereconnectedtotherealspaceshiptoreceiveinformationfromittoupdateitsoperatingcondition andproposemissionrulesbasedonitsstate,especiallyincriticalconditionsShaftoetal.[2010]. Asreportedinthis document, such 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. WiththesurgeofIndustry4.o,DTsbecameago-totermacrossseveralfields,however,thedefinitionofthetermmay stillappearblurredandunclearWrightandDavidson[2020]. Certainsources(Hughes[2018],AlamandElSaddik [2017], Platenius-Mohr et al. [2020] to name a few) define digital twins 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 framings within the engineering context, describe a DT as a process that defines a closed loop between the physical entity and the digital replica Committee et al. [2020], McClellan et al. [2022]. This necessitates a digital workflow of information, parameterized models, diagnostic and prognostic algorithms and control tools, often aggregated in a visualisation layer, which generates value for the user and facilitates decisions. 3 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint TheoriginoftheDTconceptmaybetracedbacktoapresentationbyMichaelGrievesattheUniversityofMichigan in 2002, which aimed to establish the so-called Product Life-cycle Management (PLM) framework Grieves [2002]. However,thefirstknowndefinitionforDTisconsideredtobetheonepublishedbyNASAinShaftoetal.[2010]. In this definition, a DT is claimed to be an integrated multi-physics, multi-scale, probabilistic simulation that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin forrecommending 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. Followingthisspirit,similardescriptionshavebeenassignedtoDTsGlaessgenandStargel[2012],ElSaddik[2018], Liu et al. [2021], Xu et al. [2019]. The recent AIAA position paper Committee et al. [2020] defines a digital twin as: “A set of virtual information constructs that mimics the structure, context and behavior 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 realize value." We discern three main characteristics of a DT in the various definitions offered: •A physical asset from which information is extracted, implying presence of a monitoring system. •Adigital(virtual)representationofthephysicalelement,representedbyamodelthatcapturesthebehaviourof the physical counterpart. Here we distinguish four levels of description: component, asset, system and process. •A one-or two-wayinformation flowprocess, dependingon the application,that linksthe digital andphysical counterpart to ensure continuous tracking of the behaviour 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 to actonitwithimprovedconfidencemargins. Aone-wayprocessisalsoreferredtoasa"digitalshadow". Bergs et al. [2021]. At this point, it is worth referring to the definition of Dynamic Data-Driven Application Systems (DDDAS) in the work ofBlaschetal.[2013],whoproposethisasaframeworkfordynamicupdatingofsimulators(models)withdataobtained from sensor networks and monitoring devices. While this framework focuses on the aspect of updating of a digital mirror (essentially) of the operating physical system, the purpose of DTs extends beyond computational modelling and updatingtoincludeperformanceandconditionassessment,analysis,andoptimisationofphysicalassetsthroughouttheir lifecycle. FollowingGrievesandVickers[2017],inthisworkwedefineDTsalongaclassificationinthreeessential categories (classes), according to the purpose served by the twin: •theDigital Twin Prototype (DTP) •theDigital Twin Instance (DTI) •theDigital 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 characterise and fabricate a physical counterpart (for instance requirements, 3D models, lists of materials, processes, services, and disposal procedures). This type, is usually established at design stage and is strongly linked to the characteristics and objectives of BIM (Building Information Modelling) Definition [2014]. In the work of Grieves and Vickers [2017], a Digital Twin Instance (DTI) , is described as a specific corresponding physicalproductthatanindividualDTremainslinkedtothroughoutthelifeofthatphysicalproduct. Wehere,adopt theinterpretationofMcClellanetal.[2022]inrelationtothenotionofaninstance,anddefineaDTIastheDTofan individualinstanceoftheproduct,onceitismanufacturedandequippedwithsensorsthatgeneratedata. Thisimplies that the DTI embodies the notion of information flow between the physical and digital counterpart. Finally,the DigitalTwinAggregate(DTA) GrievesandVickers[2017],McClellanetal.[2022]canbedefinedasthe collectionandprocessingofdatafrommultipleinstancesofDT(DTIs),forinterrogationofandpossibleactionona collection of assets. Essentially, it describes a computing construct that allows to gather and analyse data from various DTIs to gain insights with respect to a broader range of physical products or processes. A DTA, relates to the concept oflearningfromfleets,orpopulationsWordenetal.[2020],reflectingamoremassivecollectionofdata,whichcan enhance predictive and prognostic capabilities. In these definitions, it is important to note that the information flow is assumed to be available throughout the life of the asset. This implies that individual instances of updated models, which do not continually follow a physical asset may be merely viewed as snapshots of a twin and not actual DTs. On the other hand, in the context of engineering, it is further helpfultodefinethenotionofReal-TimeDigitaltwins(RTDTs),signifyingdigitalrepresentationsthatareupdatedinan online fashion, i.e., on the fly (in real- or near real-time) as data is attained. 