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Salzano, A.; Intignano, M.; Mottola, C.; Biancardo, S.A.; Nicolella, M.; Dell’acqua, G. Systematic Literature Review of Open Infrastructure BIM. Encyclopedia. Available online: (accessed on 24 June 2024).
Salzano A, Intignano M, Mottola C, Biancardo SA, Nicolella M, Dell’acqua G. Systematic Literature Review of Open Infrastructure BIM. Encyclopedia. Available at: Accessed June 24, 2024.
Salzano, Antonio, Mattia Intignano, Carla Mottola, Salvatore Antonio Biancardo, Maurizio Nicolella, Gianluca Dell’acqua. "Systematic Literature Review of Open Infrastructure BIM" Encyclopedia, (accessed June 24, 2024).
Salzano, A., Intignano, M., Mottola, C., Biancardo, S.A., Nicolella, M., & Dell’acqua, G. (2023, July 06). Systematic Literature Review of Open Infrastructure BIM. In Encyclopedia.
Salzano, Antonio, et al. "Systematic Literature Review of Open Infrastructure BIM." Encyclopedia. Web. 06 July, 2023.
Systematic Literature Review of Open Infrastructure BIM

Representation and modeling using the building information modeling (BIM) methodology of civil works have become the subject of increasing attention, thanks to the potential offered by Open Infrastructure BIM (I-BIM). However, the complexity of infrastructure works, i.e., the variety of construction and technological systems, makes Open I-BIM very complex and challenging. The lack of systemic knowledge on the subject is another challenging factor. 

bibliometric analysis I-BIM IFC infrastructure interoperability

1. Interoperability

1.1. Implementation of Open I-BIM and IFC

One of the aspects of the research conducted in recent years is rooted in an awareness of how tools and technologies have influenced the modus operandi by which information related to the built environment is created, exchanged, shared, and archived among stakeholders. There has been an exponential spread of integrated methods for sharing construction data following the introduction of IFC; this has changed the way tools and methods are conceived in both research and development. The IoT [1] and components of artificial intelligence (AI—data analytics, machine learning, deep learning, etc.) [2] have been integrated in tools and technologies, which is why scholars and researchers are finalizing paradigms that interconnect people, processes, and emerging technologies in a coordinated manner. This paradigm, by the way, is not to be limited to the construction sphere, but has rapidly encompassed research areas adjacent to the life cycle of the built environment, including buildings, infrastructures, and entire cities. It must be remembered that BIM, initially aimed at modeling, is now facing obstacles; the exploitation of big data, the IoT and artificial intelligence represent the missing pieces of the automation mosaic.
The reason for the exponential development in the field is the extension of the uses of BIM, which have expanded to include the life cycle management of built infrastructure assets. In this field, the study conducted by Llatas et al. (2022) focuses on the Life Cycle Sustainability Assessment, or the LCSA, proposing a systematic, interoperable and open-source approach to the implementation of this assessment in five phases. Automation of the main assessment phase is provided via the integration of IFC4. This method was applied to a case study in Spain through influencing design choices to move in the direction of less costly and more suitable structural solutions for the site, and identifying the system that emits less CO2 [3].
A study conducted by H. Li et al. [4], which proposed an information management model based on the Open I-BIM approach for monitoring and maintenance during the life cycle of bridges, has aroused great interest. Additionally, in this regard, studies have been conducted that enable the storage and visualization of inspection data so that inspection information (defect geometry, spatial location, shape representation, etc.) can be documented and represented in bridge BIM models [5][6]. Through the parametric modeling of a motorway bridge, it was possible to represent the interrelationships between defects and IFC entities, e.g., the technical elements of the bridge, root causes, and maintenance work. This method enables the representation of information on defects, with IFC facilitating the BIM environment for the purpose of civil asset life cycle management [7]. The authors analyzed the advantages of Open BIM in data and information management, emphasizing how the adoption of this approach can improve data quality, collaboration between the various actors in the infrastructure sector, and the efficiency of information management. Emphasizing the importance of obviating the loss of information, the case study conducted at the CESI Campus in Paris-Nanterre, for the realization of an intelligent building, demonstrated that IFC-based Open BIM allows for proper modeling without data loss [8]. Concerning the improvement and control of quality during the design phase, a study conducted by Häußler and Borrmann [9] proposed a standardized method to validate design quality by means of fourteen quality parameters. The results showed that the approach improved the collaboration between the various actors involved, and reduced construction costs and time by making the entire process more efficient. The authors emphasized the advantages of Open I-BIM in managing conflicts between different disciplines, information management, and process coordination. These advantages have been ascertained by studies such as that of Shin et al. (2018) [10], who demonstrated through a cost–benefit analysis the effectiveness of adapting BIM in railway construction sites for the construction of bridges and tunnels [11]. Other studies have combined the BIM method with GIS, for example, in the 4D GeoBIM system [12]. This combination ensures construction safety through the creation of three-dimensional models in a geospatial context. In order to promote the interoperability of GIS data, the study conducted by Malinverni et al. [13], analyzing the conversion methods of CityGML and IndoorGML, produced an enriched model based on the connections between different models in a GIS environment. It is an established idea that Open I-BIM plays an important role in the maintenance phase in terms of SHM (structural health monitoring) and control. Furthermore, the literature offers insights into this area, combined with the use of GIS technology. These include the BIM-3D GIS system [14] for tunnel management and the BIM-GIS framework for underground utility management systems, as well as systems combining SHM and BIM for monitoring infrastructure structures via an automated platform [15] and via VR and AR [16]. The most important aspect of the development of this technology is the interoperability of data ensured by the open formats also used for ongoing energy research. The study conducted by Choi et al. (2023) proposed a plan to generate alternatives and assess energy performance by analyzing the envelope shape of amorphous buildings through IFC [17]. Furthermore, more agile methodologies, based on the graph technique, are outlined to enable automation in the field of BEM with less inconsistency and interference [18]. The inclusion of domains not traditionally related to BIM encouraged the development of interoperability that would allow different project actors to facilitate the integration of their know-how and the import/export from one BIM tool to another.
The IFC standard was designed to solve this problem. However, at a stage close to its realization, its initial application limitations were extended into other areas. Therefore, the IFC standard, which was designed to transfer model data from one tool to another but not to be dynamically modified, has evolved significantly from the model view definitions (MVD). In addition, in relation to the implementation of the ISO-10303 Part 28 standard, Kim et al. [19] proposed a useful framework for the automatic generation of schedules from BIM, based on the international ifcXML standard schema. The scheduling approach was used to create construction tasks, considering productivity rates and sequencing rules. The practical case involved a BIM model of two separate structures modelled with basic building envelope components and exported as an ifcXML file.

