Automation in Construction 53 (2015) 1–12

Contents lists available at ScienceDirect

Automation in Construction

j ourna l homepage: www.e lsev ie r .com/ locate /autcon

Extending BIM interoperability to preconstruction operations usinggeospatial analyses and semantic web services

Ebrahim P. Karan a,⁎, Javier Irizarry b

a Department of applied engineering, Millersville University, PO Box 1002, Millersville, PA 17551-0302, USAb School of building construction, Georgia Institute of Technology, 280 Ferst Drive NW, Atlanta, GA 30332-0155, USA

⁎ Corresponding author. Tel.: +1 717 871 5142.E-mail addresses: ebrahim.karan@Millersville.edu (E.P

Javier.Irizarry@coa.gatech.edu (J. Irizarry).

http://dx.doi.org/10.1016/j.autcon.2015.02.0120926-5805/Published by Elsevier B.V.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 21 April 2014Received in revised form 29 November 2014Accepted 26 February 2015Available online xxxx

Keywords:BIMGISSemantic interoperabilitySemantic web technology

Since the early 2000s, building informationmodeling (BIM) has been used through the entire project life cycle tofacilitate effective project collaboration and integration of data to support project activities. Despite the successfulapplications of BIM in the design and construction stages, the use of BIM for preconstruction planning has notgained wide acceptance as in other project phases. The integration of BIM and geospatial analyses can offersubstantial benefits to manage the planning process during the design and preconstruction stages. However,this integration suffers from a lack of interoperability across the geospatial and BIM domains. Semantic web tech-nology is used in this study to convey meaning, which is interpretable by both construction project participantsaswell as BIM and geographic information systems (GIS) applications processing the transferred data. To achievethis, we first translate building's elements and GIS data into a semantic web data format. Then we use a set ofstandardized ontologies for construction operations to integrate and query the heterogeneous spatial and tempo-ral data. Finally, we use a query language to access and acquire the data in semantic web format. Through twoscenario examples, the potential usefulness of the proposed methodology is validated.

Published by Elsevier B.V.

1. Introduction

Many researchers have proved that preconstruction planning is oneof the key prerequisites for successful project delivery [19]. Decisionsmade during this stage have a significant impact on the successful exe-cution of the project and the efficiency and effectiveness of constructionoperations [48]. Good site layout planning assists in minimizingthe traveling time and movement costs of resources and improvesoperation productivity [44]. Temporary facilities such as equipment,site offices, and lay-down areas are located on jobsites to support con-struction operations. In response to these features, project managerscan benefit from the availability of such information within a buildingmodel [53]. A building information model is a data management plat-form that has a rich set of spatial features and building attributes [16].Since the early 2000s, building information modeling (BIM) has beenused through the entire project life cycle to facilitate effective projectcollaboration and integration of data and to support project activities.BIM was initially used for the planning and design phases of a projectand is now used in the construction phase for a wide range of applica-tions such as 4D simulation, clash detection, and providing detailed spa-tial and material quantities. Despite these successful applications, theuse of BIM for preconstruction planning has not gainedwide acceptance

. Karan),

as in other phases of the project. Most BIM tools are designed to handlea large number of permanent building objects and temporary objectshave received far less attention.

An approach using a geographic information system (GIS) is used toextract and manipulate data regarding topographic and existing condi-tions of building site terrain. Therefore, location and elevation informa-tion can be integratedwith the building’s orientation in the site. The GIShas been used successfully to solve the complexities of site layout plan-ning [11,43,55], for example, to assign a location to temporary facilities.The integration of BIM and GIS can offer substantial benefits to managethe planning process during the preconstruction stage. BIMprovides ge-ometry, spatial relationships, and quantities of building components,GIS can use them to support a wide range of spatial analysis used inan early phase of the procurement process, and BIM can visualize theresults of theGIS analyses in a 3D virtual world [23]. The data pertainingto the preconstruction phase can be obtained directly from the BIMmodel and then used for managing project logistics, detecting spatialand temporal conflicts, and visualizing construction engineering tasks.The digital model of the construction site terrain is usually generatedfirst using ground surveying methods or remote sensing techniques.The collected topographic data are transferred to a GIS environment tocreate a digital terrain model. We should define different parametersto control the visibility of a temporary building component in BIM andto detect the target objects in GIS.

Determining the location of temporary facilities involves closeness(or proximity) relationships among the temporary and permanent

2 E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

facilities. The closeness relationships such as “close to,” “far from,” and“next to” represent the site layout objectives inminimizing the travelingtime and improving safety. Far from (as opposed to close to) is usuallyapplied for the facilities that have an impact on safety issues(e.g., electrical equipment and possible sources of sparks should belocated far from flammable material). Formwork is placed next to theconcrete element. Also, some relationships can be represented in aquantitative manner such as “within specified distance.” It is necessaryto locate the supply points within operating radius of a tower crane.While we cannot directly apply query functionality of BIM to model oranalyze these relationships among the temporary structures and thebuilding elements, we can use GIS to analyze, monitor, and managethe large amount of spatial and non-spatial data involved in theconcepts of procurement and preconstruction management. There aregaps in the analysis and handling functionality available for BIM,concerning the formal spatial exploration of models [13]. For example,if we want to locate a welding shop far from office and not withinspecified distance of formwork storage area, a plug-in that enables usto perform a spatial filtering mechanism is needed in BIM software.The analysis becomes even more complex when we attempt to identifythe optimal location of welding shop on jobsite in terms of minimalmaterial delivery time. However, GIS tools can easily provide suchspatial query mechanism without any extra software component orprogramming effort.

