AbstractThe paper proposes a solution for a Building Information Modeling (BIM)-enabled Infrastructure Asset Management System (AMS) for road owners. The approach provides asset managers with a strategy for the dynamic use of Information Containers for Linked Document Delivery (ICDDs), considering the requirements of stakeholders across domains in the operational phase. The state of the art shows how information management can be carried out utilizing information containers employing Semantic Web technologies Resource Description Framework (RDF), SPARQL, and R2RML. The key output is developing a web-based platform that implements ICDD containers for asset management. Existing AMS are integrated by using SQL data mapped to RDF-based ontology data in the container. The use of existing domain-specific ontologies for infrastructure in combination with the linkage of domain knowledge to a three-dimensional BIM model is a step beyond the state of the art and practice in the construction industry. Linking inside the container allows for querying data across several information models and ontology-based data to create stakeholder-specific data views. The approach was demonstrated in two use cases. The first was related to the visual inspection of a concrete bridge. The detection of damage and the process of communicating the damage to a contractor charged with the repair were described. The second use case was related to a road pavement and demonstrated how decision-making about maintenance activities can be supported using cross-domain information containers.IntroductionThere are several challenges facing asset managers and owners of transportation infrastructure, such as road networks. Bridges and pavements across the globe are aging and are being subjected to higher loads, faster speeds, and extreme weather impacts such as floods, heat, and so forth, that can cause rapid deterioration. Asset Management Systems (AMS) that store and allow for easy retrieval of high-quality data are critical to managing risks and making informed decisions. Recent developments in internet-of-things (IoT) and sensor technologies provide an opportunity for asset managers to move toward resilient networks and to make decisions that minimize cost and environmental impact. Efficient and safe transportation infrastructure is the basic prerequisite for social progress. Planning, construction, operation, and maintenance of infrastructure requires a significant commitment of both economic and human resources. AMS are used worldwide to estimate the required maintenance measures for assets based on their current and future conditions and dependence on financial resources and objective criteria (Aktan et al. 2019). The ISO 55000ff standards (ISO 2014) provide an overarching multi-industry approach to asset management (AM). One of the critical aspects of AM required to make good decisions is the evaluation of current conditions based on inspection and monitoring.Although periodic inspections are ubiquitous in managing road networks, the requirements and methods differ across agencies and regions. Bridge inspection largely comprises visual assessment combined with some simple basic measurement techniques. In cases in which the results of inspections are not conclusive, in-depth investigation or even maintenance intervention can be triggered. To collect data about road surfaces, high-speed measuring systems have been used for about 20 years with laser technology and skid resistance measurement equipment. Based on the results of the inspections, suitable maintenance measures must be selected considering constraints regarding traffic, accidents, and environmental and natural hazards. The derivation of technical measures, as well as their execution schedules, essentially is based on an analysis of the severity and extent of current and forecasted deterioration. Bridge inspections are used to provide key input to AMS. Although many deterioration mechanisms can develop suddenly (e.g., bridge scour) or can be hidden (reinforcement deterioration), periodic visual inspections are used to provide the majority of data. Given the well-known limits of visual inspection, a range of nondestructive testing (NDT) methods are available (Kušar et al. 2018). Visual inspection and NDT can provide a snapshot of the condition assessment of an asset.For road pavements, condition data describing the surface condition and, above all, data on the structural condition of the pavement are required. The condition data can be surveyed using established image- and laser-based networkwide measurement methods, and then are assessed using an evaluation procedure. In addition, structural measurement systems, such as the traffic speed deflectometer (TSD) used to measure the bearing capacity of the pavement structure, have been developed in recent years for networkwide assessment of structural pavement condition (Pinkofsky and Jansen 2018). Complementary to this, related data on the layer structure can be determined indirectly using ground penetrating radar (GPR) (Solla et al. 2021) or directly using boreholes. For application in an asset management system, these data have to be transferred to a uniform spatial reference system to be able to determine the information that is relevant for the road authorities, i.e., the expected remaining life span and the associated necessary maintenance measures of individual road sections, or networkwide financial requirement forecasts. Therefore, AMS requires substantial effort for data preparation, which can be reduced significantly with the procedure proposed in this paper with the application of the Building Information Modeling (BIM) method.BIM methods already are used successfully for planning and construction in building and infrastructure projects. The use of BIM in the operation, inspection, and maintenance planning of road infrastructure over its life cycle is a relatively new field (Parlikad and Catton 2018; Biswas et al. 2021), but it offers considerable benefits for road authorities. Updating the asset to mirror observed deterioration and the effects of maintenance interventions is the basis for AM. This work process can be improved with BIM, especially due to the precise geometric information. Therefore, four research questions for using BIM for transportation infrastructure AMS are proposed 1.How can data across the asset life cycle be described, updated, and processed in a flexible and efficient way based on open data standards?2.How do standardized information containers need to be prepared to provide data for transportation infrastructure operations?3.How can existing asset management systems (or databases) be connected to BIM models to enable consistent use of data throughout the life cycle of an infrastructure asset?4.How must the technical system architecture be designed in order to implement a BIM-based information portal for the transportation infrastructure asset management?To address these questions, this research provides a concept and a prototype implementation of the BIM-enabled execution of AM for transportation infrastructures using standardized information containers implemented within a web-based platform.The paper is organized as follows. We first provide an overview of AM tasks in the infrastructure domain, existing AMS, and the utilization of BIM in transportation infrastructure. A general overview of information management in BIM projects is provided, and the fundamental terminology and concepts are defined. The research procedure is introduced in the section “Methodology.” Focusing on transportation infrastructure operation, the essential stakeholders, processes, and relevant data are presented in the section “Process Analysis and Concept Development.” Supplementing the technical implementation, the concept and architecture of the system are presented. The implementation is demonstrated and proven in two use cases. The challenges and limitations of the approach are discussed. The paper is summarized, and the research questions are answered in the section “Conclusion.”BackgroundThis section provides an overview of the state of the art and the practice of infrastructure asset management, the application of BIM for transportation infrastructure, linked building data (LBD), ontologies for AM, and the use of information containers for information management.Transportation Asset ManagementTransportation asset management is defined as “a strategic and systematic process of operating, maintaining, upgrading, and expanding physical assets effectively throughout their lifecycle. It focuses on business and engineering