Innovating the Construction Life Cycle through BIM/GIS Integration: History
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Subjects: Engineering, Civil
Contributor:
Mattia Mangia

The construction sector is in continuous evolution due to the digitalisation and integration into daily activities of the building information modelling approach and methods that impact on the overall life cycle. This study investigates the topic of BIM/GIS integration with the adoption of ontologies and metamodels, providing a critical analysis of the existing literature. Ontologies and metamodels share several similarities and could be combined for potential solutions to address BIM/GIS integration for complex tasks, such as asset management, where heterogeneous sources of data are involved. 

  • BIM
  • GIS
  • metamodel
  • ontology

1. Introduction

Buildings, cities and infrastructure are becoming increasingly complex systems [1], which bring together technical, economic and social aspects. For instance, infrastructure plays a crucial role in the development and sustainability of society [2] and, along with large buildings and cities, they are challenging projects for the AECO/FM sector (architecture, engineering, construction, operation and facility management). The construction life cycle is composed of several phases (e.g., design, operation, and management) and these are involved in constant evolution, due to process innovation [3]. Therefore, several research studies have investigated ways to overcome these challenges in the AECO/FM sector, by means of new methodologies and technologies, such as building information modelling (BIM) and GIS (geographic information systems). BIM is a widespread methodology which allows the AECO/FM sector to enhance its management of the information and its interoperability [4]. BIM should cover all the phases of a construction’s life cycle but at the moment, it is mainly focused on the design phase [5]. The construction sector is calling for new skills, performance standards, interoperability, training and an IT system that covers the whole lifecycle of complex products or buildings. Furthermore, the use of innovative technologies and methodologies in AEC industry is now steadily expanding, since it has reached a high level of awareness [6]. Geographic information systems (GIS) refer, instead, to information systems which can handle and analyse data associated with a location on earth [7]. BIM and GIS are commonly adopted to support the AECO/FM sector, which is information intensive [8] and involves several stakeholders with different backgrounds and expertise. Regarding this, BIM and GIS aim to develop a common and shareable ground of data at the building and territorial scale, respectively. Their respective open formats, namely IFC and CityGML (an XML-based data format, the most used for 3D-GIS applications), allow the interchange of datasets between several software families. The digital model of the construction can be modelled or imported in order to get the required information for specific analysis of the construction (e.g., structural, thermal or energy analysis) or its interaction with the environment (e.g., environmental impact analysis, feasibility, policies). Moreover, the digital model can be integrated with other valuable sources of data.
BIM and GIS, along with other tools such as IoT sensors (Internet of Things) can provide a way to develop smart environments. Cesconetto et al. [9] defines smart environments as “pervasive computing systems that provide higher comfort levels on daily routines throughout interactions among smart sensors and embedded computers.” With this upcoming and trending topic, including the concept of “Smart cities”, the last decade has seen efforts from both the academic and professional world to implement integrated applications of BIM, GIS and other sources. However, in order to realise these solutions, high-level knowledge from both civil engineers and information technology (IT) engineers is required.
BIM allows construction components to be modelled in a 3D visual space, directly creating an instance of a class (named a “family”), such as walls, ceiling, slabs, etc., instead of drawing CAD lines which represent the element. This reflects the nature of the data model itself, which BIM and GIS software present in different modelling languages: EXPRESS and CityGML, respectively [10]. Additionally, GIS has its own data models and standards, namely the ISO 19100 family defined by the ISO/TC 211 (Technical Committee), which is based on model-driven architecture (MDA). MDA and model-driven engineering (MDE) are software design and development approaches which lead to the definition of metamodels. While these standards and specifications allow each methodology to grant internal interoperability, their well-defined structures proved to be a limitation in interoperability attempts between them. The matter known as BIM/GIS integration is still an open topic, as shown by several literature reviews about the argument, and is affected by a vagueness which led to a fragmented and disorienting scenario. In this article, metamodels are intended as described in the international Standard “Meta-Object Facility” (MOF) [11]. Compared to an instance-based approach, BIM/GIS integration at metamodel level is used to harmonise, map or converge concepts from these two elements.