4 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint Accompanying the real asset along its useful life requires 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. [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. Under this premise, we refer to Hybrid Digital Twins (HDT) as twin constructs that adopt a hybrid modelling approach. Such an approach combines different modelling techniques or methodologies to create a more comprehensive and accurate representation of a system or process. In the context of DT, hybrid modelling involves integrating various modelling schemes, such as physics-based models, data-driven models, and ML algorithms, to create an accurate representationofaphysicalassetorsystemthatgoesbeyondtheavailableknowledge. HDTextendthisconcepttodigital twin technology Chinesta et al. [2020], Haywood-Alexander et al. [2023]. Hybrid models typically entail incorporation of physics into the simulation process allowing for interpretable diagnostics and generalisation in their predictive ability. By combining physics-based models with data-driven approaches, hybrid digital twins can provide more accurate predictions, real-time monitoring, and decision support across a wide range of applications Wagg et al. [2020b]. Inthisparadigm,thereisanincipientsub-classofDTsthatisexpectedtoleadnextdevelopmentsinthedomain: the cognitive digital twin (CDT) Abburu et al. [2020b], 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 (CDTs) refers to systems that can interact with both physical and virtual environments to autonomously make smarter decisions based on the context Zheng et al. [2022], Abburu et al. [2020a]. Although both HDTs and CDTs use machine learning 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 complexinterpretation,alsocalledperceptionMoyaetal.[2023],reasoning(autonomouslymakingdecisionsabout its performance), automatic recalibration for enhanced decision making Arcieri et al. [2021], and interactivity 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 withinthedigitaltwinparadigmleveragestheexpertiseandexperientialknowledge,servingasacrucialfacilitatorin comprehending the underlying rationale of decisions and their appropriateness within a specific context. Consequently, theexpert-in-the-loopparadigmunderscoresthesignificanceofmodelexplainability,asalientfeatureduringvarious interaction phases within a Cognitive Digital Twin (CDT). At their core, DTs are powered by use of simulators/models that provide representations of complex systems, processes, orphenomenaofinterest. Inspiteofthecontinuousgrowthoftoolsfordata-drivenmodelling,thereisstillaremarkable prevalenceoftrustinBIM(BuildingInformationModeling)representations,orpurelysimulation-andphysics-based models that are cross-validated across industries. Such models rely on finite elements and well established formulations such as fluid mechanics, transient dynamics, and degradation models. In order to make such models actionable within a twinning framework, it is necessary to deliver reliable, yet reduced order representations, which can incorporate physicsinawaythatismanageablefortheprocessathand. Thisiswhere Reduced Order Models (ROMs) presenta salient contribution, in that they deliver accelerated emulators of a monitored system at reasonable computational costs Chinestaetal.[2011],Amsallemetal.[2012],Farhatetal.[2018],Agathosetal.[2022],Vlachasetal.[2021],Agathos et al. [2024], Idrissi et al. [2022], Frangos et al. [2010], Kapteyn et al. [2020]. ROMs are mathematical representations ofcomplexsystemsthataimtoprovidesimplifiedbutaccuratepredictionsofsystembehaviour. Whenincorporating physics principles, such ROMs are often referred to as intrusive Chinesta and Cueto [2014]. While non-intrusive, i.e., purely data-driven techniques do exist and employ data from simulations or experiments to bypass physics Hernandez et al. [2021], Ibáñez et al. [2018], the imposition of physics biases is often desirable to ensure interpretability Vlachas et al. [2012], Liu et al. [2022a], Bacsa et al. [2023]. When extending prediction/estimation at the system level, the DT may require the incorporation of representations and simulationsofinterconnectedsystemsorcomponentsHeussenetal.[2011],Ouyang[2014],Schluseetal.[2018],Liang and Xie [2021]. Such representations are defined as System-Level Models . For instance, energy system network models Heussen et al. [2011], Ouyang et al. [2017] provide a detailed understanding of how energy flows through various components, aiding in the optimisation of energy consumption and the identification of potential inefficiencies. Once models have been selected, AR, VR, and DT technology create a bridge between the physical and digital realms Moyaetal.[2022a],Badíasetal.[2019],Vettorietal.[2023]andaugmenttheuserinterface,fosteringanenhanced comprehension, collaboration, and decision-making process across diverse domains Michalik et al. [2022]. Virtualenvironmentsoftenusevirtualsensingtosimulatethebehaviourofsensorsthatexistintherealworld. Although remotesensingfacilitatesthecreationofaccurateDTofinfrastructuresystemsBadoetal.[2022],Kaartinenetal.