Many of the preconstruction activities (e.g., site layout planning) donot fully take advantage of the benefits BIM provides to the design andconstruction practice, primarily because of the diversity of spatialrelationships between topographic and temporary objects in a BIMenvironment. In project planning, the topography and physical charac-teristics of the jobsite influences the layout of temporary facilities [54],preconstruction site activities, and even construction site safety plan-ning [4]. Moreover, the location of temporary facilities is closely relatedto the spatial characteristics of building elements and construction siteobstacles (e.g., overhead power lines, adjacent buildings). These factorsare a significant part of the overall construction work; however, theycannot be modeled with current BIM tools. As an alternative approach,a central or distributed database approach is used by many authors tomodel the spatial relationships between topographic and buildingobjects. Examples include the space database developed by Lee et al.[34] that contains BIMobjects and their space-use semantics, a databaseapproach incorporated by Chong and Phuah [12] for contractual issues,a spatial database developed by Kim et al. [30] for automating thegeneration of construction schedules by using spatial and quantitydata stored in BIM, and a spatial information integrated energy modeldesigned by Kim et al. [31] that retrieves geospatial data from a GISdatabase and displays geo-location characteristics specific to the respec-tive energy usage. Although GIS tools can be used to manage this diver-sity (e.g., model the construction site terrain and locate the temporaryobjects), a higher level of integration is needed to share informationwith and between BIM and GIS data sets. In 2006, the Open GeospatialConsortium (OGC) started an initiative for bridging the data modelsof the Architecture, Engineering and Construction (AEC) domains(e.g., CAD and BIM) with GIS workflows [33]. The major difficulty incombining BIM and GIS data is the incompatibility between these twotechnologies, the most obvious example being the modeling and refer-ence system because the GIS data are always geo-referenced and intwo or two-and-a-half dimensions (i.e., one z value per 2D images)while the BIM objects have their own local coordinate systems andthe third dimension is of common use [33].

The decision making process in a construction project is based onavailable information (usually extracted from different sources)coupled with the domain knowledge possessed by an individual. Eachrepresentation of an object or input in the individual’s mind is taggedwith a meaning. When making a decision, it is often not enough tomerely access information; rather, it is necessary to understand themeaning (or semantics) of the acquired information. For instance,

construction site topography is typically modeled within a GIS as arastermap that contains regular grid cells and elevation values assignedto each cell. However, BIM authoring tools do not support raster-basedterrain models. Even if the topographic data are transferred by otherdata formats (e.g., CSV file with x, y, and z coordinates of each recordedterrain point), their precise meaning is not understood by the BIM plat-form. Different classes (e.g., bare ground, vegetation, body of water, andnoise) in a GISmodel are considered identical (e.g., topography surface)in a building information model. Moreover, since BIM authoring toolsdo not support the geospatial analysis needed in the process of locat-ing temporary facilitates, GIS can be leveraged throughout thepreconstruction phase of a project. Again, even if the building data areexchanged by an interoperable format like Industry Foundation Classes(IFC), their precise meaning is not understood by the GIS platform.Because the use of this particular family of modeling language and theexistence of tools in other engineering domains is very limited [6],such application suffers from a lack of interoperability across the GISand BIM domains. Although many studies have been conducted tosolve the interoperability problems between BIM and GIS, a higherlevel of integration (i.e., semantic interoperability) is needed to shareinformation and their meanings with their data sets. Most of these pre-vious studies have primarily focused on BIM and GIS data conversionand transformation. For example, Wu and Hsieh [50] proposed anapproach to transform the geometric information of an IFC model intogeometric objects for the GML model. Döllner and Hagedorn [15] uti-lized the data integration capabilities of IFC to CityGML transformationin order to integrate data fromCAD, GIS, and BIM applications into a vir-tual 3D city model. Nagel et al. [35] provided conceptual requirementsfor the automatic transformation of IFC geometry to the different levelsof detail in CityGML. Laat and Berlo [32] developed an extension tomapIFC classes and their properties to CityGMLmodels and attributes. Hijaziet al. [21] presented an approach for mapping information from IFC toCityGML in order to model interior utilities within a GIS context.Although these approaches have provided a range of means to enabledata translation between BIM and GIS platforms, their applications arelimited to solve interoperability problems at the syntactic level. Sincethese efforts cannot guarantee that the resulting model reflects theintended meaning of the data, the user needs to have knowledgeabout both systems and their functionalities. Moreover, it is very timeconsuming and error prone for the user to figure out the meaning ofdata in the new system or to search and access the information by key-words. Due to the inconsistent level of details between IFC and CityGMLdata (e.g., non-existence of equivalent CityGML model for an IFC class),the syntax of two different BIM and GIS languages may never be fullytranslated.