1.2. Open I-BIM and Data Exchange

Following the transition of BIM into the infrastructure project sector [20][21], the design, construction, and maintenance phases of infrastructure projects have presented complex challenges mainly related to the concept of extension, linear development, and environmental compliance. To address chronic low productivity and unsatisfactory project performance, Baghalzadeh Shishehgarkhaneh et al. [22] examined alternatives to standard project delivery models and approaches. They found that failures of integration and collaboration are the fundamental reasons for unsatisfactory performance. The education sector has also aligned itself with the need to improve these aspects. Regarding the BIM transition in tertiary education, Chegu Badrinath et al. [23] state that the importance of BIM education has been recognized as of 2015 in AECO disciplines worldwide. Santos et al. [24] highlighted that the topics discussed are inherent to BIM tools, worldwide BIM adoption, and interoperability between BIM tools for different applications. One of the most critical issues remains the ability of IFC to support information exchange. Among numerous studies, Steel et al. [25] investigated interoperability in the exchange of IFC-based models between different BIM tools. Shahzad et al. [26] believe that BIM applications are more developed in design than their applications in construction and operation precisely because of interoperability challenges. Dixit et al. [27] investigated the factors hindering the integration of facilities management (FM) with BIM technology. They concluded that although the use of BIM technology has grown exponentially in recent years, unfortunately, its application to FM has not been fully accomplished. Lockley et al. [28] addressed the creation of standard building component libraries for BIM that can be shared between different BIM platforms. They also have highlighted the problem of comparing IFC models in a neutral collaboration between building information model platforms.
The critical problem in IFC models’ comparison is the definition of globally unique identifiers (GUIDs). As fruitful as it can be considered, the exchange of information between BIM tools is lacking, because when exporting the BIM model to IFC, one is subject to loss of information due to data reduction, simplification, translation, and interpretation. To remedy this, neutral networks partially solve this defect by classifying the contained objects and suggesting corrections. The SpaRSE-BIM neutral network model, based on sparse convolution for geometry classification in IFC and the semantic enrichment of BIM models, is better in terms of accuracy and execution time [29]. Similarly, the study conducted by Buruzs [30] on IFC models of residential buildings with the help of a GCN(graph convolutional network) led to an improvement in accuracy because the neutral network uses information about connections and spatial context for its classification decisions. Transport infrastructures, such as roads, railways, bridges, and tunnels, require constant and continuous geographical referencing during their design and construction; it is a fundamental parameter that cannot be excluded at any stage.
Therefore, the concept of interoperability is the common denominator in the presence of road infrastructures, as it ensures collaboration and exchange between professionals, and mainly between the different pieces of software used globally. Sometimes, software coordination is also necessary between platforms relating to infrastructures that differ in type, e.g., linear works and point works. With regard to bridges, software coordination and automation for the acquisition and visualization of inspection data are useful tools. At the territorial scale, with a view to the digitization of cities, the concept of interoperability and its application, including aspects relevant to the management of an urban context, are essential. Lv et al. [31] agree that smart urban management is realized through the digital collection, transmission, processing, and visualization of physical urban data, which can only be ensured by interoperability.
Therefore, the construction of a DT urban platform can improve the perception and decision-making capacity of cities, as it simultaneously offers a broad vision for planning future interventions and a valuable tool for city governance. Musarat et al. [32] state that digitization in infrastructure construction has improved quality of life, and enhanced productivity and automation. As repeatedly mentioned, data sharing and exchange are necessary to reduce risks to people, reduce environmental impacts, and to prevent unforeseen events on the construction site by checking in advance for interference between architecture, structures, and installations. Geographic references are obtained from the geographic information system (GIS) which, integrated with the BIM model, allows for parametric modeling with additional geo-information [33].