[2022], 5 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint Figure 1: Landscape of the DT paradigm. DTPs and DTIs reflect the basic characteristics of the twinning technology in designandoperationrespectively. TheHDTincludeshybridmodellingtoenrichsimulationswithaspectsofphysics and ML to accurately mimic the behavior of real systems. Such a construct offers higher interpretability. Finally, CDT would combine previous technologies with scene understanding and autonomous decision-making. As a result, the DT progressivelyincreasesincomplexityandopportunities. DTscanalsoevolveonatemporalscale,whereRTDTsare specific DTs that are updated in a more frequent, real-time, manner. Dorafshan et al. [2018], Phillips and Narasimhan [2019], there are still scenarios where it is impractical, expensive, or insufficient, such as the case of the evaluation of the load and prediction of the performance of DTs of wind turbine blades Vettori et al. [2022]. These virtual sensors generate data within a virtual environment, which can then be used to simulaterealisticscenarios,testalgorithmsforsensordataprocessingandanalysis,andperformdynamicadaptation within virtual environments. 3.2 Role of Internet of Tings, Real-time Data Analytics, and Artificial Intelligence TheconceptofInternetofThings(IoT)touchesontheaspectsofsensorselection,deployment,use/acquisition,and connectivity. IoT represents not only the deployed sensing network but also the purpose of connecting and transferring information. Mostoftheinformationcomesintheformoftime-seriesorimage-basedrepresentations,collectedvia appropriate compression schemes. IoT regimes often entail multiple and, thus, heterogeneous or multi-modal data sources. Hence, DT must be designed to flexibly tackle diversified types of data inputs, which is usually tackled via the aspect of fusion. Moreover, even though some measurements (strains, pressure, temperature) can be directly correlated toquantitiesofinterest,thisisnottrueforothersources,whichdeliverindirectinformation(suchasvibration-based ones). In order to derive physical insights from a collection of heterogeneous and often indirect observables, it is necessary to resort to physically-infused hybrid modelling. Inthiscontext,werevisitthepreviouslyintroducedconceptofRTDTs,whichfocuson real-time performance,reflecting a growingdesire ofthe industry. Itis importantto properly definewhat real-time implies inpractice andconsider the appropriate time scale of system performance assessment and require rate of data flow. We define an RTDT as a digital twin whichevolvessynchronously tothe physical counterpart,by measuringand processing changesthat occur inthe physical counterpart and correspondingly updating the virtual replica, and possibly implementing feedback (in the form ofactions)tothephysicalasset,inanonlinefashionZipperandDiedrich[2019]. However,aperfectlysynchronous response, in the sense of hard real-time, implying minimal delays and alignment at a high sampling rate, could be inefficientinpractice,involvingdisproportionalallocationofresourcesandinfrastructureandrisksofoverheadand latency. Thus, the meaning of "real-time" performance in a digital twin is used in a flexible context and will differ 6 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint Type Description System Loads and Response data Time-series data on environmental sources (e.g., pressure, wind/wave velocity), and system response/condition data (e.g., acceleration,displacement,strain,powerproduces,acousticemis- sion signals, vision-based data on defects) External Environment data Environmental conditions and stressors that may impact the asset ortheassociatedprocesses(e.g.,temperature,humidity,pollution, CO2 concentration, precipitation) Historical and domain knowledge Time-stampedrecordsofpastoperationalandmaintenanceactions, whichcanbeusedforanalysingandidentifyingtrendsandpatterns Geospatial and connectivity data Geographic features, coordinates, and spatial relationships of physical entities (e.g., GIS layers, satellite imagery) and relation- shipsbetweendifferentcomponentswithinasystem(e.g.,network diagram, fault tree) Table 1: Summary of main sources of data for DTs based on the DT purpose, ranging from instantaneous to periodic updates, based on the rate of data collection and time restrictions in assuming corresponding action/decisions. Machine learning and deep learning can synergistically be combined with hybrid models, enhancing their explainability andpredictivepotential Montánsetal. [2019],Champaneyet al.[2022b]. Suchan instancehas emerged inphysics- enhanced or physics-informed modelling, which capitalises 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]. In this context, one can refer to physics-informed digital twins (PIDT) as those DT representations that integrate domain-specific knowledge of physical laws and principles, providing interpretable models that capture the underlying systemdynamicsKapteynandWillcox[2020],Liuetal.[2022a]. Althoughthesemayrequiremoreefforttodevelop and calibrate,PIDTsoffer transparencyand fidelity, rendering thesewell-suited for applicationswhere understanding andcertifiabilityarecrucial. Ultimately, thechoicebetween theseapproaches dependson thespecific requirementsof the problem at hand, balancing predictive power with interpretability and reliability. Some versatile examples are those thatemployknowndescriptionsofthesystem,suchaspartialdifferentialequations,oralgorithmsfoundedinknown physicallaws HAYWOOD-ALEXANDERand CHATZI[2023],Zhangand Zhao[2023],Yang etal.