McGraw Hill released a report in 2009 that stated a lack of interop-erability between software applications at the top of the list of areasthat need to be addressed to fully realize the benefits of BIM (Younget al., 2009). In order to fully integrate GIS and BIM, there is a need forsemantic interoperability solutions between these two technologies.Enabling interoperability at the semantic level is a key issue for bringingthe benefits of both technologies together into one integrated solution[24]. Semantic web technology is used in this paper to convey meaning,which is interpretable by both construction project participants as wellas BIM and GIS applications processing the transferred data. In the nextsection, a description of interoperability between BIMandGIS is provid-ed. How semantic web technology can be used to provide semanticinteroperability between BIM and GIS operations is also addressed.Then the proposed methodology is further described by means oftwo examples. Finally, the feasibility of semantic web techniques andpotential applications for construction industry are discussed.

2. Different Levels of Interoperability

The interoperability between BIM andGIS can be presented in differ-ent levels [8]. At the lowest level, users may connect to a host and

3E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

download files in a standard format or transfer data files between BIMand GIS systems. Users may also open files on other systems and displaythem in their native formats. However, the main disadvantage is thatusers are not able to search and manipulate distributed databases [20].The next level (i.e., syntactic interoperability) is about the data exchangebetween BIM and GIS systems by using a common data format. This levelprovides userswith the ability to directly access data andmethods from adifferent software program [29]. Some examples of syntactic interopera-bility between BIM and GIS are systems that combine building (or BIM)data with landscape (or GIS) maps [39,42,45], data formats that presentBIM data on GIS [26], and prototypes for mapping information from IFCto CityGML [9]. IFC is the most well-known interoperable format forexchanging data throughout the construction community. The IFC is de-veloped by buildingSMART® to support interoperability for buildingsand architecture. Although much of the IFC content is specific to thebuilding, significant efforts have been made to promote the scope ofIFCs to other civil engineering domains, such as GIS-based systems. Inthis manner, IFCs for geographic information systems (IFG) has been de-veloped to enable the exchange of geographic information inGISwith theIFC schema [10]. An example from the geospatial community is the geo-graphicmarkup language (GML) that is publishedby theOGC. Therehavebeen efforts by theOGC and industry associations in the 3Ddomain (suchas CityGML),which resulted inmany opportunities and discovered issuesrelated to CAD-GIS-BIM architecture [37,38]. While recent attempts tointegrate BIMs within CityGML models have value, CityGML has beenlimited in use to exterior buildings and their surroundings and thereforecannot be applied for building activities. A major problem in trying to in-tegrate BIM and GIS at this level of interoperability is that there aremanyheterogeneousfile formats for representing building andgeospatial infor-mation. In addition to this structural heterogeneity, it is also important tounderstand the semantic heterogeneity between BIM and GIS informa-tion. Although these file formats (e.g., IFC) still play a significant role inthe proposed approach, but they are used to represent the knowledge re-lated to building and geospatial objects and describe their interactionwith each other in the model. Using this semantic approach, we canunify the knowledge provided by BIM and GIS (as two heterogeneoussources) in the integrated model.

Semantic interoperability provides interoperability at the highestlevel, which is the ability to attach meaning to conventional concepts.This is used to structure and organize domain knowledge about an ob-ject or a phenomenon in such a way that software can automaticallyprocess and integrate large amount of informationwithout a predefinedinterface or human intervention [25]. The key to achieving interopera-bility at the semantic level is tomake sure that the relationship betweentwo different disciplines is maintained during data transfer [40]. Incomputer science, ontologies were adopted to define the standard tax-onomies and services to facilitate sharing and reuse of domain knowl-edge [14]. Ontologies provide a framework for representing, sharing,and managing domain knowledge through machine understandabledescriptions that define the objects (taxonomies) and their associativerelations across a domain (relationships) [17]. Enabling technologiessuch as the semantic web have provided standard taxonomies andontologies for GIS and construction knowledge. In this study,we consid-er the semantic web as a common framework for providing semanticinteroperability between BIM and GIS operations in order to transportmeaning that is interpretable both by (1) construction project partici-pants and (2) BIM–GIS applications processing the exchanged informa-tion. To achieve this, the information should be semantically describedand categorized in a standard way. The next section discusses theconcept and summarizes the various uses of the semantic web in theconstruction domain.