1.3. Complexity of BIM Dimensions

In the complex landscape of Open I-BIM, studies published in the last decade highlight the registered tendency to create more efficient ways of visualizing and utilizing data from nD models, particularly with regard to site monitoring [34], health and safety [35], and environmental aspects [36], showing that as the amount of input data increases, so does the degree of complexity of the domain.
The origin of this complexity lies in the heterogeneous nature of the data’s sources (tools, sensors, building management systems, etc.) that link them to the existing BIM model. The implementation of BIM reaches its peak for coordination and collaboration, unlike in the past, when it was thought that data augmentation coincided with the pre-construction and construction phases. This implementation is due to the synergy of all stakeholders converging and collaborating using a 4D BIM for activity planning and 5D for costing. The technique developed by Marzouk and Hisham (2014) combines time and cost management with the concept of earned value (EV) to determine the state of work at a specific date for the construction of a bridge through a BIM model of the bridge; this is referred to as bridge information modeling (BrIM) [37].
This phenomenon occurrs as a result of the exponential increase in data since the adoption of BIM, especially in the design and construction phases. The portion of the structured data pertaining to the operation and maintenance of the infrastructure is transferred in the form of a COBie spreadsheet [38]. The COBie standard (Construction Operations Building Information Exchange), also definable as an IFC model view definition (MVD), has been approved by buildingsSMART International, which identifies it as a subset of IFC (ISO 16739:2013) that includes data useful for facilities management. The creation of an IDM (information delivery manual) later translated into a pilot MVD for cultural heritage in consultation with experts in restoration and conservation, as stated by Oostwegel et al. [39], highlighted developments within a model of a historic building, in which the historic building was semantically enriched with information about the conservation plan.

2. Methodology

2.1. VR/AR and AI

Part of the research on Open BIM for infrastructure addresses a certain range of technologies, such as virtual and augmented reality, to improve management procedures and generally develop advanced methodologies. In 2022, Carbonari et al. [40] published their paper demonstrating that an MR-based platform can involve interested stakeholders in the assessment of renovation design projects, speeding up the decision-making process and increasing projects’ quality. Experimentation in this direction also harks back to earlier work, such as in the case of Zaker and Coloma [41], which involved BIM professionals in order to assess the limitations of applying VR in BIM, and how these issues might be overcome. However, there are many technical problems when trying to fully exploit the integration of BIM, open formats, and augmented and virtual reality technologies. For example, although virtual and augmented reality visors bring obvious advantages in the health monitoring of important infrastructures such as bridges, tunnels, and roads, and also in buildings of various kinds (even those of considerable engineering importance and weight) [42], IFC standards do not provide a description of structural health monitoring (SHM) systems. Theiler and Smarsly [43] proposed an extension of the IFC schema to bridge this gap. In the work of Chung et al. [44], dated 2021, the authors defined a tool for the conversion of BIM objects, namely doors and windows, from COBie format (Construction Operations Building information exchange) to a format that can be interpreted through augmented reality, thereby achieving an improvement in the facilities management process due to the immediacy of information transmission.
As early as 2013 [45], research looked for methods to bring site data to construction sites, and thus to on-site operators, in digital format, via augmented reality. Many methods were developed [46], and today, VR and AR viewers are widely used on construction sites for the management of construction orders [47]; web-based tools are also used [48], particularly given the central importance of cloud resources in the context of the BIM methodology [49]. The research field of technologies for the visualization and navigation of augmented reality based on BIM models is expanding, and has also been integrated with some specialized fields related to artificial intelligence [50]. For example, Chen et al. [51] published a paper describing a procedure for enhancing unmanned ground vehicles’ behavior, global path planning, and collision and congested environment avoidance in indoor environments, while Musella et al. [52] exploited AI for the digitalization of seismic damage in existing buildings. AI is also used in the form of neural networks and predictive and automatic modeling, both geometric and semantic. Koo et al. [53] used two models, namely a multi-view convolutional neural network (MVCNN) and PointNet, to classify ten types of commonly used BIM elements in road infrastructure, using a dataset of 1496 3D models to enhance mapping between IFC entities and BIM elements.