[2024], Vlachas et al. [2022], Tatsis et al. [2022], such as those of thermodynamics Cueto and Chinesta [2023], Hernandez et al. [2022], and preservation of physical quantities Kirchdoerfer and Ortiz [2016], Bacsa et al. [2023]. AIstronglydependsondataqualityandavailability. Toovercomethislimitation,newtechniquesdrivenbyphysical knowledge may find patterns and reconstruct missing information. This involves embracing the smart data regime, which involves the right information, in the right moment, and right place. 3.3 The smart data paradigm Data collection process can pose challenges that require a comprehensive framework for intelligent data collection, processing, and use. Table 1 summarizes main sources of data in DTs. In this context, wireless technology plays a pivotal role in data acquisition and communication in DTs. in the future prospects of this technology, 6G networks can be potential enablers in the synchronisation-delay-accuracy commitment Bariah et al. [2023]. SomeearlydefinitionsontheincipientconceptofsmartdatarefertotheextractionofvaluableinformationfromBig Datatosupportdecisionmaking[Lenketal.,2015,Iafrate,2014]. However,thisterminologyhasevolvedtorefertothe formulation of data practises that focus on answering four questions as detailed in Chinesta et al. [2020]: (1) what data to collect, (2) where to deploy sensors for extracting relevant information, (3) when and for how long to deploy the system, and (4) at which scale. As a result, the so-called smart data pipeline possesses some specific characteristics. A main trait pertains to trustworthiness, i.e., the extent to which reliability and accuracy is ensured, and whether credible sources are used. This trust is established through robust data collection methods, quality assurance processes, and adherence to data governance standards Kirchen et al. [2017], Bicevskis et al. [2017], Hong and Huang [2017]. The smart data paradigm is also linked to the extent to which it facilitates downstream tasks, usually linked to the conceptof cognitivecapabilities ,whichrefertoadvancedanalysis,interpretation,andlearningAbburuetal.[2020a], Zheng et al. [2022]. A very intuitive classification of digital representations in relation to their function is offered in Wagg et al. [2020a]. Using techniques such as AI, ML, and natural language processing, cognitive data systems understandandderiveinsightsfromcomplexdatasetstoreachadesiredcharacteristic,namelyinterpretability. Thisisa pivotal characteristic for hybrid modelling Champaney et al. [2022b] and can typically be achieved through appropriate 7 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint exploitation of prior knowledge on the system and its behaviour Chinesta et al. [2020]. Akin to the concept of gathering meaningful data is the concept of active learning, which allows to target maximal information extraction on the basis of minimal data Settles [2009], Chabanet et al. [2022]. Active learning makes use of either human expertise Khamesi et al. [2020]orMLschemes,allowingforselectiveguidanceonlabellingspecificunlabelledsamples,optimisingresource utilisation and integrating human insights into the learning process. However, an important challenge is the fact that not all required data can be measured. Internal variables, such as energy, entropy, and strain, cannot be directly measured, and some variables, such as stress and damage, are difficult to accessaccurately. Partialobservationsalsooccurinspaceandtimeanditisimportanttounderstandwhereandwhento measure, with a view to optimising data collection efficiency and ensure data relevance Di Lorenzo et al. [2023], Bigoni etal.[2020]. Datacompletenessreferstothenotionofensuringavailabilityofallrelevantinformationforinformingthe digitalasset,withaviewtoenhancingthereliabilityandapplicabilityofthemodelprediction. Withtheappropriate data collected, and a proper understanding of the system, hidden patterns and information can be recovered Champaney et al. [2022a], Moya et al. [2022b], Schöbi and Chatzi [2016], Liu et al. [2022b], Liang et al. [2020], Bermejo-Barbanoj et al. [2024]. Data quality and observation stochasticity need also be considered in the hybrid modelling paradigm Liuetal.[2022a],Vettorietal.[2024],topropagateandevaluateuncertaintyinthepredictionandassesitsvalueand trustworthiness. 4 Applications in Management and Resilience of Smart Infrastructures The information generatedand transformed by the HDTis expected to support long-termdecision making through an asset’slifecycle. Whatneedstobefurtherhighlightedisthattheseassetsareusuallyorganisedinaninterdependent mannersoastosupplyspecificserviceorfunctionality. Hence,hybridtwin-enhancedknowledgeoncomponentsshould beassembledandtransferredontothesystem-levelforenablinginformedandcomprehensivedecisionsinsupportof infrastructure management and recovery from extremes, as mandated by the need for resilience. The term resilience is commonly employed in infrastructure engineering to assess the system’s capacity to endure and bouncebackfromdisturbancesordisruptionsBruneauetal.[2003],Labakaetal.[2016],Ouyangetal.