3. Interoperability through semantic web technology

There are different approaches for the exchange and sharing of BIMand GIS information across diverse resources, such as metadata for

topological models. However, it is not possible to search and retrievetemporal and geospatial data based on their content. For example, wecan search and access spatial and attribute data stored within electronicdrawings by keywords, but their content cannot be retrieved in thisprocess. Semantic ambiguities of different GIS and building data sourcesare one of the major obstacles to effective data interoperability. Forinstance, “obstructions near lay-down areas” may raise ambiguousinterpretations in the geospatial and the building domains. Obstruc-tions are composed of many permanent objects as well as temporarystructures located around the lay-down areas. Different metadatacreators may use different names for the obstructions. Also, thescales of the two entities, “obstructions” and “lay-down areas,” aremismatched. Most of such ambiguities can be resolved by means ofontology and utilizing the knowledge of operators. As all geographicobjects have their physical scales, different physical scales such as 0square meter, 10 square meter, and 100 square meter can be used tointerpret the semantically ambiguous words “obstructions” and“lay-down areas.” To model and visualize geospatial data in a BIMenvironment, the semantics of the GIS data set need to be sharedand delivered. This is done by annotating semantics of the GIS infor-mation to the shared reference ontology [51]. In this study, buildingand geospatial information are given semantics (or well-definedmeanings) by means of geospatial and AEC domain ontologies; thus,we can search and retrieve data by their content rather than just bykeywords in metadata.

Generally, semantic technologies aim at exchanging information in ameaningful way and with a minimum of human intervention. Due tothe nature and large amount of data needed to transfer between BIMand GIS systems, semantic web appears to be the best fit compared toother semantic technologies such as artificial intelligence, classification,and data mining. The nature of the information in the geospatial andAEC domains is decentralized and multidisciplinary, which hinders thewide adoption of artificial intelligence models by BIM and GIS practi-tioners. The integration of BIM and GIS is more about data than logicand reasoning in a centralized data model like artificial intelligence orexpert systems. Although classification technologies can help BIM orGIS users to quickly and efficiently retrieve small amounts of data,they are not likely to support the scale of data sharing between BIMand GIS applications. Data mining technologies can be used to analyzelarge amounts of data; however, the user needs to have knowledgeabout the data in order to supply the correct data andmake conclusionsout of the resulting data.

The semantic web technology represents a fundamentally new wayof formatting data that can be processed directly and indirectly by ma-chines [41]. According to the World Wide Web Consortium (W3C),the standard enables the description of information together with itsinherent semantics to be shared and reused across application andcommunity boundaries [47]. The primary difference between semanticweb technologies and other available solutions related to data (such asrelational databases or data fusion models) is that the semantic web isconcerned with the meaning and not the structure of data. It enablesus to add information relating different BIM and GIS databases andthus capture and understand the semantics from the data provider.Semantic web technologies have been used by several researchersto facilitate construction project information sharing. Anumba et al.[2] explored the use of semantic web technologies to meet the chal-lenges of collaborative project informationmanagement. Akinci et al.[1] developed a web-based approach to enable semantic interopera-bility between CAD and GIS platforms. Beetz [5] demonstrated thefeasibility of semantic web tool to address information exchangeand integration problems in AEC interoperability. Niknam and Karshenas[36] presented a new approach to construction cost estimating using thesemantic web technology. None of the previous models have demon-strated the potential application of semantic web as a common frame-work for providing semantic interoperability between BIM and GISoperations.

4 E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

To semantically search and integrate heterogeneous spatial andtemporal data between BIM and GIS, a set of standardized ontologiesfor both AEC and geospatial domains are needed [33]. The ontologiesspecify a set of classes, attributes, and relationships to providemean-ings for the vocabulary used in the domain of knowledge. If we con-sider a “class” as a group of things with something(s) in common(e.g., concrete), then unified identifiers are used to identify things.Also, we need to provide useful information such as attributes and rela-tionships (e.g., concrete has strength of 27,000 kPa) about the thingsusing standard formats. The standard data model that the semanticweb infrastructure uses to represent this distributed pool of data iscalled the resource description framework (RDF). The RDF data modelrepresents a relationship (or a predicate that donates a relationship)between a subject and an object. As an example, “The concrete hasstrength of 27,000 kPa” can be represented in RDF as this collection oftriples; a subject donates “concrete,” a predicate donating “has strengthof,” and anobject donating “27,000 kPa.”A subject (or an object)may belabeled with a short string, a uniform resource identifier (URI), which isa universally unique identifier. Although it is possible to create newURIs for all the resources and properties, but it is necessary to useonly universally unique URIs to link our data to data from third partiesand prevent the problem of co-reference.

4. Research methodology

The purpose of this study is to extend the interoperability of BIMauthoring tools in geospatial domain by employing semantic webtechnology. To achieve this, we first translate building's elements andGIS data into a semantic web data format, RDF. Considering the largenumber of applications and the nature of the information in BIM andGIS domains, we focus only on two scenario examples: (1) developmentof construction site topography using GIS and then modeling that in aBIM environment and (2) modeling of temporary facilities using BIMand locating them in a GIS environment. Then we use a set of standard-ized ontologies for preconstruction operations to integrate and querythe heterogeneous spatial and temporal data in RDF format. Finally,we use a query language to access and acquire the data.