2. Background

2.1. BIM/GIS Integration

The BIM/GIS integration is a broad and trending topic in AECO/FM-related research. This can be observed in the annual number of documents found on Scopus with the following query: “BIM“, “GIS” and “Integration” (e.g., 196 in the 2015–2020 period). As stated by Beck et al. [12], the BIM/GIS Integration is an umbrella term that does not refer to a precise methodology or subject area. As can be seen from literature reviews investigating the BIM/GIS integration [12], studies developed several methods, such as model conversion [13][14] extension [15], interlinking [16] or merging [17]. In their literature review, Liu et al. [18] evaluated integration solutions by means of “EEEF” criteria, namely effectiveness, extensibility, effort and flexibility. Moreover, [18] classified BIM/GIS applications, grouping them into thematic categories, such as 3D cadastre [19], location-based services and navigation [20], asset management [21] and so on.
Therefore, the BIM/GIS integration topic includes several approaches and several applications, without a clear pathway and with a heterogeneous understanding of the term “integration”. However, one of the most relevant approaches is based on a semantic web by means of linked data and ontologies (e.g., [22][23]) which will be further explained in Section 2.3. This approach involves the convergence of several data sources, maintaining them consistently and distinctly, and it can overcome problems due to the different nature of the native data models of IFC and CityGML, as observed by [24] in the definition of a city information model (CIM). Moreover, in their literature review, Zhu et al. [25] identified the two main levels of the BIM and GIS integration: geometric and semantic, with the latter being the most complex to resolve. Therefore, the BIM/GIS integration topic is still an open argument that continuously expands with further exploration and research, due to the variety of approaches which can be adopted and the countless specialised applications which, altogether, constitute the whole AECO/FM sector.

2.2. Model Driven Architecture and Engineering

Model-driven architecture (MDA) was first described in 2000, by the Object Management Group (OMG), and it is an approach for software design and development that relies on linking models in order to build a complete system [26]. MDA links the abstract work of developing a model with its concretisation in code in order to automate this process. MDA provides a set of standards and guidelines, and it can be considered as a part of the broader model-driven engineering (MDE) [27], which follows the principle of “everything is a model”. MDE introduces the concepts of model and metamodel, relating them with the system of which they are an abstraction. The abstraction presents a layered structure, standardised in the “Meta-Object Facility” standard by the OMG; it is subdivided into four layers, called “metalevels”:
  • M3: is the meta-object facility, the most abstract layer, and is also called the meta-meta-model. In the IFC case, the model language EXPRESS itself can be considered to be the M3 layer, according to [28];
  • M2: also referred to as the metamodel, describes the schema used to instantiate M1 models. The IFC structure itself can be placed at this level, as well as related entities such as IfcWall or IfcDoor;
  • M1: is the model itself, for example the package of UML schemas which are used to describe a real-world domain. As a BIM analogy, the IFC model of a construction, designed by an expert in BIM software, is a construct belonging to the M1 metalevel;
  • M0: is the real-world object. In software engineering it can refer to code, in the AECO/FM sector it is the real-world building or infrastructure.
In this paper, M2 and M1 will be addressed, in particular. M2 and M1 are two levels of abstraction directly linked in a class-instance kind of relationship.
MDE and MDA are approaches for software design with the aim to facilitate and handle the whole life cycle of information and to improve software re-usability through the abstract layer subdivision. This concept is also applicable to the BIM vision, which inherits these traits from the object-oriented programming and modelling (OOM) of IT technologies. M2 metamodels for BIM/GIS integration can be achieved through UML profiles, being packages that can be applied to the core UML to extend and specialise the metamodel for a specific domain. The development of a BIM/GIS metamodel provides a formal structure for software development, which can be adapted, transformed and extended according to stakeholders’ needs.

2.3. Ontology and RDF/OWL Ontology Language

The term ontology, in computer science, refers to a special kind of information object or artefact [29]. Studer et al. [30] defined ontologies based on former ontology definitions as “a formal and explicit specification of a shared conceptualization”.
In practice, ontologies can be developed using the resource description framework (RDF), a standard data model for data interchange on the web [31]. However, ontologies commonly require more expressive elements to properly describe a domain and, thus, on top of RDF the Web Ontology Language (OWL) is provided. OWL language allows more complex RDF statements and it is a formal semantic developed by the W3C consortium. The adoption of ontologies leads to the creation of formal knowledge bases from which information can be retrieved unambiguously, both from humans and other agents.
Ontologies suit large knowledge bases well and they allow for the provision of a whole overview which could not be achieved from the simple sum of the underlying clusters. For example, ontologies are used in medical science, such as the Ontology for General Medical Science [32] or the Foundational Model of Anatomy (FMA) [33].
Foundation (or top-level) ontologies are ontologies that provide the very general terms which are common across domains. They act as a common basis for domain-specific ontologies. In the AECO/FM sector, a minimal ontology is the Building Topology Ontology (BOT), which describes the core topological concepts of a building [34], and it can be combined with other general or domain-specific ontologies, such as sensor observations (e.g., SOSA ontology [35]) or IoT devices (IoT-Stream [36]).