[2012]. Forbetter understanding and visualisation, Fig. 2 depicts a time-evolution resilience curve in terms of performance/functionality of an infrastructure system, under the impact of both long-term effects (e.g., climate change/ ageing/ corrosion/ fatigue/ deflection) and short-term extreme events (e.g., earthquake/ flood/high gusts) throughout its life-cycle. Infrastructure resilience is commonly quantified using metrics and indicators (e.g., residual functionality, downtime and recovery time)thatcanbecomputedfromactualdataorsimulatedbasedoncorrespondingresiliencecurvesPoulinandKane [2021]. Noteworthy,undernormalcircumstances,thelossoffunctionalityinaninfrastructuresystemistypicallynot significant and takes a long time for performance to degrade below the performance threshold. This is attributed to the low probability of multiple component failures occurring simultaneously within the same infrastructure system. However,the situationchangesunderextreme conditions,where anumberofcomponents becomesmorelikely tofail. Consequently, the functionality of the network may experience a sudden and unexpected reduction below the predefined target threshold during such extreme conditions Mohammadi and Taylor [2021]. This highlights the importance of consideringandpreparingforexceptionalscenariosthatcouldleadtosimultaneousfailures,ensuringtheresilience of infrastructure systems under adverse circumstances Arcieri et al. [2023], Fang and Sansavini [2019], Didier et al. [2018], Blagojević and Stojadinović [2022], Rehak et al. [2018], Francis and Bekera [2014]. 4.1 Benefits and Status of DT-powered Decision Making As mentionedDTsare increasingly enteringthe assetmanagement process, renderingseveral benefits. A firstadvantage pertains to the reaction time, allowing for rapid response and alleviating the delays associated to non DT-informed approachesthatarenecessarilyofreactivenature,oftenresultinginincreaseddowntimeandincreasedmaintenancecosts. Moreover, the pure reliance on historical data limits predictive capabilities, leading to suboptimal policies that may not anticipatefuturechallenges. Intermsofresourceallocation,conventionalpracticesmaysufferlackofreal-timeinsights, reliance on subjective schemes that are prone to human error (e.g. inspection practices), potentially yielding inaccurate assessment. Additionally, limited data integration in conventional decision-making, involving disparate sources of information,hampersholisticdecision-making,particularlyforcomplexandinterconnectedinfrastructuresystems. This fragmentationincreasestheriskofinfrastructuremismanagementandpotentialfailures. Onthecontrary,theuseofa DT allows to adapt to changing conditions, technological advancements, and shifts in infrastructure requirements, thus, favouring the resilience of infrastructure systems. To this end, the integration of hybrid digital twinning in decision-making motivates a paradigm shift by providing proactive, on-time, and simulation-driven insights, promoting adaptability, and enhancing overall system understanding comparedtotraditionaldecision-makingframeworksMakhouletal.[2024]. Thisevolutionisparticularlysignificant 8 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint Figure 2: Time-evolution resilience curve of component/asset/infrastructure network exposed to various environmental changes throughout their life-cycle with and without the monitoring system incomplexanddynamicenvironmentswhereamoreresponsiveandaccuratedecision-makingprocessiscrucial. In this manner, a so-called smart decision would be achieved, referring to the policy that can inform sequential actions to maximise resilience with minimum cost, by dictating what actions should be taken, when and where to take that action from a system-level perspective. The growing recognition of the unparalleled efficacy of digital and hybrid twin models is manifesting in their escalating deployment within tangible infrastructure systems. As asset owners and managers increasingly acknowledge the transformative impact these models wield, there is a discernible trend towards incorporating DT across diverse sectors of real-world infrastructure Zhao et al. [2022], Kuo et al. [2021]. This surge in adoption is a testament to the significant advantages these models confer in terms of predictive maintenance capabilities, operational efficiency enhancing, smart cities planning, and overall resilience improvement in the face of dynamic challenges. This trend is expected to persist and expand as DT technologies continue to evolve, offering innovative solutions to complex problems within the realm of infrastructure sustainability and emergency management. 4.2 Use cases Thissection aimstoelucidatethetransformativeimpactof DTapplicationsonstrategic decisionframeworks andthe overall enhancement of infrastructure system resilience. PredictiveMaintenance standsasaprimaryusecaseforDTtechnology. Interactivedigitalrepresentationsthatallow for continuous monitoring, analysis and intervention on infrastructure components. The integration of sensor and historical data with predictive models empowers decision-makers to optimise system performance and anticipate failure. This facilitates proactive scheduling of maintenance activities, minimising risk, and enhancing the reliability and longevity. Recentrepresentativecasestudiesincludethecondition-basedmaintenanceplanningofarailwaysystem based on the geometric measurement of track recorded periodically by a mobile sensing system on the train Arcieri et al. [2023]; diagnostics and prognostics of wind turbine structure health based on time-series environmental measured data, vibration data Bogoevska et al. [2017], and SCADA data Urmeneta et al. [2023], Schlechtingen et al. [2013]; fault diagnosis and condition based maintenance of overhead power transmission lines utilizing the Cablewalker robotic system consisted of a laser scanner, a stereo camera, or a magnetic scanner Tajnsek et al. [2011], Gitelman et al. [2019]; predictive maintenance of manufacturing facility by monitoring parameters from sensors embedded within equipment, suchasreal-timetemperature,vibration,andlubricantconditionofthemotors,bearings,andgearboxesOlivottietal. [2019],Yuetal.