Since RDF provides the meaning of data, it is an excellent comple-ment to extensible markup language (XML) that provides a flexibleway for the interchange of data between applications. Therefore, it isbetter to convert BIM and GIS data to XML-like formats, such as IFC forbuilding and GML for geospatial data formats. The IFC was designedand written using the EXPRESS schema that includes classes withprimitive types. An example is shown in Fig. 1, in which “Subject” isused for the modeling of physical element such as foundation or build-ing, “Property” is used to assign property to the subject (i.e., foundation)such as size, and “Value” or “Unit” is used to further specify the sizeproperty. GML defines features for physical entities (e.g., building,road) using simple properties such as names, integers, and Booleanvalues (true/false) and geometric properties such as Points, LineStrings,and Polygons. Because the original GML model was based on RDF, itcontains many features of RDF, including the idea of representing infor-mation in “striped form” by asserting values of properties on objects soelements alternately represent nodes and edges.

ENTITY foundation;

SUBTYPE OF (BuildingElement)

Size: OPTIONAL size;

WHERE

WR: size > 0;

END_ENTITY;

Subject

Property

Value

Fig. 1. EXPRESS entity example.

In the first scenario example, geospatial analyses are used mainly toretrieve height information about the construction site topography andto generate a geo-referenced data set. Most of the GIS are based on rela-tionalmodels that structure data according to tables of data, or relations.A GIS relational database is a set of relations (tables) containing a finitenumber of fields or attributes, but a semantic web agent uses descrip-tion logic to represent predicates between different classes. The startingpoint to transform a GIS relational database into RDF is to define rela-tional terminologies and identify their ontology equivalents. A relation(or a table) is organized into tuples (or rows) that have the same attri-butes (or columns). Each of the relations can be defined as a class (agroup of things that share some properties). A class is stated asowl:Class (a subclass of rdf:Class) if the terms are not already presentin the RDF schema. By this definition, all attributes associated with a re-lation can be represented as Properties. As mentioned earlier, an RDFproperty states a relationship between subjects and objects (or betweeninstances of a class). Object type properties are used when stating rela-tionships between instances of two classes. Otherwise, the relationshipsbetween instances of a class and values (e.g., number, string, etc.) aredefined using data type properties. Constraints are another importantfeature in a relational database. They allow us to restrict the possiblevalues for a given attribute. For instance, a constraint can restrict anelevation attribute to values between 300 and 350m.Most of these con-straints can be expressed by rules (or axioms) in RDF schema(e.g., SubClassOf, EquivalentClasses, DisjointClasses, etc.). All subjectsand objects defined in an RDF are called resources, so “rdfs:subClassOfrdf:resource” is used to state that one class is a subclass of anotherclass of resources. In relational database terminology, a primary key ofa relational table uniquely defines the characteristic of each tuple inthe table. The primary key is represented as functional property axiomin this study because a functional property can have only one (unique)value for each instance. Fig. 2 shows an example of such transformationwhere a relational database is first converted to GML and theninterpreted.

In the second scenario example, tower crane as a temporary facilityis modeled using BIM, and then its optimal location is identified in aGIS environment. In order to transform the building informationmodel and derive an RDF notation from the EXPRESS model, the ontol-ogy developed based on IFC for the building and construction sector isutilized. Since all the buildings information (e.g., materials, quantities,representations, units, etc.) are defined as a set of IFC entities, we firstdefine an OWL class named “Entity” for all IFC entity definitions(i.e., bowl:Class rdf:about= "…Entity"/N). Thenwe define the commonsupertypes of all other IFC entities as a subtype of this general class. TheIFC entities such as “IfcAddress,” “IfcMaterial,” “IfcPerson,” and “IfcRoot”are maximally general IFC classes at the same level of hierarchy. Forexample, IfcAddress is defined as follows:

bowl:Class rdf:about = "…IfcAddress"N

brdfs:subClassOf rdf:resource = "…Entity"/N

b/owl:ClassN

This RDF data model represents a relationship (i.e., subClassOf) be-tween a subject (i.e., IfcAddress) that is defined by “rdf:about” and anobject (i.e., Entity) that is defined by “rdf:resource.” We continue withthe subtype of each IFC class until no more IFC entity can be joined tothe ontology. For instance, “IfcAddress” is the supertype of only two en-tities; “IfcPostalAddress” and “IfcTelecomAddress,” both have zero childnodes (i.e., there is no subtype entities in the ontology). In addition tonormal attributes that are used to define an IFC entity, there are optionalattributes that give additional information about the element but theirvalue is optional (not always needed and can be assigned a nullvalue). We define the attribute as an OWL data type property becauseit relates literal data (e.g., strings, numbers, datatypes, etc.) to an IFCentity. The “IfcAddress,” for example, has three optional attributes;

OBJECTID X_COORD Y_COORD Z_VAL1 2223695.83 1377849.13 904.792 2223539.35 1377849.44 911.973 2223527.15 1377849.77 913.644 2223444.48 1377849.78 910.35 2223683.69 1377850.15 905.54

.