2.2. Point Clouds

BIM provides a tool to digitally represent the built environment. Moreover, it enables the possibility of dynamically representing the building process in every phase, enhancing facilities’ management, but also construction sites’ management. To this end, there are very advanced supporting technologies for geometric surveying [54]. For example, laser scanning can survey any object and create clouds of millions of points within minutes. The instrument fires laser beams covering a 360° space, and measures the position of the points hit as a distance from itself, based on the time it takes for the laser beam to return to the instrument. Then, knowing the geographical coordinates of the position covered by the instrument or other notable points, the entire point cloud is geo-referenced. Each point carries information about its location, geographical coordinate system, and other characteristics such as coloring and reflectivity index. The ability of discerning objects within the point cloud to segment the point cloud is very important [55]. Laser scanners mounted on unmanned aerial vehicles (UAV) are a game changer for large infrastructures’ management by means of BIM and digital data [56][57].
The two main methods based on point clouds are Scan vs. BIM and Scan-to-BIM [58][59]. Scan-vs-BIM is a monitoring method, while Scan-to-BIM is a modeling method. In fact, the first is a methodology based on comparing the characteristics of point clouds with those of the 3D models underlying BIM; these characteristics are not generated from the point cloud, but through other methods and other data. The second method consists of creating a BIM from the data contained within point clouds obtained via laser scanning.
In 2017, Reboli et al. [60] tried to define an accurate and applicable metric for evaluation of the quality of a point cloud for construction progress monitoring using the Scan-vs-BIM method. They developed a framework to evaluate hundreds of test point clouds vs. a test BIM, and a different scanning methodology to come up with the definition of the right quality criteria for successful Scan-vs-BIM identification.
Information retrieval from point clouds is a hot topic in BIM research. In 2018, Hidaka et al. [61] proposed a method for polygonising from a point cloud based on similarity. In particular, the authors’ algorithm identifies objects that may belong to a same category through shape matching. In 2019, Xue et al. [62] developed a very innovative approach for detecting architectural symmetries directly from 3D point clouds. The authors formulated an automated architectural symmetry detection system as a nonlinear optimization problem, involving parameters reflecting architectural regularity and topology; then, they developed a derivative-free optimization approach for optimization-based detection of an architectural symmetries algorithm, testing it on nine sets of point clouds and achieving good results in terms of computational time and symmetries detected.