practices for resource allocation and utilization, with the objective of better decision making based upon quality information and well defined objectives” (AASHTO 2013). In recent decades, infrastructure owners have moved towards strategic AM, i.e., a systematic approach managing their infrastructure assets to support long-term transportation infrastructure goals and inform investment planning. Owners strive to optimize their limited budgets and resources to adequately plan maintenance activities for assets, thus maximizing the benefit and minimizing the necessity for reactive repairs or asset replacement. Furthermore, transportation agencies are coming under increased pressure to provide more-reliable and safer infrastructure, especially in the light of recent sudden failures such as the collapse of the Polcevera Viaduct in Genoa in 2018 (Calvi et al. 2019). Many infrastructure owners have legacy transportation networks; for example, the rail networks in Ireland (IRRS 2010) and the United Kingdom (UK) (Guler 2013) were constructed in the nineteenth century, prior to the advent of modern engineering standards. In addition, transportation agencies have to handle increasing demand from customers, reduced budgets, and greater challenges to deliver value for money and transparent processes for investments (PIARC 2017). To overcome these challenges, one aim of AM is to align and link processes so that all business units within an agency operate in a coordinated fashion (FHWA 2012). Within the framework of AM, reliable data are needed to obtain information about the current condition of the transportation infrastructure and, based on this, to predict its future state. These data then serve as the basis for the decision-making process on the timing, scope, and costs of maintenance interventions (Hajdin et al. 2019). AMS help to force this decision-making process by supplying results of different case scenarios by comparing several applied maintenance strategies.Asset Management SystemsAMS for roads and bridges can be viewed as a cornerstone of the broader context of infrastructure asset management (Hajdin et al. 2019). These management systems use the results of data collection processes, prediction models, a set of rules to determine intervention options within maintenance planning, and functionality to generate and evaluate projects and policies (Zimmerman and Ram 2015). There are a variety of different AMS solutions on the market. However, a common AMS consists of inventory, inspection, maintenance, and planning modules (Hajdin et al. 2019). One of the key components of the management of assets is the inventory and documentation related to all relevant assets (OECD 2001). An inventory module includes administrative and technical asset data (e.g., bridge and road IDs, geolocation, bridge type, and so forth) and optional information such as photos of bridge elements or pavements. An inspection module enhances the asset data according to the inspection findings, i.e., ratings of the structural (e.g., deck, piers, and abutments) and nonstructural (e.g., safety rail, pavement, and drainage system) elements. A forecast of condition deterioration is the basis for estimating future maintenance. The final component of an AMS is a planning module that enables the managing agency to determine what maintenance and rehabilitation actions should be taken given the current and predicted conditions of the pavement sections and bridges (Ismail et al. 2009).BIM for Transportation InfrastructureBIM for the transportation infrastructure was analyzed extensively by Costin et al. (2018) by reviewing relevant literature to provide insights into the current topics, the state of the art in industry and academia, and future challenges in this domain. They also reviewed application and use cases of BIM in transportation infrastructure, particularly transportation asset management, maintenance, inspection, and data management. Costin et al. found a large number of current research papers on bridge and road infrastructure. Within bridge management, Bridge Information Modeling (BrIM) approaches cover the life-cycle aspects of bridges: planning, design, construction, and maintenance. Considering the use of BIM in pavement design and planning implementation to date has been challenging, and the limitation of the industry foundation classes (IFC) data model and a lack of standards was referred to in several studies. A review of the literature shows that data exchange between infrastructure and BIM still is lacking a standardized neutral exchange format. Thus, ontologies and Semantic Web technologies are an emerging trend to enrich the information with BIM and to increase interoperability among stakeholders. Bazán et al. (2020) examined road and bridge infrastructure management, and presented a set of use cases for optimized and sustainable infrastructure management. In the design phase, BIM is used to a certain extent, but the lack of semantics for geometric elements hinders the efficient use of BIM in successive phases. In use cases, Bazán et al. (2020) noted that due to complexity, lack of appropriate software applications, and missing standardized information exchanges, BIM is not yet implemented widely for maintenance of infrastructure.In the design and planning phase, the parametric and algorithmic modeling of bridges was considered by Girardet and Boton (2021). They improved the time for generating a 3D BIM model of a bridge is reduced, and the geometric accuracy. Sacks et al. (2018) proposed a BIM-integrated bridge inspection system. Their approach mainly involves capturing and collecting data for bridge inspection using BIM with IFC standards. With the recommended information delivery manual (IDM) as the documentation medium for data exchange and the developed model view definition (MVD) based on the IDM, the required bridge inspection data can be defined and validated using IFC entities and their spatial and semantic relationships.Vignali et al. (2021) presented a case study of the application of BIM for the design of road segments, including the utilization of three-dimensional (3D) parametrized modeling. The modeling approach is applied to several different road segment types, such as roundabouts, bicycle lanes, or railway underpasses. In practice, these different elements need to be modeled in various software applications. Collaboration between planners when designing these shared spaces in transportation infrastructure, e.g., using the IFC data model, still is challenging due to missing interoperability. BIM for modeling road pavement was investigated by (Tang et al. 2020). They integrated the pavement structural analysis of the asphalt surface layer into the design process using a framework developed in Dynamo. They stated that BIM enables the possibility to add more analysis indicators and to consider the whole pavement layer, which can support the design more efficiently. The use of BIM and IFC also was investigated for planning future traffic technologies such as intelligent transportation systems in order to provide a formal description of these systems for road networks and to use the resulting IFC models for traffic simulations (Mirboland and Smarsly 2021). For asset management, a semantic description is essential for these future BIM applications, and formalization and standardization of these road and bridge semantics are being discussed further.Several existing approaches have been proposed for BIM-based asset management of transportation infrastructure. Because most infrastructure assets are existing objects, there is considerable potential for the integration of BIM into AM processes (Pocock et al. 2014; Chancey et al. 2017). Realistic representation of objects significantly can improve the quality of decisions regarding maintenance measures. In addition, BIM enables an improvement of the existing methods of data acquisition and storage, through seamless information exchange over the entire life cycle of the road infrastructure. Currently, approaches are in the form of BIM pilot projects (Jackson 2018), primarily considering planning and execution phases and mimicking a “digital construction site” (Parlikad and Catton 2018). A particular issue noted is a lack of uniform specifications for data exchange between partners. A general description of processes, data structures, and flows within national roads authority (NRAs) does not yet exist in a comprehensive and usable form for the application of the BIM method. This hampers the use of novel data streams via technologies such as unmanned aerial vehicles (UAVs), e.g., drones