Primary key Attribute <ogr:FeatureCollectionxmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"…<gml:boundedBy><gml:Box><gml:coord><gml:X>-

<ogr:X_COORD>2223695.83</ogr:X_COORD><ogr:Y_COORD>1377849.13</ogr:Y_COORD><ogr:Z_VALUE>904.79</ogr:Z_VALUE> …

Class

Property

Functional Property

Rule

RDF/OWL syntax

Elevations relation GML

.

<owl:Class rdf:ID=Elevations">

<rdfs:subClassOf rdf:resource="sql;Relation"/>

</owl:Class>

<owl:DatatypeProperty rdf:ID="OBJECTID">

<rdf:type rdf:resource="&owl;FunctionalProperty" />

…<owl:DatatypeProperty rdf:ID="X_COORD

…<rdfs:range rdf:resource="xsd;double"/>

</owl:DatatypeProperty>

...

Fig. 2. Relational database to ontology database transformation example.

5E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

Purpose, Description, and UserDefinedPurpose. The attribute is definedas follows in the ontology:

bowl:DatatypeProperty rdf:about = "… Purpose "N

brdfs:domain rdf:resource = "…IfcAddress"/N

brdfs:range rdf:resource = "&xsd;string"/Nb/owl:DatatypePropertyN

The International Framework for Dictionaries (IFD) (ISO 12006–3)together with the latest version of the IFC standard, 2 × 4, provided ameans to describe the semantic. The IFD relates to the representationof the objects and their relationships. This allows all the informationin the IFC format to be tagged with a Globally Unique ID (GUID) [22].URI references are included in RDF models to describe subjects and ob-jects. One way to create globally unique URI references for the buildingcomponents is to use IfcGloballyUniqueId of the IFC object. Moreover,many of product manufacturers provide BIM models of their productsfor the users, which contain a URL back to the product webpage. TheURL can also be used as the URI to identify the term in the RDF syntax.

The geometry of each object in EXPRESS is represented by a set ofcomma separated values within parentheses after the name of the ob-ject. In order to accurately represent the building elements within GIScontext, spatial coordinates is transformed from local coordinatesystems to the real word coordinate system (i.e., geo-referenced) withthe aid of a coordinate transformation matrix. The axes of the localcoordinate system are rotated compared to the axes of the real-worldsystem and the origin of the local coordinate system is located at theorigin of the real-world system. For each subject definition in theEXPRESS schema, a corresponding rdf:datatype is created as a subclassof rdfs:Class. For instance, “IFCFooting(…,'Footing-Rectangular:4 x 4 x1…)” is transformed as “rdf:datatype = "http://www-.w3.org/2001/XMLSchema#string"- N Footing-Rectangular:4 x 4 x 1….” Asmentionedearlier, building components are represented as subjects or entitiesin EXPRESS. The subjects are identified using a hierarchical entitystructure, in which each entity is related with one other entity bysubtype/supertype relationships. For example, the IfcFooting is de-fined as subtype of the IfcBuildingElement. These “Subtype of” and“Supertype of” relationships are transformed into rdfs:SubClassOf

and rdfs:SuperClassOf relations. Following the same approach de-scribed above, the EXPRESS file is converted into its nearest equivalentin RDF. For example, all EXPRESS attributes associatedwith an entity arerepresented as Properties.

When building and geospatial data are represented in the RDFgraph, shared conceptualizations of domain knowledge can be annotat-ed with some ontology; thus, information about the triples will bestored. Although it is possible to determine type and properties ofobjects at this stage, it is generally not possible to add meaning tothose objects (or add semantics to the data) without ontology. So far,we related one object (or thing) to another using conversion models.The semantics to the entire RDF documents can be added with formalontologies, so the computers will understand the meanings of data. Atthis stage, different ontology editor tools are used to create and visualizeontology language and therefore donate how classes or entities areassociated with others. Protégé is one of the oldest andmost widely de-ployed ontology editing tools that is nowwidely used for RDFmodeling.In this study, Protégé 4.3 is used for building ontologies that implementsthe above approach. For the purpose of arranging the classes in a taxo-nomic (subclass–superclass) hierarchy, owl:Thing is defined as the topclass that contains individuals (things). In other words, all classes arerdfs:subclassOf owl:Thing. Since the construction site topography isthe main class in the geospatial ontology (first scenario example),rdf:type is used to state that topography is a member (or an instance)of a Class (note that a Class is stated as owl:Class if the terms are notalready present in the RDF schema). As a result, topography classhierarchy would be defined as follows:

Topography rdf:type owl:Class ;

rdfs:subClassOf owl:Thing .