2.3. Semantic Technology

Works such as that of Xue et al. [63] deal precisely with the possibility of automatically identifying elements that can correspond to instances of BIM models. To do this, algorithms are used that exploit semantic technologies. Semantic technology is a set of methods and tools that, through using formal semantics, provide advanced means for categorizing and processing data. It presents the possibility of analyzing the relationships within varied datasets. It can be seen as a buffer layer between human data understanding and artificial intelligence data and information processing. The process of identifying possible instances of BIM models from geometric data in point clouds using semantic technologies is called semantic segmentation. Moyano et al. [64] applied semantic segmentation to retrieve data from the point cloud of the façade of the Casa de Pilatos in Seville, which is a heritage building. For a very similar purpose, Zhai et al. [65] exploited artificial intelligence, and in particular deep learning, to automate the semantic segmentation of point clouds. Jung et al., in 2018, developed an effective method for automatic analysis and segmentation of point clouds in order to recreate the indoor environment of buildings with multiple rooms. In particular, the authors managed to reproduce various objects, such as doors and windows, and to discern between structural and non-structural elements [66]. The semantics of BIM models is a fundamental dimension of the entire methodology, fully embodying the I of the BIM acronym, which stands for information. Indeed, information management is an aspect that BIM research has always invested in, and it is what makes a BIM an Open BIM. Some of the existing research focuses on the development of knowledge bases via ontologies [67]. These can be uploaded online, made public, and subsequently customized by other users. These knowledge bases find wide application in those fields less covered by BIM schemas and file formats, such as Heritage BIM (BIM applied to architectures with cultural–historical character) [68]. Furthermore, the use of semantic technologies is widely applied by BIM researchers to optimize data transmission and sharing, sometimes circumventing the limitations of available formats such as IFC [69]. Guerra de Oliveira et al., in 2022, published a paper addressing these last two topics, by developing an ontology for architectural H-BIM in order to enhance the semantics of an algorithmically designed model [70].
Another work using semantic technology to extend the IFC standard is that of Wang et al. [71], which concerns the field of facilities management by means of sensors and Internet of Things systems. Other researchers, such as Rampini and Re Cecconi in 2023, proposed the use of synthetic images, alongside real ones, generated from 3D BIM models; this proposal aimed to improve the performance of training object detection models in facilities management [72]. Semantic technologies are very useful in the field of facilities management; in 2022, Chen et al. [73] developed a method to automatically detect defects in the concrete of buildings by exploiting aerial images and semantic-rich BIM models. Another example of semantic technology use is that of Oti-Sarpong et al. [74], who in their 2022 paper proposed a novel algorithm for querying online BIM libraries, using the items’ attributes and not only their geometrical similarity or matching keywords. The Scan-to-BIM method can be seen as a sub-method of reverse engineering, that is, the process of retrieving a building or infrastructural feature and then producing a representation of it. This is a common practice in H-BIM, both for architectural modeling [75][76] and for structural health modeling [77].
It is very important to clarify that a multiplicity of studies simultaneously cover many of the topics already addressed. In the case of Banfi et al. [78], for example, the authors focused on a specific case study, the Claudian Aqueduct in the Appian Way Archaeological Park, integrating the geometric survey techniques of laser scanning and photogrammetry, knowledge of ancient Roman construction techniques and materials, semantic technology, HBIM, and extended reality (XR).

2.4. BIM-GIS

Another tool at the core of infrastructure’s digitalization is GIS (the geographical information system). A basic requirement of a project is geographical coordination, and BIM is no different [79]. Research on GIS integration within BIM has produced many results. First of all, there is an existing field of research field GIS and BIM file formats’ conversion to permit the integration of the two [80]. Other researchers have focused on specific applications, such as Mignard and Nicolle in 2014, who used BIM, GIS, and ontologies in urban facilities management [81]. Kurwi et al. [82] applied a similar integration approach to the design of rail projects, while Barazzetti et al. [83] published a paper on a BIM-GIS approach for roads’ detection and parametrization using point clouds. Other applications cover underground utility management systems, drainage systems for roads [84], and systems for heritage [85].

2.5. Analysis Based on BIM Data and Facilities Management

As previously mentioned, BIM is about information. Before being processed, information is data. A building information model relies on a huge amount of data concerning several dimensions, and new methods to deal with the computation burden must be applied [86]. Data and information are used for analysis and represent a rich meaningful resource that must be organized in a standardized manner to ensure communicability [87]. Data analysis and information retrieval within Open BIM is an existing research focus. Zhou et al., in 2020, published a paper on methods of 3D spatial data analysis for BIM, presenting an outstanding and comprehensive review [88]. Ramji et al., in 2020, dealt with using BIM for evaluation of the energy performance of buildings in the early stages of a project, performing a data file format integration between BIM and BEM (building energy modeling) [89]. BEM is a thriving research topic, with great focus on energy efficiency evaluation, planning, and optimization [90][91][92][93][94]. For example, Bughio et al. [95] investigated potential reductions in indoor temperatures by exploiting simulations run in building information models. Data and information within BIM cover fields such as risk analysis [96][97], delay analysis [98], fire risk evaluation and management [99], and construction management in a broader sense [100][101][102]. Indeed, integrating other techniques within Open BIM, such as life cycle management and assessment, cost-estimating approaches, and scheduling systems, is a powerful tool within facilities management [103][104][105][106][107]. The applications that BIM data can provide in terms of data analysis are many and varied. For example, Nik-Bakht et al., in 2021, investigated the possibility of analyzing the acoustics of buildings by means of data stored in building information models, with specific reference to reverberation time [108]. Chen and Huang, in 2015, developed an emergency response model during construction fires based on the analysis of data from BIM models using evacuation route optimization systems [109]. Dols et al. [110] used BIM for road safety analysis by developing several different driving simulation scenarios. It can also be found that structural engineering benefits from BIM data analysis in terms of structural material degradation and damage management [111][112]. Moreover, other works, such as that of D’Amico et al. [113], have investigated the integration of non-destructive survey data into preliminary phases of digital design within BIM, with possible applications in pavement management systems for roads. Others have investigated the possible benefits of using the Open BIM methodology in renovation projects by developing an innovative approach named Living-Labs, which involves key stakeholders through consultations [114].