and photogrammetry (Hajdin et al. 2018).Semantic Web and Linked Building DataThe Semantic Web integrates different internet resources and adds a semantic layer describing the resource with additional metadata (Berners-Lee 2006). Therefore, ontologies are used to define a schema for the interpretation of metadata from these resources (Staab and Studer 2009). Ontologies determine the meaning of the metadata instances for a generic concept of machine-readable data access. Furthermore, the semantic layer can be utilized to link data and resources across the World Wide Web using Semantic Web technologies (Domingue 2011). This linkage is called linked data (Berners-Lee 2006). The Semantic Web technology stack consists of a layered set of technologies developed for use in Semantic Web environments and applications (Domingue 2011). Identifiers according to the uniform resource identifier (URI) syntax form the basis of each resource described. The exchange of data is performed using the Resource Description Framework (RDF) (Cyganiak et al. 2014) and the extended RDF schema (RDFS). Semantic Web data are structured using ontologies defined in the Web Ontology Language (OWL) (Motik et al. 2012). These data can be serialized into, e.g., RDF/Extensible Markup Language (XML) (Gandon and Schreiber 2014), Turtle (Beckett et al. 2014), or other syntaxes. To query data in the structured format, the SPARQL Protocol and RDF Query Language (SPARQL) was developed, which as a query layer can process all defined vocabularies and search the data record on the basis of triple pattern matching (Harris and Seaborne 2013). For validation of RDF-based data, the Shapes and Constraint Language (SHACL) has been developed and extended (Knublauch and Kontokostas 2017). Linked data and Semantic Web technologies increasingly are being researched and applied in the architecture, engineering, construction, owner, and operation (AECOO) sector (Pauwels et al. 2022). The development of ifcOWL as an exact representation of the IFC STEP formats in an OWL ontology can be seen as a starting point for accessing building data in a Semantic Web context (Beetz et al. 2009). Bonduel et al. (2018) implemented the conversion from IFC STEP to several construction ontologies as the IFC to Linked Building Data converter (IFC2LBD). Moreover, a central ontology for representing the topology of a building or a structure was proposed by Rasmussen et al. (2017) as the Building Topology Ontology (BOT). It represents the site, buildings, building stories, zones, building elements, and the spatial relationship between these entities. This ontology fulfills the minimal requirements to represent building data and to align with other ontologies that extend the elements with domain-specific information (Schneider 2019).Ontologies for the Transportation Infrastructure DomainThe number of published domain-specific ontologies for transportation infrastructure has increased in the last few years. There are several domain ontologies that consider the inspection and maintenance of bridges. Based on the BOT, a Bridge Topology Ontology (BROT) as a core ontology for representing a bridge was developed (Hamdan and Scherer 2020). It associates four additional ontologies as an extension for modeling the bridge type, bridge components, and building materials. With the Damage Topology Ontology (DOT) (Hamdan et al. 2019), the damage representation can be linked to bridge components defined in other ontologies, e.g., BROT or BOT. The DOT provides an ontological basis for bridge inspection and damage detection. The Bridge Maintenance Ontology (BrMontology) is defined with a focus on condition assessment and maintenance planning without alignment of existing ontologies (Ren et al. 2019). It consists of classes considering the structure, material, hazards, and other events. Another ontology focusing on Concrete Bridge Rehabilitation Project Management (CBRPMO) was developed to model domain information during the rehabilitation procedures (Wu et al. 2021). For the area of road AM, a variety of ontology developments mainly has focused on the operation and maintenance phase (Lei et al. 2021). For example, an important outcome of the European research project INTERLINK was the development of the European Road Object Type Library (EUROTL) (Luiten et al. 2018). This Object Type Library is available as the EUROTL ontology, which provides classes and properties for modeling infrastructure entities, actors, and operational activities and reuses concepts of the well-established Provenance Ontology Family (PROV) (W3C 2013). An ontological basis for the management of infrastructure information related to the requirements of European Road Directors has been provided. Within the scope of this research project, several test ontologies were developed which are oriented toward EUROTL and correspond to the respective national standards. One of these ontologies represents the German standard for bridge condition assessment, which is referred to in this work as the ASBING ontology.Information ContainersInformation management using BIM is specified in ISO 19650-1 (ISO 2018). To exchange information between project stakeholders, information containers can be used. According to DIN SPEC 91391-2 (DIN 2019), an information container can provide metadata along with arbitrary information content. Thus, these standards and specifications do not provide the container definition and syntax itself. Therefore, in recent years research has investigated how to exchange interrelated BIM data in containers. Some approaches are the Dutch standardized COINS container (Hoeber and Alsem 2016), the multimodel or BIM-LV-container (Fuchs and Scherer 2017), and the most recent Information Container for Linked Document Delivery (ICDD) (ISO 2020). Extensions of the defined openCDE interface according to DIN SPEC 91391-2 (DIN 2019) for specific container types, for example, using the ICDD (Höltgen et al. 2021), were analyzed, conceptualized, and implemented. The ICDD container utilizes Semantic Web technologies to provide metadata about the contained data, and the linking between these documents delivered within. The application of Semantic Web technologies enables an extendable structure of the container schema, unique identification of objects, and independent localization of data for integration of distributed data (Hoeber and Alsem 2016). The data schema of this information container relies on three main ontologies for the structure of the container, the included linksets, and further specialized human-interpretable link types based on the Semantic Web technologies RDF and OWL (ISO 2020). The standard also provides a data validation approach using SHACL. The information container can host several types of documents internally or externally, all of which are identified uniquely using URIs. Several approaches utilize the ICDD for validation (Hagedorn and König 2021) or for the integration into a common data environment (CDE) (Karlapudi et al. 2021).MethodologyFollowing the definition of the research questions, the research process consisted of three main steps (Fig. 1). The methodology used in this paper follows the analysis, concept, implementation, and proof-of-concept approach. Regarding the proposed research questions in this paper, the BIM-enabled infrastructure asset management approach is supported using information containers on a web-based platform. Therefore, in Step 1 the process analysis of transportation asset operation is conducted. The process analysis identifies the general work process of recurring engineering and maintenance tasks in the operation of transportation infrastructure modeled as an abstracted workflow. To define data exchange requirements, the task- and process-driven data flow is analyzed, focusing on the implementation and use of standardized information containers. Following these requirements, a web-based platform for working with the standardized ICDD containers is conceptualized. The resulting concept for the ICDD platform employs the data management to realize the identified work processes and data flows.The layered system architecture of the platform is presented and the software implementation is conducted in Step 2. This software architecture is a common architecture pattern, and was used in this paper to provide an extendable and reusable implementation of the individual components. The resulting implementation enables the use of ICDD for data acquisition, data exchange, and data integration with the existing database.In the last step, Step 3, two use cases relying on recurring routine