Three classes of topography are used in this study: bare ground, veg-etation, and body of water. Bare ground and body of water are definedas physical, no distinct objects on the terrain having a plurality of curvedsurfaces. Vegetation, however, is defined as physical objects on theground that can be distinctly extracted from continuous gridded dataof elevation. Each such classification results in different sets of attributesdenoting properties of objects. A terrain surface can be described by a

6 E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

minimum of three points, given as Cartesian points that defined thegeometrical placement of the ground surface (or a land covered withwater). A new class, Terrain, is added to the ontology as a subclass oftopography (i.e., Terrain rdf:type owl:Class ; rdfs:subClassOf :Topogra-phy). Then ground surface and water object are defined as subclass ofthe Terrain. The geometrical properties of these two classes are definedby a resource from the Geonames ontology (URI: http://www.w3.org/2003/01/geo/wgs84_pos#Point). Using a coordinate system (e.g., WorldGeodetic System (WGS) 84), each point is uniquely defined by latitude,longitude, and elevation (or altitude) as follows:

rdfs:isDefinedBy rdf:resource = "http://www.w3.org/2003/01/geo/wgs84_pos#Point"

bgeo:latitude N latitude of the point b/geo:latitude N …

Vegetationobject is also defined as a subclass of the topographywitha relative placement in relation to the geometrical placement of theground surface and with a single value for its height. In this study, thisclass is defined using a point in a two-dimensional horizontal coordi-nates (x,y) and height (h). Additionally, vegetation object is locatedwithin a Cartesian coordinate system relative to a project origin. Inorder to make computers understand meaning of various terms acrossbuilding and geospatial domains, the goal of this stage of the proposedsystem is to develop a shared ontology. As a part of this shared ontology,geographic reference data are captured in a form consistent with thetopography that hosts the terrain and vegetation and a coordinatetransformation matrix is used to accurately position a local topographyorigin to the geographic reference data. If we adopt an IFC entity typeknown as IfcSite to represent Topography objects, IfcBuilding entitycan be used to define man-made structures on the terrain. TheIfcBuilding is used to describe the spatial properties of a building suchas height, all areas covered by the building, and volume of all areasenclosed by the building. Using the developed shared ontology, thespatial elements of the building are positioned geospatially relative tothe construction project origin that has been defined as the geographicreference system. Fig. 3 shows the shared ontology developed for thisstudy, in which building domain ontology is partially developed. Itshould be noted that developing the taxonomies for AEC domain isbeyond the scope of this research.

Once semantics are added to RDF data, a query language can then beused to access and acquire the data. SPARQL is the standardized querylanguage for semantic web, which retrieves and manipulates the data

Fig. 3. Preconstruction operation ontology (partially dev

stored in RDF format. A SPARQL query consists of a set of triples likeRDF triples except that each of the subject, predicate, and object maybe a variable. The URIs in RDF data are necessary to identify whichdata to retrieve. In this study, we declared some prefixes to representdifferent namespace URIs. After declaring the prefix point: refers tothe namespace http://www.w3.org/2003/01/geo/wgs84_pos#Point,anywhere in SPARQL we use the prefixed name instead of entirenamespace URI. A typical SPARQL query looks something like “selectDISTINCT?veg where {?veg a Vegetation ; geo: latitude?latitude ;geo:longitude?longitude … }.” This query would return the latitudeand longitude components of subjects defined as Vegetation class inthe database. The DISTINCT keyword prevents the SPARQL processorfrom showing duplicate answers. A constraint, expressed by the key-word FILTER, instructs the query processor to only select data thatmeets very specific criteria. A collection of elevation points for a givenarea would have the same x and y values, but their z value would bedifferent. The elevation values for vegetation cover and man-madestructures are higher than the bare earth surface. Therefore, differentclasses of topography are identified and isolated from the bare earthterrain by the FILTER statement.

With regard to integration of BIM and GIS data, it has beenestablished that the level of detail of geospatial information is not com-patible to the BIM systems that are used for preconstruction operations.In IFC, for example, there is no concept of specific Body of Water objectas in GIS terminology. The OPTIONAL keyword, which gives the querythe flexibility to retrieve data that may or may not match every singletriple pattern, is used to resolve these incompatibilities between IFCand GML classification schemes. This clause is similar to the concept ofouter join for relational database, in which a query lists all tuples inone relation that does not have corresponding data in another relation.In addition, the query languages available today for BIM models do notallow the application of qualitative spatial relationships between build-ing components to select individual attributes (e.g., SELECT all concretewalls within the first floor). If the standard set of operators is notsufficient for the needs of spatial query language, custom functions aredefined in FILTER expressions so that queries can be used for buildinginformation models.

The format of query output is often considered the most importantcomponent of a SPARQL query result. Generally, the schema or ontologyused to generate the data is different than the one utilized to use thedata. As a result, the development and standardization of integratedSPARQL outputs is one of the main challenges that the current study

eloped for topography and building components).

7E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

needs to address. Although there are several formats to represent thequery results, there is no universally accepted IFC-compatible stan-dard to represent the information. Perhaps the most appropriateway to get SPARQL results is to use the SPARQL Query Results XMLFormat, so all of the XML-based tools and processors can use thequery results. Another format that is useful in the application scenar-ios used in this paper is the Query Results CSV Format, whichexpresses the results of a SPARQL query as table with the CSV headerline, followed by one line for each query solution. Each set of queryresults must be converted into a more readable format that is com-patible with the BIM or GIS tools. This is discussed in detail in thenext section.