2.6. Digital Twins

Another recursive concept in research on Open I-BIM is what is referred to with the keywords ‘digital twin’. Pregnolato et al. [115] consider it a means of introducing Civil Engineering 4.0 to existing infrastructures. In particular, the authors proposed a step-by-step workflow for producing digital twins of existing bridge infrastructures, while Lee et al. [116] and Kaewunruen et al. [106], working with the same aim, dealt with underground tunnels and subways stations. DTs are a tool for the constant supervision of buildings or infrastructure, the monitoring of health assessment parameters, and real-time maintenance planning, with a view to reducing the polluting impact of such operations [117][118]. Pollution reduction and sustainability levels’ enhancement are some of the objectives of the Lazio Region (Italy) project for port areas’ transformation into a ZED (zero-energy district). Agostinelli et al. [119] exploited the DT of the Anzio harbor, integrating BIM and GIS to assess energy efficiency measures.

2.7. BIM/Blockchain/IOT

Connected to the concept of digital twins is the IoT, which is a network of physical objects, i.e., ‘things’, that have sensors, software, and other technologies integrated for the purpose of connecting and exchanging data with other devices and systems on the Internet. The IoT can be used to ensure that BIM information is updated in real time [120], particularly for the purpose of facilities management and maintenance [121]. The IoT can be used for real-time prediction of flooding; Edmondson et al. [122] designed a prototype Smart Sewer Asset Information Model (SSAIM9) for an existing sewerage network, developed using IFC4 and involving the use of IoT sensors for real-time monitoring and asset performance management.
The quantity and quality of data potentially contained in BIMs is such that the information that can be derived from them may be sensitive. This means that data defense systems are needed that are equal to the technical and economic importance of the projects that BIM addresses. One example of this is the Blockchain, a computer system based on nodes and components that uniquely and securely manages a public ledger consisting of a series of data and information, such as transactions, in an open and distributed manner, without the need for central control and with a near-zero probability of cracking. This has wide application potential within BIM and the AECO fields for the management of large projects [123][124][125][126].

2.8. Modeling/Design Methodologies

BIM involves a type of parameter modeling known as procedural parametric modeling, whereby modelled objects are linked through parametric relationships. Clearly, anyone working in BIM and BIM research is working in parametric environments and with parameterized models, thereby justifying the work of authors such as Biancardo et al. [127] and Zhang et al. [128], who have investigated the methodology’s potential for practical applications in road engineering, and in geotechnical and dam engineering.
Other studies have focused on integrating BIM and its open approach into other methods and techniques that improve productivity, for example, 3D printing and pre-assembled or prefabricated buildings [129]. Schwabe et al., in 2019, proposed model-based rule checking for the planning of construction site layouts [130]. Some of the existing research focuses on the creation of specific BIM object libraries; for example, Bridge and Carnemolla [131] addressed the gap in BIM object libraries for facilitating social inclusion with sustainable architecture. Similarly, Xue et al., in 2018, published a paper proposing a segmentation-free derivative-free optimization (DFO) approach that would transpose the generation of as-built BIMs from 2D images into an optimization problem of fitting BIM components to comply with architectural and topological constraints [132]. Doukari and Greenwood’s paper from 2020 presents an innovative approach for creating BIM directly from plan drawings using automatically derived parameters [133]. As multidisciplinarity is a key feature of the Open BIM methodology, some researchers have focused on the integration of architectural and structural modeling. Hamidavi et al. [134] developed a structural design optimization (SDO) prototype to semi-automate the structural design processes of tall buildings, residential buildings, bridges, truss, girders, etc., in their early stages.

2.9. Knowledge Transfer

Some existing research also focuses on knowledge transfer [135][136]. Indeed, one of the main obstacles to BIM’s adoption in small- and medium-sized companies is the need to adopt a new production paradigm that involves the education of employees; this is costly both in terms of the money and time spent on education courses [137]. However, new technologies, digitalization, and new methodologies have always been economic drivers, and managers must confront this [138]. Finally, there are many works by those who have attempted to give a systematic order to the knowledge inherent in Open BIM and its applications, in construction [139], road infrastructure [140], and transport infrastructure hubs [141]. The attempt by Godager et al. [142] to formalize and systematize the concept of Enterprise BIM, i.e., a comprehensive and holistic way of utilizing BIM throughout a building’s life cycle, is interesting; the authors made this attempt through harnessing the intersection of the technologies described above, such as IoT and AI, and integrating them with multidisciplinary methodologies such as life cycle assessment, aiming to optimize resources, minimize environmental impact, and improve safety in the context of the entire infrastructure life cycle.