engineer work for bridge and road infrastructure were demonstrated, validated, and discussed. The first use case incorporated a visual bridge inspection in which data were acquired on-site and transferred into the respective information container. The second use case comprised a pavement maintenance plan, for which information about the condition of the existing pavement was loaded from an external AMS and processed within the container for the maintenance plan. The use cases are discussed afterward.Process Analysis and Concept DevelopmentThe overview of the general abstracted work process and data flow in infrastructure asset management in Fig. 2 takes into account four major aspects: the stakeholders, the processes, the data, and the encompassing web-based platform as the data management solution. Asset management tasks usually are executed by a range of different stakeholders. These tasks can be presented in an abstracted work process considering the technical and data aspects. The retrieved and generated data can be identified for each process. The integration of stakeholders, executed processes and generated data in Fig. 2 is defined herein as the general workflow. A web-based platform can enable data management integrated with existing asset management databases. This section describes the general work process, the task- and process-driven data flow, and the concept of an ICDD platform for infrastructure asset management.Identifying the General Workflow in Infrastructure Asset ManagementThe traffic authority is responsible for the AM to ensure safe and reliable infrastructure by employing the management group and the operational engineering group. The main technical tasks in infrastructure asset management focus on inspection and on planning and executing maintenance based on the condition of the infrastructure. The abstracted work process is performed by several stakeholders (Fig. 2). First, the manager plans tasks based on the available infrastructure data. Engineers provide the technical requirements and data needs of the planned task related to national standards. Then the commissioning can be prepared. After contractors receive the order, they can execute the work and deliver the requested or updated data back to the operating engineers. The engineers verify the quality of the work executed and data delivered against the requirements, and finally approve the work if the requirements are fulfilled. The data generated during the process are provided or exchanged in the form of information containers.Defining Task- and Process-Driven Data for Information ContainersAn information container can be used to bundle and link different types of data and documents (ISO 2018). The generic structure of the containers consists of an index.rdf file and three predefined folders—Ontology resources, Payload documents, and Payload triples—that are distributed in a ZIP archive (ISO 2020). The index.rdf file describes the container and its contents, including the respective metadata. The ontology resources folder is used to store employed ontologies. The mandatory container ontologies Container.rdf, Linkset.rdf, and ExtendedLinkset.rdf according to ISO 21597-1 (ISO 2020), and ExtendedDocument.rdf according to (2021) must be included. These ontologies provide the class and property definitions to specify and link documents within the container. Depending on the scope of the container, utilized domain ontologies also are declared in this folder. The Payload documents folder is used to store all defined documents, e.g., 3D-object models, images, reports, and so forth. The Payload triples folder is used to store the links collected in the related linkset file and to store all RDF-based data sets, which are instances of domain ontologies.In the work process depicted in Fig. 2, the data and the connection between data and process are shown as containers and dashed lines, respectively. The manager can access and query the available infrastructure data from existing relational databases connected to the 3D-object model for task planning inside the Asset information container. The planning data for a maintenance task can be collected directly in the Asset information container by the asset managers. The data exchange points and the related data scope are defined as a part of the task requirements. An information delivery manual based on ISO 29481-1 (ISO 2016) can be used. The level of detail of the data exchanged at each point must be defined. In this paper, business process model and notation (BPMN) is used to formalize the detailed process map for the use cases and to specify the content of the container for the exchange. The content of the container then is described in terms of the data scope, data type, and the connection between the data or documents. In addition to the IDM, the necessary data and documents, i.e., the infrastructure and technical specifications for executing the task, are provided in a Requirement container for the commissioning preparation. This Requirement container is created once throughout the workflow and is supplemented and handed over to stakeholders in each step. When the container is handed over to further workflows steps, the status and suitability of the container are changed accordingly. Thus, the whole workflow is documented within this container, and the history of changes and details of the previous content can be tracked. In addition, the contractor’s works also are recorded in this container as requested documents. The resulting Requirement container will be handed over via the ICDD platform to the contractor by receiving the order.The contractor uses the Requirement container for retrieving the technical documentation of the infrastructure and as a template for capturing the requested data. Adding the requested information from the contracted task into the Requirement container, this container is transitioned into a Delivery container. This transition implies that the contractor modifies the Requirement container with all information included as a template container and adds each piece of the required information into the container during the work process. After successfully integrating the data, this container can be delivered to the operating engineers for approval. The same container is reused, and thus is only annotated with metadata (suitability and status) to embody the data flow in the overall workflow. After delivering the required documents and information into the container, it can be approved by the receiving operating engineers. Certain data can be selected and integrated into the existing relational database, which can be used for the review of the infrastructure information and as a basis for future maintenance tasks. This paper introduces two use cases to demonstrate the application of the Asset information container for planning maintenance tasks and to present the data exchange using Requirement container and Delivery container for a bridge inspection. The use cases were realized by providing an ICDD platform implementation.Utilizing an ICDD Platform for Asset Information ManagementTo facilitate sharing, exchange, and visualization of data between different stakeholders in information containers, an information system called ICDD Infrastructure Asset Management Platform is implemented. To track the work process for a container concerning information management, the platform enables setting the container status according to the ISO 19650-1 (ISO 2018) status codes and workflows. The user access to the container can be determined depending on the relationship between the logged-in user role and the project. Functions for visualization, creation, manipulation, and deletion of the container content are provided. Data querying within the container and data integration from an existing relational database is implemented. A detailed description of the system architecture of the proposed platform is presented in the following section.System Architecture of the ICDD PlatformThe system architecture of the asset management platform is split into five layers with different functional scopes (Fig. 3). The foundation is the data layer consisting of two data stores. The user and project database is conceptualized as a relational database storing users, projects, and the relation of projects to a container with additional metadata. The container storage is file-based storage, in which container payload and RDF-based data are deposited. The data from the container storage is processed using the IIB.ICDD framework on the data access layer. This library provides the container payload data