[2019]; autonomousflawsdetectionofbridgebasedonimagescollectedthroughaninspectionrobotor unmannedaerialsystemsDorafshanetal.[2018],Galdellietal.[2022];BIMaugmentedmodelsbasedondrone-imaged damage detection enhanced with AI To et al. [2021];temperature prediction from the the building scale (BIM buildings) to city scale (CityGML) taking into consideration major anthropogenic heat sources and wind fluid dynamics through the Virtual Singapore digital twin (VSdt) Ignatius et al. [2019], Gobeawan et al. [2018]. OperationOptimisation ,inthecontextoflogisticsandsupplychainmanagement,distributedagent-basedDTsimulation can be performed by integrating real-time logistics data, external need trends, and optimisation algorithms, assisting in streamliningoperationsandoptimisinginventoryParketal.[2021]. Insmartmanufacturing,real-timemanufacturing data, historical performance metrics, and dynamic simulation models cab be integrated into the DRL-based digital modeltoidentifybottlenecksandrefinemanufacturingpractices,leadingtoincreasedefficiencyandcostsavingsXia 9 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint etal.[2021]. Forbuildingenergymanagement,digital-twin-basedmethodscanutilisebuildingsensornetworksand heating/ cooling data to optimise energy design, improve users’ satisfaction, and reduce energy costs Bortolini et al. [2022]. In the context of traffic management, DL algorithm can be employed by utilising real-time traffic data and dynamic simulation models to optimise signal timings under disturbance and reduce congestion Rasheed et al. [2020]. UrbanPlanning undergoesarevolutionarytransformationwith theapplicationofdigitaltwintechnology,particularly in the realm of Smart and Green City Development Deng et al. [2021], Caprari et al. [2022]. In this context, DTs can be used to create virtual representations of entire cities by incorporating weather conditions, geospatial data, trafficflowsimulations,buildingstructureandinfrastructuremodels,etc. withtheaimofensuringamoresustainable andefficienturbanenvironment. Recentrepresentativecasestudiesunderscoretheimperativeofreevaluatingurban planninginlightofclimatechangerepercussions(asobservedinDublinDTWhiteetal.[2021]),evolvingenergyneeds (exemplifiedbyresearchinCambridgeDTNochtaetal.[2021]),biodiversitypreservationinitiatives(asevidencedin SingaporeDTGobeawanetal.[2018],Ignatiusetal.[2019]),governanceframeworks(asanalyzedinstudiesfocusedon Cambridge, Singapore, and Zurich DTs Nochta et al. [2021], Ignatius et al. [2019], Schrotter and Hürzeler [2020]), land allocation dynamics and social equity considerations (as exemplified in Herrenberg, Nigeria, and Zurich DTs Dembski etal.[2020],Enoguanbhoretal.[2021],SchrotterandHürzeler[2020]),andenvironmentalqualityassessments(as illustrated in Nigeria and Helsinki DTs Enoguanbhor et al. [2021], Hämäläinen [2021]). These studies advocate for urban planning strategies that prioritize flexibility, adaptability, and incremental adjustments to effectively address multifaceted challenges confronting modern cities. Extreme event handling receives growing interest given the increasingly frequent extreme events (e.g., earthquake, tornado, wildfire) we have experienced recently. DTs can play a crucial role in supporting decision-making by reducing uncertaintyofconditionassessmentandinturn,facilitatingtheefficiencyofemergencyreactMakhouletal.[2024]. UsecasesincludedevelopingDTsthatcansimulateandpredictthespreadofwildfires,allowingdecisionmakerstoplan evacuation routes, deploy firefighting resources strategically, and communicate timely warnings to community Zhong et al. [2023]; developing deep RL-based decision framework to make rational decisions for transportation management under hurricanes based on the monitoring weather information and traffic flow Li and Wu [2022]; introducing a spatial-temporalgraphDLmodelthatusesheterogeneouscommunityfeatures(physics-baseddataandhuman-sensed data),topredicturbanfloodinginreal-time. Thismodelenhancesriskmappingforbettersituationalawarenessand response strategies, verified using the 2017 Hurricane Harvey in Harris County Farahmand et al. [2023]. The common thread across these applications is the ability of DT to provide a dynamic, data-driven foundation for informed decision-making. In essence, the combination of robust data, advanced modelling, and diverse use cases exemplifies the multifaceted impact and potential of DT in revolutionising decision-making processes. 