3. Transport Infrastructures

3.1. Bridges

Academic research concerning bridges within Open BIM is in full swing; structural design, optimization, inspection, data retrieval and analysis are all hot topics [143]. The Open I-BIM approach has proved particularly suitable for structural health monitoring operations regarding both pillars, girders, and decks [144]. Artus et al., in 2022, published a study focusing on a BIM-based framework used to incorporate automatic bridge damage data acquisition and transfer, as a tool for inspectors to use in subsequent analysis and simulations [145]. However, BIM tools are also and above all the prerogative of design operations; in 2021, Girardet and Boton developed an algorithm for the automated production of a building information model of bridges based on structural design and data analysis [146]. Automation is one of the goals that researchers pursue, with the aim of reducing the time taken by certain BIM processes that are vital, but also time-consuming and prone to error. In 2020, Lee et al. published an innovative framework for automatic bridge design parameter extractions from point cloud data, with optimal results and only a 0.8% error in parameter estimation [147]. Other authors have instead embraced the idea of Open BIM as a management tool for various phases of a bridge’s life, using terrestrial laser scanning technology for the retrieval of geometric data as the basis for a BrIM process (as a sub-entity of BIM), and integrating this methodology with a decision support system for asset management [148].

3.2. Tunnels

Regarding tunnel infrastructure, Massimo-Kaiser et al. [149] evaluated how the use of BIM has affected the social and economic aspects of seven different tunnelling projects, resulting in a general benefit. The studies of Wang et al. [150] focused on sustainability. In particular, the authors designed a tunnel using parametric modeling in a visual programming software environment (Dynamo), which allowed them to enhance the geometrical modeling and to perform a carbon emission assessment; thus, the tunnel design could be positively affected by the parameter of sustainability. Yu et al. [151] formulated methods of configuring BIM + VR prototypes to enable the visualization of the physical context of tunnels to enhance emergency response training, in particular regarding fire risks. Borrmann et al. [152] developed a sophisticated framework for the multi-scale geometric–semantic modeling of shield tunnels in subway transport systems, for BIM and GIS applications. The authors placed particular emphasis on providing consistency in representations across the different levels of detail (LoDs), and they proposed a potential extension of IFC for incorporating multi-scale representations of shield tunnels in a manner that enables the automatic and consistent updating of all dependencies on finer levels when an object is modified on a more coarse level. Additionally, Zhou et al.’s paper from 2018 looked at the modification and enhancement of IFC standards for shield tunnels, a common approach for subway tunnel excavation that is not yet properly represented by IFC [153].

3.3. Roads

There are many and diverse applications of Open BIM to road infrastructure. Some authors have focused on the management and maintenance of existing assets, others seek to innovate design processes, and still others search for solutions to specific problems and design contexts. Vignali et al. [154] embarked on designing a new road intersecting an existing road and a railway line, evaluating how the use of BIM in a complex context benefitted the design solution. Tang et al. [155] used an advanced visual programming language environment to integrate pavement structure analysis into 3D road design within BIM. With a similar approach, Oreto et al., in 2021, performed a decay analysis on a road pavement based on material and mixtures data, and developed a management tool for scheduling urban road maintenance [156]. In the context of maintenance and asset information management, Aziz et al. [157] worked on integrating big data, sensors, and BIM for highway applications, while Kim et al. [158] developed a BIM approach based on smart objects, from which information about scheduling and cost estimations could be automatically derived. A specific design problem for highways concerns underpass road clearance in road-widening projects; in 2022, Jiang et al. published their paper proposing a method for building road digital twins from online map data, and a method for road widening based on them [159].
Another important aspect of road infrastructure is traffic control and sensor systems, such as intelligent transport systems (ITS). Mirboland and Smarsly, in 2021, proposed an extension of IFC for modeling and semantically described an ITS for highways [160]. Moving away from motorways toward micro-mobility, in 2020, Campisi et al. dealt with cycling paths using I-BIM to connect cost and safety requirements in the planning phase, developing a methodology which, starting from the identification of the intervention area and the available economic resources, provides all the elements for designing a cycling path, from location and safety to the definition of preferred options in terms of materials [161]. Biancardo et al. [162] proposed a BIM workflow for modeling airport terminal expansions, demonstrating a reduction in construction times and costs for their case study, which was based on the ‘IV Bridge’ project for the expansion of the departure area of Naples Capodichino International Airport by means of an elevated walkway.