at runtime and RDF-based data in in-memory triple stores for accessing, transacting, and querying data inside containers. The framework handles ICDD containers that are valid according to ISO 21597-1 (ISO 2020). The transactions are referred back to the file-based containers using writer classes for disposing of the in-memory triple stores and releasing memory resources.The service layer provides user-related data to implement a token-based authorization in the service layer. These tokens can be used by any web-based user interface. Together with the project service, the access rights to projects and containers are assigned. Projects have members, and these members have access to the containers included in the projects, which in turn means that each container is allocated to a project.On the service layer, access to projects, containers, container metadata, contents of the container (as payload or RDF data sets), linksets, and the querying and validating services are provided. At the information management level, the services provide project and container management according to ISO 19650-1 (ISO 2018). The implementation of this standard contributes to the successful implementation of the asset information model (AIM). Thus, each container receives a status from the status codes (work in progress, shared, published, and archived) of the standard. The archived status encompasses the journal of changes in the container but does not allow any change of information. However, each archived container can be updated to the next version, in which the status is work in progress, and changes can be made in this version. The versions of containers are linked to each other using the previous version predicate of the container description defined by ISO 21597-1 (ISO 2020). The container claims additional metadata as a set of recipients inherited from the project members, a revision number, and suitability. The suitability is adapted from PAS 1192-2:2013 (BSI 2013) and indicates the use or type of information. A set of predefined labels from PAS 1192-2:2013 is implemented into the platform. These labels include coordination, information, internal review, construction approval, manufacture, Project Information Model (PIM) authorization, AIM authorization, costing, tender, contractor design, manufacture procurement, construction, AM inspection, and AM maintenance. The suitability is extended with another label, Requirements container, which specifies a container for modeling exchange information requirements. Therein, requested documents can be added into the payload, setting the property requested to true for this special document. These documents do not have a file stored in the payload documents folder, but need to be filled by the receiving stakeholder after the suitability has changed. When the suitability has changed, documents can be supplied for the placeholder file metadata. The property requested then is set to false if the uploaded file matches the requested metadata for mime-type and file extension. With this concept, links between payload documents also can be defined in the requirements container so that the files uploaded afterward are linked directly, as required by a client.On the business logic layer of the system architecture, the data flow from the data access layer to the representation layer is managed, and input from the user interfaces in the representation layer is processed. Therefore, processors are implemented to fulfill certain tasks with input data and user interactions and provide the application programming interface (API) and the user interface with the required data. The processors access and distribute data to clients throughout the services and the data access layer. Every processor is related to the Container processor because all of them retrieve or send container-related data. Additionally, functionalities are provided for particular payload contents in containers. One of these functions is the processing of IFC-based building models. Building models first are converted into a WebGL format to be shown within the web user interface afterward using the xBIM Web UI module. In parallel, the IFC model is processed to retrieve the spatial hierarchy of the IFC model and the property sets from the IFC objects to be accessible for the user. Moreover, IFC models can be converted into RDF-based data sets within the platform using the IFCtoLBD converter provided by Bonduel et al. (2018).To establish the connectivity with existing AMS, a connector to relational database systems was designed and integrated. This connector enables the access of AM data from other systems within the container context, creates links between IFC models, documents, and AM data, and performs queries on an integrated data set. To enable the exchange of containers between stakeholders during operation workflows, data from an existing asset management system are selected from the external systems based on the defined mapping. These are converted into RDF to be queryable within the container context, and then stored in the container for exchange. To realize this connector, first, the container schema is supplemented with a subtyping of the ct:ExternalDocument class from ISO 21597-1 (ISO 2020). The specialized class is denoted exdoc:ExternalDatabase and documented in an extension ontology for ICDD containers ( 2021). The external database class provides the necessary information to connect a container to a MSSQL or other database specifying a connection string. To retrieve data from the relational database, the R2RML mapping language (Das et al. 2012) is utilized. R2RML is well researched and defines either automatically generated basic mapping algorithms or creates user-defined mappings (Hazber et al. 2016). In the platform concept, both basic mapping of the full database and the user-defined mappings are envisaged. Therefore, the exdoc:ExternalDatabase class can have zero or one mapping file attached using the databaseMapping object, which references a mapping file stored inside the container as a ct:Document individual and provided in a RDF-based syntax. If no mapping file is defined, basic mapping is executed. In either case, the resulting generated RDF triples are stored within a payload triples file and can be accessed using SPARQL or SHACL. User-defined R2RML mappings are defined according to the W3C recommendation (Das et al. 2012). The R2RML-based retrieval of RDF data from relational databases in the platform context is performed using the r2rml4net NuGet package.On the presentation layer, two possible user interfaces are implemented. A representational state transfer (REST) API is provided, which conforms to the DIN SPEC 91391-2 (DIN 2019) specifications. Höltgen et al. (2021) demonstrated the API for the management of ICDD containers, contents, and linksets. Moreover, the web user interface provides a graphical user interface for all provided functionalities in the business layer and service layer. Additionally, the web user interface is equipped with an IFC viewer that becomes the starting point for creating queries in the container related to selected IFC objects without the user needing much prior knowledge of SPARQL. Both representations are secured using token-based authentication and access the same data sources so that users can work in the same project or container in several client applications.Overall, the layered architecture allows for extensibility, which enables different clients and front ends to be combined with each other on the basis of the REST API provided, and thus to be usable from any client software or even different AMS.Use CasesTo visualize, exchange, and integrate AM data for inspection and maintenance based on the proposed ICDD platform, this section introduces two use cases. The first use case simulates a visual bridge inspection by an external contractor. The focus is on creating, collecting, and exchanging the relevant data between the external and internal stakeholders using the information container. The second use case demonstrates a pavement maintenance plan based on the current condition of road sections. The pavement condition data are stored in an existing asset management database and retrieved to create a maintenance plan by the asset manager. Using the information container, the data from the relational database are integrated and linked with the object model of the asset. Based on SPARQL queries, the asset manager determines the road sections for the maintenance plan by querying the critical condition within the container.Use Case 1: Visual Bridge InspectionIn this use