5 Future Outlook 5.1 Future Goals Asdistilledintheanalysisexposed,twomainobjectiveshavebeenidentifiedforfutureDTs. First,itisimperativeto develop future-proof systems, that do not only draw insights from previous experience, but also anticipate and adapt to forthcoming changes. This capability would enable proactive adjustment and resilience in unpredictable circumstances. Additionally, DT are expected to work on multiple cross-connected levels in which the twins operate in reality. Tosetthefoundationtoachievethesegoals,itisnecessarytofirstworkonacommonlanguage,yetequallyessential isimplementingframeworkstoeffectivelyorganizethevastarrayofmetadataandmodelinformation. Thisiswhere knowledgebasisemergesasindispensabletoolforhousingvitalinformation,insights,andmodelsMarykovskiyetal. [2024]. By leveraging knowledge bases, establishment of uniform data models and vocabularies, organisations can promote smooth communication and cooperation within digital twin ecosystems. This common language not only encouragesstandardisationandcoherence formodels,butalsofosterscooperation amongstakeholdersfromdifferent fields and sectors. In essence, it sets a solid foundation for more efficient and flexible digital twin solutions that can addresscomplexreal-worldproblems. Thisideacouldalsobeexpandedbyhavingahigh-fidelityrepositoryofassets (BIM, visual platforms) across domains. Next, it would be necessary to create a basis for actionably implementing hybrid modelling techniques and intelligent algorithms within a DT framework that can cater to creating value for assets. To this end, DTs must evolve towards decision support, with a focus on analysis tasks, such as independently analysing data, evaluating scenarios, and recommending actions with or without direct human intervention. A profound and interpretable use of AI, ML algorithms, and online data streams will allow DT to independently evaluate the present condition of a system, forecast future results, and recommend the best course of action to attain pre-established goals. 10 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint Allactionsbeingconsidered,afinalgoalinthedevelopmentofDTsforsmartinfrastructureswillbequantifyingthe return on investment (ROI), and modelling the long-term benefits of DT involves assessing both tangible and intangible factors over an extended period. The ultimate aim would be to optimise the strategies of the stakeholders for DT to consolidate their implementation, develop future opportunities, and create value. To this end, a number of approaches for quantifyingthe Value ofInformation have beenrecently put forthand serveas foundational workKamariotis et al. [2022], Zhang et al. [2022], Memarzadeh and Pozzi [2016], Saifullah et al. [2023]. 5.2 Challenges Drivenby industrialdemandsontechnologicalreadinessand maturity,formalframeworksonexploitationofDTsare comingforth. Nevertheless,challengespersistinrenderingDTspracticalforuseinreal-worldapplications,asdiscussed below. Adaptation to changing climates . Climate-related data, such as future weather patterns and extreme events, often involves uncertainties and may be incomplete. Inaccurate or insufficient data can compromise the reliability of digital twinprediction. Also,theamountandrateofdataproducedbysensorsandIoTdevicescanexceedcurrentinfrastructure capabilitiesMashaly[2021],necessitatingscalablestrategiestohandleandanalysethedataflowtoreducelatencyinthe response. Open data exchange . challenges in open data exchange include ambiguous data ownership, data privacy concern Wang et al. [2023], data quality and consistency variability, leading to potential disputes and limiting the availability of relevant data for digital twin systems. Securityandtrustworthinessofalgorithms/data . Datamaybecorrupted,tamperedwith,ormanipulated;algorithms used in DT may exhibit bias and may not under go thorough validation processes; explainability of AI models is often limited; all these can lead to inaccurate representations and flawed decision-making outcomes. Standardisation and certification of DT . Lack of universally accepted standards hampers interoperability and poses challenges in assessing digital twin quality and reliability Burns et al. [2019], Kirchen et al. [2017], Bicevskis et al. [2017], Hong and Huang [2017]. Dealingwithfalse positives/ responsibility for decision . Inalegalcontext, the attribution ofresponsibilitybecomes 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 integration of human expertise and ethical guidelines into automateddecision-makinginDTtomitigaterisksandbuildtrust. Inaddition,traininguserstounderstandandwork 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 organisations, technology providers, and end-users, to develop frameworks, standards, and best practices that promote the responsible and effective use of DT for decision-making in a rapidly evolving technological landscape. 5.3 Opportunities Recent perspectivepapers have highlighted thelimitations ofcurrent digital twintools in urbanplanning, particularly regarding their focus on short-term goals versus the long-term focus of city planning policies Batty [2024] Bettencourt [2024]. Theynoteissuessuchasstaticity,limitedaggregationcapacity,andaprimaryfocusonvisualisation. Emphasising the need for improvement, they advocate for modelling multilevel and multidomain as well as multi-spatiotemporal scalenetworksbettertocaptureinteractionsandthedynamicnatureofurbanenvironmentsfacingvariousstressors. 