3. Lack of a Bridge between AEC and MDE Communities

Regarding the first research question, the state of the art of BIM/GIS ontologies and BIM/GIS metamodels show two different scenarios. While BIM/GIS ontologies have already been investigated for several AECO/FM applications, on the other hand, from the state of art review of BIM, GIS and MDA/MDE (thus metamodel), it emerged that the topic has scarcely been investigated or addressed. This gap found in literature could be exploited as a future work direction, since it can be related to the widespread effort of organisations such as buildingSMART, regarding data interoperability and harmonisation. Addressing the issue of BIM/GIS integration at the metamodel level can provide a high level of abstraction strategy that at the model level cannot be evaluated. The work of Jetlund et al. [37] is a recent effort in this direction. From the literature analysis it was found that the assignment of the abstraction level of a model can be ambiguous, with different authors referring to the same conceptual construct both as a model or metamodel. In fact, Götz et al. [28] carried out research in which they assessed and classified AECO/FM-related articles dealing with MDE techniques, highlighting how several proposals made by the AECO/FM community could have been implemented and misconceptions about modelling could have been avoided.
Regarding articles found in the BIM/GIS integration topic and related literature review [12][25], it can be seen that the topic is mainly addressed by members of the AEC/FM community, and there is a slight lack of awareness about MDE-modelling tools. However, the BIM/GIS integration topic actually adopts MDE approaches, such as model conversions, transformation, mapping and extensions. Moreover, with the incoming replacement of the IFC EXPRESS data model with the IFC UML [38], a convergence between AECO/FM and software engineering members could be strengthened. Adoption of UML for both BIM and GIS, along with most national and international standards, can ease metamodel approaches. Since standardisation is a main issue found in the BIM/GIS integration, it is important to understand what the added value is of metamodels, compared to models. For instance, the multi-LOD metamodel [39] was developed to provide a means for defining a project-specific data model, incorporating formal LOD definitions for component types. The metamodel itself can be used, transformed and adapted subsequently, constituting a reference point for underlying models. Otherwise, the risk is to provide still effective data models which could result in being isolated and hard to formalize or standardize. A stronger link between MDE and AECO/FM communities is also needed to clarify terminological ambiguities which can lead to misconceptions. For example, in the first part of the literature review, the term “model driven” referred to MDA and MDE concepts, but also to the concept of an application driven by the 3D model developed with BIM tools. This issue extends to the broader topic of the BIM/GIS integration, in which Beck et al. [12] noted a lack of expert knowledge as being one of the challenges that need to be addressed. In fact, the vast knowledge required from both AECO/FM and IT domains is rarely possessed by individual developers and this lack of awareness leads to insufficient solutions and intensive training.
In addition to the intrinsic complexity of the matter, the terminological ambiguity contributes to hindering a mutual and smooth learning of concepts. For example, [19] unified building model (UBM) is referred to as a metamodel but [12] addressed it as a shared model, in order to differentiate it from the metalevel M2, known as “metamodel” according to MOF specifications. This observation is fundamental to not confuse the schema and instance integration approach, which are at the lower metalevel M1 and M0 of the MDA hierarchy. The metamodel approach is referred as the “unification approach”, according to the ISO 11354 classification. The authors of [40], in their analytic review of previous BIM/GIS integration approaches, classified the works done by [41] and [42] as a “unification approach”, therefore implying the adoption of metamodels.
However, the first query performed in this paper could not manage to retrieve these articles by means of “metamodel”, “MDA” or “MDE” keywords, and they were found from previous, more general, literature investigations on the broad BIM/GIS integration topic. This shows how the matter can be easily misunderstood or missed, especially when investigated by users from the AECO/FM community who may not possess advanced IT knowledge. Therefore, the current state of the art about BIM-based metamodels is a relatively unexplored topic and suffers from a lack in the literature. Compared to previous literature works, this study proposes a novel overview and approach for the BIM/GIS integration, relying on the potential of the joint use and synergies between metamodels and ontologies, as discussed in the next section.