case, the condition of bridge elements is assessed using the German guideline ASBING. The detected damage of an element is recorded with an image related to the respective IFC object. An IDM is defined for data exchange between different stakeholders in the inspection process. Fig. 4 shows a BPMN diagram. This formalization of processes within the scope of IDM particularly supports understanding complex workflows of BIM model exchanges between several stakeholders. The process typically contains detailed tasks for each stakeholder and the respective data exchange points. In this case, the process has just two data exchanges using the information container. The container content must be defined as an exchange requirements model for each data exchange. The operation engineer prepares the commissioning of the inspection with the necessary documents concerning the bridge structure. This information is collected in the ER1_requirement_container. The contractor uses this container as a template, and changes its name to ER2_delivery_container with status “Work in progress” and changes the suitability from “Suitable for requirement” to “Suitable for inspection.” These changes can be realized simply on the ICDD platform. Then, the inspection information as required can be added into the ER2_delivery_container. As a result, the ER2_delivery_container with the aggregated data is delivered. The operation engineer verifies the deliverables against the technical and exchange requirements. The verified information can be filtered by the engineer for specific tasks, e.g., maintenance planning.The configuration of the requirement container and the delivery container related to the use case is displayed in Fig. 5. Regarding the type and the usage of the data, the documents are stored in the three container folders as defined in the ICDD standard. In addition to the ontologies required by ISO 21597 and the ICDD platform, the domain-specific ontologies also are considered in the folder Ontology resources for executing RDFS reasoning in the container to further process the data with SPARQL and SHACL. For the use case, the container employs three existing domain ontologies for capturing semantic data (Fig. 6).In addition to the structure model BridgeModel.ifc for a BIM-enabled inspection, the operation engineer provides the predefined document templates [REQ]DamagePlacement.ifc and [REQ]DamageImage.jpg with the requested attribute as placeholders in ER1_requirement_container. Using the requested attribute, the information requirements can be defined digitally, fulfilling the process of information delivery and information approval both in practice and for the ideal process according to ISO 19650. The contractor can use the container template to deliver information and complete the ER2_delivery_container. The required documents for the damage description can be uploaded into the container. If more damages were captured, all related documents should be uploaded regarding the requirements of the defined template files in the Payload documents folder.Semantic data are captured in Turtle files in the Payloads triples folder according to the defined domain ontologies. Links between IFC elements, images, and semantic data are specified in RDF files as so-called linksets. All linkset files included in the two containers are summarized in Fig. 7, providing the link description and the document containing the linked elements. The link description clarifies which data are associated with each other. These data form the connective elements that are contained as components or instances in the aforementioned documents. They then are linked using the link type Directed1toNLink that defines a direction via exactly one from-link element and a set of to-link elements defined by ISO 21597-2.Use Case 2: Pavement Maintenance PlanThis use case demonstrates how decision-making can be supported with the help of cross-domain information containers. Steps 1–7 in Fig. 8 show the establishment of an information container with available data from an existing asset management database linked to road object models and the generation of an appropriate maintenance plan. Step 1 defines the scope of the generated information container. For example, an asset manager can use the information container to request the current condition of pavement road sections linked to the respective elements in a 3D model. Based on the result, they can plan the maintenance based on the geometry of the affected elements and the historical pavement data from the database considering time planning and cost estimation of the maintenance work to be executed.A relational database RoadInformationDatabase (RIDatabase) is set up consisting of seven database tables based on the German NWSIB database for state-specific road maintenance. It contains four main information components: road section data, construction project data in connection with the road network information, inspection data in connection with the condition information, and the maintenance plan in connection with the planned pavement works. The pavement condition assessment with four key indicators (friction, evenness, rut depth, and crack width) available from the Condition table (Fig. 9) is the focused of Step 2. These condition data are recorded as instances of an appropriate domain ontology. In the same manner, the data of the maintenance plan must be acquired using a domain ontology regarding the database table the Maintenanceplan table of the RIDatabase. Considering both aspects, the EUROTL ontology is employed to capture the numerical values of the condition and the planned maintenance data as Step 3. In Step 4, the mapping rules for exporting data between relational databases and RDF-based payload triples are defined in the R2RML mapping language. The alignment between attributes of the Condition table and the classes and properties of the EUROTL ontology is defined as shown in Fig. 9.To generate the container for this use case in Step 5, all contents are provided in Fig. 10. When the relational database is registered as the payload document RIDatabase using the extdoc:ExternalDatabaseLink class and the mapping rule is declared as RIDatabase.mappings.ttl and referred to the database link, the condition data can be integrated automatically into the container as payload triples RIDatabase.instances.ttl using the defined mapping. The connection of lane sections from the database to respective IFC elements is created and stored in the linkset file Ls_ifc_RIDatabase.rdf. To query and filter the relevant data to create the maintenance plan, the SPARQL query language can be used, and queries can be executed in the platform on the aggregated container data as Step 6. By reviewing the query result, the asset manager generates the maintenance program, for example, for the queried road sections in critical surface conditions, and incorporates the respective data into the information container in Step 7. These data are collected in the Maintenance.instances.ttl file. These instances of the planned program also are linked with the related IFC elements. The links then are recorded in the linkset file Ls_ifc_maintenance.rdf. As the last step, Step 8, the asset manager can import the relevant result back to the original database, an approach for which was introduced by Liu et al. (2022) and which will be implemented into the platform in the future.ValidationUsing the web user interface of the ICDD platform, stakeholders can create, edit, view, and delete container contents using the applied functions as shown in Fig. 11. The user interface includes three main components for the interaction between the user and the system. In the Explorer component, the structure of the container is presented. The document content is represented in the Content component, e.g., the elements and structure of an IFC model, images, or RDF-based triples. Domain ontologies, RDF-based triples, documents, and linkset files can be added to the container from the navigation bar. SPARQL queries can be executed on the entire container data. The output of the results is provided as a table or in the standardized SPARQL Query Results JSON Format. In the Properties/IFC viewer component, the user can switch between the metadata of documents and the integrated IFC viewer, which provides loading multiple models combined into a federated model and selecting elements directly per partial model.Use Case 1: Visual Bridge InspectionFor the BIM-enabled bridge visual inspection, the IFC model of a concrete road bridge is used that contains two bearing axes built with a