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]discusstheimportanceofutilisingSmartCityDTfordisasterdecision-makingincitiesfacingvarious stressors. They emphasise the integration of fast and slow modes in decision-making processes and highlight the needforcapturing,predicting,andadaptingtourbandynamicsatvaryingpacestoeffectivelymanagedisaster-related mortality and economic losses. The ongoing standardisation of DTs presents numerous opportunities for industries and stakeholders. Standardized frameworksandprotocolsfacilitateseamlessinteroperabilityandintegration,fosteringcollaborationandinnovation while reducing implementation costs and risks through clear guidelines and best practices. Moreover, standardized data 11 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint formatsandcommunicationprotocolsenhancedataquality,consistency,andsecurity,buildingtrustandconfidence. Despite these benefits, current digital twin standards, including the IFC and ISO series 19650-1:2018, 23247-2021, 24464-2020, 30172, 30173, 37100-2016, IEEE series SA-P2806.1, SA-P3144, IEC series 61850-2024, 62832-2020 and ITU series Y.3090, ITU, 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,therapidevolutionofdigitaltwintechnologiesoutpacesstandarddevelopment,resultinginoutdated guidanceforemergingusecases. Achievingconsensusamongstakeholdersandallocatingresourcesforcompliance alsopose significantchallenges, especiallyfor smallerorganisationsor thosewithlegacy systems. Overcomingthese limitations is essential for advancing digital twin standardisation and realising its full potential across industries. Last but not least, in the rapidly evolving landscape of technology, the demand for open platforms that incorporate previous technologies has become increasingly apparent Robles et al. [2023]. These platforms are designed to facilitate the integrationof variousdata sources, sensors, devices, andapplications withina smartcity environment. Platforms likeiTwinJSBentleySystemsandOpentwinsRoblesetal.[2023]exemplifythepivotalroleofopennessinfostering collaboration,innovation,andinteroperabilitywithinthedigitalrealm. AnotherexampleistheDigitalTwinPlatform (DTCC Platform), developed at the Digital Twin Cities Centre Centre, that incorporates a DTCC builder Logg et al. [2023] Somanath et al. [2023], model and simulation, and visualisation. 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 labour 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. DT must be built to empower the human, not the machine. The exploitation of AR, VR, or virtual spaces (metaverse) as facilitators can democratise 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 Thisstatementpaperaimstosetthefoundationsforthedevelopmentofnext-generationDTsandtheirapplicationto smart infrastructures. We have identified challenges in the data acquisition and simulation that could be addressed throughtheso-called smartparadigms . Thesmartuseofdataenhancesdatacollectionandprocessingefficiencyby selectingwhat,when,where,andatwhichscaletoavoidproblemsderivedfrombigdata. This,combinedwithanalytics enriched with physics improves the interpretation and quality of the results. Additionally, hybrid modelling provides an effective strategy for integrating diverse modelling 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 handlingvarioustypesofdata. Furthermore,weadvocateforexploringtheintegrationofelementsandagentswithin thedigitaltwinframeworktofullyaccountforoperationalinteractionsandconnectionsatdifferentlevels. Lastly,we recommend further investigation into the development of the smart digital twin framework to facilitate automation and intelligentdecision-making processesthat wouldenhance reactionto unpredicted, andpossibly crucial, new scenarios. Weadvocateforaparadigmshiftfromtraditionaldecision-makingpracticesininfrastructuremanagementtowardsmore proactive,data-drivenapproaches. Weproposedevelopingdigitaltwin-enableddecision-makingframeworksthroughout projects’ life cycle and discuss advanced applications including autonomous management, predictive maintenance, adaptivebehaviour,andresilienceenhancement. Furthermore,weoutlinethefutureoutlookforaugmentingsuchdigital twin-enableddecision-makingframeworksbyapplyingexpert-guidedparadigms,formingsystem-levelperspectives, and considering unexpected extreme events, in order to make more informed and comprehensive decisions in support of infrastructure resilience. Acknowledgements This position paper has been developed as part of a roundtable session on the theme of Digital Twinning and Decision Support for Asset Management . Theroundtablewasheldinthecontextofjointcollabo- rationbetweentheFutureResilientSystems(FRS)oftheSingapore-ETHCentreandtheDESCARTESinterdisciplinary program of excellence by CNRS@CREATE. All involved sector stakeholders, including TÜV SÜD, ARUP, MEIN- HARDT, CETIM-Matcor, NAVAL Group, Ministry of National Development (MND), Land Transport Authority (LTA), and GOVTECH, are acknowledged for their participation and active feedback. 12 Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures A Preprint FundingStatement TheresearchwasconductedattheSingapore-ETHCentre, whichwasestablishedcollaboratively betweenETHZurichandtheNationalResearchFoundationSingapore,andCNRS@CREATEthroughtheDESCARTES program,bothresearchsupportedbytheNationalResearchFoundation,PrimeMinister’sOffice,Singaporeunderits Campus for Research Excellence and Technological Enterprise (CREATE) programme. Competing Interests None EthicalStandards Theresearchmeetsallethicalguidelines,includingadherencetothelegalrequirementsofthe study country. Author Contributions 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 visualisation: 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.