3.4. Railways

As far as railways are concerned, there are several studies testing the suitability of Open I-BIM for the planning, design, operation, and maintenance of railway works. Park et al. [163] focused on reverse engineering a ballasted track of a straight railway stretch. The authors focused on methods for automatizing onerous and error-prone manual operations such as point cloud denoising and registration and 3D modeling, within BIM-based tools. Moreover, the railway ballast representative parameters were selected for automating maintenance planning. Similarly, Grandio et al. [164] focused on automation of the segmentation operations of point clouds representative of extended railway complexes by exploiting deep learning. Acerra et al. [165] investigated the potential of the Open I-BIM approach for the design of an urban tramway line, and clash detection simulations and scheduling analyses were performed. In 2019, Neves et al. published a paper on a case study of rail track rehabilitation based on the implementation of BIM with a comprehensive workflow [166]. Part of the research focused on the use and expansion of the IFC standard to include the description of railways, which certainly favored and supported the development of the IFC 4.3 standard, which can now also describe railway objects [167][168][169]. An interesting example of applying Open BIM to asset management can be found in the work of Ciccone et al. [170]. The authors had the main goal of systematizing information by digitalizing the infrastructure of the Cancello–Benevento railway line in Italy in order to assess possible performance gaps within the national railway standards. A federated digital model was developed for the survey, data management, maintenance, financial evaluation, and general asset management, through software specifically designed and based on the IFC4×2 schema. Seo and Lee [171] focused instead on developing suitable BIM libraries of railway objects complying with South Korean standards. Haussler et al., in 2021, dealt with code compliance checking for railways in Germany by using a visual programming language environment and integrating BIM, BPMN (business process model and notation) and DMN (decision model and notation), thus investigating rather the railway BIM models released in IFC format would provide all the required information or not [172].

3.5. BIM Authoring

Santamaria-Pena et al., in their 2022 paper, provide an example of a workflow using Autodesk products, in particular Revit and Civil 3D, for modeling levelling, embankments, and overburdens [173]. In 2021, Fabozzi et al. published their work on a BIM-based approach to the geotechnical and numerical modeling of conventional tunnel excavation using Bentley products [174]. Additionally, in the work of Biancardo et al. (2021), Bentley software was involved in the as-built modeling of an existing railway line in Croatia. Works such as that of Abbondati et al. [175] and Guerra De Oliveira et al. [176] have investigated the applicability of I-BIM to airport infrastructure management, and in particular to runway pavement maintenance. Yin et al. [177] dealt with tunnels. Port infrastructure has also been the subject of academic interest; Hua et al. (2020) applied BIM technology to the design of breakwaters [178], while Xiao et al. [179] focused their work on canals.
Usually, software for the geometric modeling of roads is based on the realization of three-dimensional axes, with which a geometry (the cross-section) is associated, and which will be extruded along to obtain the road solid. However, this entails certain limitations, especially concerning those more elaborate objects that nevertheless constitute fundamental parts of the infrastructure, such as safety barriers, hydraulic works, technological installations, furnishing elements, etc. Some academic works focus on the compensation of parametric object libraries referred to for modeling, which are often not sufficiently provided; this is the case with the work of Biancardo et al. (2020), for modeling retaining walls and safety barriers [180]. In some academic work, I-BIM has been applied to the modeling of existing roads to obtain an information model of the condition of a road’s pavement, in order to support the management of the road pavement itself [181]. As expected, I-BIM has many applications for pavement management. Indeed, the information-rich digital model obtained by applying the I-BIM methodology is a tool able to support the development of road pavement maintenance systems, both in urban and suburban areas. In the case of Oreto et al.’s work, the authors integrated methods such as life cycle assessment for technical–economic assessments and maintenance planning into the I-BIM methodological framework [182].
An example of the application of BIM in this field can be found in the work of Biancardo et al., 2021, in which an as-built model of a main street in the historic center of Naples (Italy) was produced by combining several software tools and using advanced surveying techniques, such as laser scanning and photogrammetry, resulting in a case study of road H-BIM [183]. Other cases of BIM applied to roads made of stone led to Archaeo-BIM; these are located in the archaeological park of Pompeii [184][185]. In conclusion, the academic community is well aware of the potential of the BIM methodology applied to the road environment. The number of case studies is growing. The main obstacle to wider dissemination is related to software development. This can be improved, particularly with regard to two aspects: modeling, which for higher levels of detail becomes more difficult and less automatic (and therefore, generally more expensive), and interoperability.


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