foundation and abutment walls. The structure and traffic load are carried by a grid consisting of two main beams and three cross beams. The bridge substructure elements described previously are considered to demonstrate the visual bridge inspection use case. The topological representations of structure elements are described using BOT as shown in Fig. 12. The whole bridge structure was typed both as a bot:Building and a brot:Bridge to be able to use the generic topology hierarchy from BOT and to provide a more specific classification of the structure using BROT explicitly. Further classifications of BOT individuals can be inferred through the alignment between BOT and BROT as provided by Hamdan and Scherer (2020). The metadata of the inspection with the execution year and the assigned contractor are collected as instances of classes from the ontologies EUROTL and PROV. The links defined in the use case description are created between these instances and the elements of the IFC model.The contractor collects the condition of the nine elements defined as BOT instances. Based on the German ASBING ontology, each element possesses three condition grades considering traffic safety, stability, and durability on a scale from 0 (no influence) to 4 (renewal is to be initiated). In this inspection use case, all elements of the substructure are assessed as having no influence on traffic safety and Grade 1 or 2 for stability and durability without the relevant need of maintenance, until the main beam at the south side has damage such as concrete cracking or spalling. It then receives a Grade 3 for both conditions and needs to be maintained shortly. The respective damage will be captured within the IFC model DamagePlacement.ifc for the localization and in an image DamageImage.jpg for visual assessment afterward. The IFC elements that are in a Stability grade 3 state can be selected as shown in the IFC viewer in Fig. 11 and linked to create a maintenance plan. Moreover, the model with the damage location is shown in the IFC viewer.Use Case 2: Pavement Maintenance PlanFor planning the pavement maintenance based on the BIM model, a limited road section with a length of approximately 1,000 m was modeled. This road model contains only one direction and one lane without a terrain model. The underlying alignment of the road was built from an alignment element straight line. Because the current export of ifcAlignment in IFC 4×3 is not provided in the modeling application, the model was exported in IFC 4 without alignment elements. The road cross section consists of four layers: asphalt surface course, asphalt base course, base layer, and antifrost layer. In addition, a virtual layer over the pavement structure in the longitudinal direction was defined with element lengths of 100 m as a homogeneous section for the condition assessment.The condition data for the 100 m homogeneous sections are recorded in the RIDatabase with the numerical values of friction, evenness, rut depth, and crack width. The database is connected to the information container as a payload document (Fig. 13). The mapping rules RIDatabase.mappings.ttl are defined using EUROTL to export the data from the database into the container. Fig. 14 shows an example of R2RML mapping rules for converting the data of column crackwidth from condition table of the RIdatabase into instances of the class eurotl:condition with the related pavement section as a spatial object. Fig. 15 shows the imported data set of one pavement section represented in triples as a result. The whole ontological data are captured as triples in the RIDatabase.instances.ttl, which then are linked with the homogeneous section of the IFC model.Combined with the IFC model, the stakeholder can select the pavement sections with critical values of the condition indicators. Based on the query result, the pavement sections requiring maintenance can be determined. The recommended work, year, and cost are recorded in the maintenance plan linked to the sections. Fig. 16 demonstrates a query to filter the sections with a value of crack width of more than 3 mm through the SPARQL query. As a result, the two end sections between 800 and 1,000 m are returned and highlighted in the IFC model (Fig. 13). After a review of the IFC model, the maintenance plan can be made for the related sections with the required information using EUROTL.DiscussionWhen using BIM for infrastructure operations, practical engineering challenges must be overcome. BIM still is not adopted widely in the transportation infrastructure domain. BIM models mostly are created during the design and construction phase but are not carried forward to the operational phase to maintain the existing physical assets (Bazán et al. 2020). Thus, it is demanding to bridge the gap between current engineering practice and the optimal BIM usage over the asset life cycle. Moreover, it is difficult to establish technologies such as BIM model servers and RDF triple stores adjacent to existing organizational AMS, even if using BIM delivers more added value for the organization. On the other hand, it is challenging to establish a relation between the vast amounts of existing data, especially road network data, the BIM model, and related data synchronously. Therefore, the use of information containers based on Semantic Web technologies can provide a solution for the temporary storage of the BIM model and allow the linkage with further data as a first step. The asset manager can create information containers regarding the defined workflows without influencing the existing AMS. After the advanced system for combining BIM and interconnected RDF data in a RDF triple store is established, the information container can be used for the data exchange and data visualization to overcome the difficulty of accessing the database as an external stakeholder.Therefore, the most important benefit of the information container is to provide an open standardized data exchange format, in which data content also can be verified using the SHACL rule language. The data exchange and the demand for a standardized data model still is one important impediment to BIM adoption in transportation infrastructure (Costin et al. 2018). The presented ICDD platform facilitates the creation, manipulation, and exchange of ICDD-conform information containers between different stakeholders in a project or asset context. Relevant standards have been incorporated into the platform. The generic data structure of the information container makes the collection and linkage of extended information and documents with different types outside the BIM model possible.Even so, the generic concept requires a mapping of original data structures into ontologies (as shown with the IFC model converted into LBD). Beyond a certain point, one single ontology is not able to cover all aspects of a data model, so alignments between ontologies need to be defined and terminology has to be added. Although a large number of central, general ontologies for AEC have been established, there is a lack of a comprehensive overview of existing ontologies, their application, and interfaces with other ontologies.Based on the implemented ICDD concept, information containers also can be used for different life-cycle phases. Especially for the construction phase, in which the BIM model is established and the amount of information and documents increases intensively, providing a structured data package without restrictions on the data type can improve the digitization of the construction documentation. At the end of a construction project, the as-built model with related information and documents can be handed over as a whole data package, which also simplifies the data integration into the AMS. However, the use of ICDD information containers as a data structure and an exchange medium is only an intermediate step for overcoming the file-based data exchange issues and establishing the vision of a fully remote access of linked building data in triple stores, as proposed by Pauwels et al. (2022). The platform development prototype presented here offers the possibility that users can generate queries and rules and execute these on containers for their purposes. The creation of SPARQL queries on LBD requires knowledge of the syntax and semantics of the query language on the one hand and the queried data set on the other hand. This knowledge cannot be assumed for application in the engineering context. Query templates and rule sets must be prepared accordingly and filled with parameters, e.g., from the IFC model, so that efficient use is possible. Although