4. Metamodels and Ontology Synergies for Digital Systems

In the AECO/FM sector, BIM and GIS integration empowers data management and analysis capabilities, due to highly detailed and geo-located models of the assets. Acquiring, collecting and representing heterogeneous information in an interconnected environment allows the observation of elements of the assets as a collaborative and symbiotic ecosystem, allowing the construction of a new knowledge base, capable of supporting complex decision-making processes. As shown by the literature review, a digital system based on ontologies and metamodels was not found, and this answers the second research question. The definition of a BIM/GIS integrated metamodel, powered by metamodels and ontology synergies, can be used as a common environment of representation for complex infrastructure applications. A metamodel can be sided by a common ontology, enabling a transformation of information from heterogeneous sources into uniformly represented homogeneous elements. This approach allows us to achieve the following objectives:
  • Seamless data integration between BIM and GIS with minimised data loss;
  • Definition of methodologies for the creation and joint adoption of ontologies and metamodels for BIM/GIS integration;
  • Definition of a conceptual framework to enable artificial intelligence (AI) and machine learning applications.
The creation of reference ontologies allows the harmonization of data coming from different sources, to define standards, vocabularies and the problem domain. Upon this common ground, the BIM/GIS metamodel can define the solution domain and, therefore, the functionality of the system that needs to be developed. A simple conceptual overview is shown in Figure 2.
Sustainability 14 00766 g002 550
Figure 2. BIM/GIS digital system for asset representation and management.
Ontologies allow SPARQL queries because of the storage of BIM and GIS models in semantic web data formats, but they still operate in the problem domain. They give a representation of the problem, defining its classes and relationships, but to allow integration with operative applications, by means of AI and machine learning, there is a need to reach the solution domain. This is the purpose of metamodels: to define how a system operates and identify its applications. Ontologies serve as a common ground which can be modularised and queried, in order to develop clusters of knowledge bases that are understandable by every component of the integrated system while its applications can be instantiated from the metamodel, acting as a design compass for software adaptation and re-usability. These aspects need to be taken into account since each component of the system has its own standards and formats. Moreover, constant updates and changes need to be managed in order to avoid obsolescence and costly refactoring of the whole system every time. It is also worth noting that from the review, no relevant trend about a specific AECO/FM scope was found, besides the four occurrences of “sustainability” in Table 3. However, this scope, along with “Cultural Heritage” is expected to be predominant in the future. This consideration is inferred by the fact that they are both heavily semantic-based domains upon which a shared conceptualisation by means of ontologies and metamodels can be strategic to develop an effective knowledge base.

5. Implications of a BIM/GIS Integration Based upon Metamodels and Ontologies

To better understand the value and contribution of this paper, a comparison of the main activities which metamodels and ontologies can affect is described here. Companies involved in the AECO/FM industry or municipalities need to extract the highest value from data, processing and turning them into information, knowledge and wisdom. The sharing of information is crucial, along with the capability to integrate existing BIM and GIS data models with other sources (e.g., IoT sensors on buildings and infrastructures). From this perspective, a metamodel (e.g., in UML) integrating BIM- and GIS-data models allows a formalised and stored structure for software development of the company or agency to be defined, improving the re-using of software and potential expansions and contributions to new standards. At the same time, shifting BIM and GIS data to RDF or OWL allows them to be put on the same level for queries and knowledge extraction.
The adoption of this approach, involving metamodels and ontologies, can provide benefits to several users. For instance, the Research and Development division of a company can adopt metamodels to better understand how to develop software and data models linked to already existing BIM and GIS software. Ontology can be adopted to provide data in a machine-readable and interpretable way, which other stakeholders can receive and employ for their tasks (e.g., an energy expert who requires data from scenario simulations for building renovation.). Metamodels and ontologies address the domain of the discourse, from two different perspectives (i.e., solution and problem domain), and in particular, ontologies may constitute a knowledge base compliant with the specified formalisation defined in the metamodel. An overview of the comparison of the main activities, with and without the employment of BIM/GIS metamodels, and ontologies is provided in Table 4.
Table 4. Comparison of main activity differences between a with and without BIM/GIS metamodel and ontology scenario.
Activity without BIM/GIS Metamodel and Ontology with BIM/GIS Metamodel and Ontology
Code generation Hindered potential in formalising, re-using and integrating code-generation activities. Metamodels provide a formal set of concepts and relationships to which BIM and GIS data models conform.
Information processing The majority of information needs to be interpreted by human agents. Software agents can interpret information and make inferences, thanks to ontologies.
Knowledge management and extraction Knowledge is spread about data models, documentation and other resources managed in databases or data lakes. A formal knowledge base is defined and can be linked or integrated with other ontologies, thanks to a common language.
Query potential BIM and GIS stay as separate systems or integrated, human-readable information systems only. Machine can understand the whole knowledge base provided by BIM and GIS and queries can be performed employing data from the two domains.
Integration of other data models Complex and without a high-level construct for shared compliance (i.e., metamodel). New concepts and relationships can be formally linked and are easier to conform.
Complex solution development Hard to design and to implement. Metamodels provide the high-level structure and ontologies can allow communication bridges between data (e.g., sensors data linked to BIM models).
Standardisation Difficult to contribute to the standardisation of new data models Since metamodels and ontologies heavily rely upon shared consensus, their development may lay the foundations for new standards.
The perspective of a BIM/GIS integration based upon metamodels and ontologies has implications for both the academic and professional context. In fact, from the review, ontologies and metamodels are discussed separately, but for a topic in which standardisation of data is a key concept, it may be an interesting topic to investigate in future studies. An assessment of the level of knowledge about IT and knowledge management concepts (and therefore metamodels and ontology) in the AECO/FM sector may provide a starting point to address the matter. In the professional context, the issue is relevant because companies and agencies commonly deal with unharmonised and heterogenous data. Their knowledge bases need to satisfy both business needs and external standards, and the introduction of a metamodel could provide a horizontal continuum between past and future data models and a vertical continuum between international, national and company standards.

This entry is adapted from the peer-reviewed paper 10.3390/su14020766

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