the platform already allows the storage and import/export of SPARQL queries, as well as the creation of SHACL shapes in a knowledge base, the major challenge is actually creating this content according to requirements for the application domains.Using the developed solution, aggregated data from different domains also can be made usable for other cross-domain use cases, e.g., to perform combined maintenance for a road section and several bridges on the network together in one or more federated containers. This is especially useful to align the competencies and responsibilities of different planners from different disciplines. Moreover, this paper shows how the reuse of established domain ontologies can be beneficial for AM and also can provide a common basis for cross-country and cross-language AM. In addition, condition assessment rule sets for asset information containers can be developed using the SHACL rule language. By combining rules with regularly updated data sets, e.g., through inspections or integration of data from existing relational databases, the presented ICDD platform can function as an up-to-date dashboard for monitoring infrastructure conditions, as shown in Use case 2.ConclusionThis paper shows the use of information containers, Semantic Web technologies, and domain-related ontologies as schemas in a web-based platform for infrastructure asset management. A concept and a methodical approach were developed, and a web-based platform was implemented, demonstrated, and validated in two different use cases. The investigated use cases are related to common activities of visual bridge inspection and road pavement maintenance planning. Existing ontologies from the AM domain were reviewed and used in ICDD containers in the proposed ICDD platform. Existing AMS and their relational databases were considered in the approach and connected to the information container. Thus, the information container can access and evaluate data from the database within a container and can be linked to IFC models and instances of ontology data to provide a cross-domain queryable AIM. SPARQL queries can be executed on an aggregated data set of the container in an in-memory triple store. In the use cases, these SPARQL queries were utilized to retrieve road sections or bridge elements that relate to areas of interest for inspection or maintenance (e.g., damaged sections). The results of this paper answer the aforementioned research questions.Information containers are suitable for describing, updating, and processing data across the asset life cycle in a flexible and efficient way. The use of information containers for the management of PIM and AIM is predetermined by standardization (ISO 2018). Information containers support information delivery according to IDM. They also entail the systematic assembling, structuring, and exchange of data, which facilitates data management for transportation infrastructure operational activities combined with the BIM model. In this research, ICDD containers according to ISO 21597-1 (ISO 2020) were used to maintain IFC models and accompanying heterogeneous asset data, which were linked together inside these containers using Semantic Web technologies. These technologies enable the container for extension and usage of dynamic data schemes with domain-specific ontologies.To examine how standardized information containers need to be prepared, a process analysis and an analysis of the data exchange during infrastructure maintenance operations were conducted. As a result, a workflow including stakeholders, processes, and data flow was described in this paper. In use cases, these processes were modeled as BPMN diagrams according to the IDM framework. The respective data exchange points in the workflow were identified. Container templates are provided for the handovers in the two use cases. These container templates also include requirements, which can be configured as placeholder files for the receiving stakeholders. The suitability and status of the containers are declared for the process steps and are tracked throughout the workflow. These annotations are used to compose Asset information containers, Requirements containers, and Delivery containers.Considering the challenge of connecting existing asset management systems to BIM models, we demonstrated that an existing database can be embedded in a container. An extension of the container schema is presented to allow setting up the connection to a relational database. The content of the database can be integrated into the container using the R2RML specification for converting all database entries or a subset of database entries defined by R2RML mappings. These mappings can convert data from the database into RDF data using domain-specific ontologies so that these entries can be linked to the IFC model afterward. The import of specific data from the database was demonstrated in Use case 2. In addition, the data inside the information containers can be accessed in the scope of the original AMS, which is possible in this approach through the implemented REST API. The integration of RDF data into existing SQL databases for a bidirectional data exchange was investigated by Liu et al. (2022), and will be considered for future extensions of the presented platform.A system architecture was designed and implemented into a web-based platform in this paper. The system architecture comprises components for the general platform implementation (i.e., user authentication, access rights, project management) and components to handle the information containers. The core of the implementation relies on the ICDD framework, which also is provided decoupled from the whole platform. The ICDD framework implements functions to create, read, update, and export ICDD containers. Moreover, it enables further processing of IFC models, handling of SHACL and SPARQL requests, and providing the R2RML processor for the integration of external data from relational databases into the container. Altogether, these functions are provided behind a unified service layer that can be accessed via the REST API and the web user interface in the presentation layer. This implemented web-based platform enables executing the identified workflow for the maintenance of transportation infrastructures using ICDD containers.Nevertheless, several improvements of the presented system for AM processes are conceivable and will lead to future research directions. Firstly, the role of standardized domain ontologies was highlighted in this paper, but efforts need to be made in particular to find and align existing ontologies and provide a high proportion of semantic information in a language-independent way. For AM, the connection to geometric information is helpful for linear structures such as roads and bridge structures. Thus, research needs to investigate how to better locate information both semantically and geographically using geometric representation and preserve these relationships between geometry and semantics for all stakeholders. In addition, maintenance processes in AM often require the updating of geometric representations or the placement of geometric objects, such as drill cores or other test bodies, which can be automated in the future. Especially for the use of different domain models in information containers, an automatic generation of the relationships between entities in these domain-specific models is necessary and needs to be researched further to increase the automation for this. Finally, the connection to sensors and other measuring devices that produce regular time-dependent data must be made usable in information containers automatically. Research in this direction, particularly concerning infrastructure digital twins, can lead to several interesting topics for the further development of this concept.Data Availability StatementSome or all data, models, or code generated or used during the study are available in a repository or online in accordance with funder data retention policies. The data generated for this paper is provided by Hagedorn et al. (2022).AcknowledgmentsThe authors gratefully acknowledge CEDR (Conference of European Directors of Roads) and FFG (Austrian Research Promotion Agency) for funding this research. The authors thank the consortia of the projects AMSFree and BIM4AMS for their collaboration on the research of BIM-based AM concepts for roads and bridges.References AASHTO. 2013. Transportation asset management guide: A focus on implementation. Washington, DC: AASHTO. Aktan, A. E., I. Bartoli, and S. G. 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