5. Case study

There are many possible use cases where information from buildingmodels will be of use in the geospatial environment and vice versa. Twouse case scenarios are presented to demonstrate the usefulness ofsemantic web technology for integrating BIM and GIS during thepreconstruction stage. The application of the proposed method is notlimited to these examples, and many other interoperability problemsin BIM–GIS integration can be approached by the steps explained inthis paper (e.g., make material supplier’s information available and un-derstandable to contractors through semantic web services). Even ifsome BIM-related information are transferred to GIS or vice versa(e.g., using a data conversion tool), there is no guarantee that anothersystem can interpret the data being transferred. One of the bestuse cases for semantic web technology is probably large-scale BIM andGIS data integration across institutional and national boundaries(e.g., emergency response and disaster management). Fig. 4 shows

Fig. 4. BIM and GIS integration for preconstructi

the overall system architecture for developing a digital model of con-struction site topography using GIS, followed bymodeling of temporaryfacilities using BIM and finally locating them in a GIS environment. Thedigital model of the construction site terrain is currently being used inseveral application areas such as volumetric calculations in cut-and-fillproblems, route planning of vehicles for earthmoving projects,visualization of construction operations, and site layout planning. Digitalelevation models (DEMs) can be acquired through terrestrial methods(e.g., ground surveying) and remotely sensed data (e.g., satellite images,airborne LiDAR, etc.). A more detailed overview of how terrestrial andremotely sensed data may be used to generate DEMs, can be found inKaran et al. [27].

To retrieve height information about the construction site topogra-phy, we followed a similar approach as Karan et al. [27], in which GISanalyses are used to generate a geo-referenced model of the existingconditions of the construction site terrain. The study area is locatednorthwest of Atlanta, Georgia, with an area of 51,270 m2 (12.67acres), and with average elevation of 284 meter (933 feet). The site iscovered with light vegetation and is surrounded on three sides byroads and on one side (i.e., south) by railroad tracks. Because there isno standardized format to exchange data between different BIM andGIS software, the precise meaning of the topographic data is not under-stood by the BIM platform. For instance, Fig. 5 shows visual representa-tions of the construction site topography in two different environments.The screenshot on the left shows the digitalmodel as a raster model in aGIS environment, while that on the right shows the (same) model in aBIM environment. The GIS model contains different classes of landcover including bare ground, vegetation, man-made structure, andbody of water, but they are all represented as a single topographysurface class in the BIM model.

on operations using semantic web services.

Fig. 5. Topography as raster format in GIS environment (left) and as surface model in BIM environment (right).

Table 1Translation of a small portion of query results in XML format.

Original XML format ifcXML schema

bvariable name = "Vegetation" /N…

brdfs:subClassOf …N

…

bvariable name = "host" /N …

bvariable name = " geo:latitude " /N …

bvariable name = " geo:longtitude " /N …

bvariable name = " height " /N…

…

bxs:element name = "Vegetation" /N …

substitutionGroup = …

type = "ifc:IfcElementCompositionEnum" …b/xs:elementNbxs:element name = "IfcObjectPlacement" …type…bxs:attribute name = "RefLatitude" …type…bxs:attribute name = "RefLongitude" …type…bxs:attribute name = "RefElevation" …type……

8 E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

Because the DEM is developed as a raster model, we need to trans-form its relational database into RDF by annotating the properties ofthe raster model such as the cell size, class type, the number of rowsand columns, and the coordinates of its origin. The elevation data areextracted from the relational database and transformed into GML. Theconversion step makes use of an available application programminginterfaces (APIs) such as GeoTools for the manipulation and parsing ofGML data and cwm (pronounced koom) for parsing the IFC files intoRDF format. The GeoTools is a Java API developed and maintained bytheOpen SourceGeospatial Foundation,whichprovides standards com-pliant methods for themanipulation of GIS data [18]. CWM is a general-purpose data processor developed by theW3C for querying and filteringRDF information. It should be noted that the available APIs only providepart of the adaptation required for the conversion of RDF and GML filesinto RDF triples (e.g., interfaces for basic RDF entities), and the proposedmethod is independent of any specific software package. The ontology(as described in the research methodology section) is needed to definethe classes and their association. According to the developed ontology(see Fig. 3), bare ground, vegetation, and body of water are defied asdifferent classes of topography in this study.

The Jena ARQ, which is a query engine for Jena that supports theSPARQL RDF Query language, is used to retrieve the data required forsurface modeling from the related RDF file [3]. Jena is an open sourceJava framework for building semantic web applications. To create BIMtopographical models of the existing construction site, the relatedparameters such as the classes used in the data, latitude/longitude prop-erties, physical quantities, axioms, and other annotations defined in thedata are obtained through query (e.g., SELECT DISTINCT?class WHERE{?class rdfs:subClassOf Bare Ground}). As described earlier, the site to-pography is modeled as a raster map that contains regular grid cells(which uniquely defined by latitude, longitude) and elevation valuesassigned to each cell. Depending on the type of class, a grid cell mighthave two elevation values; altitude for all elements in the Terrain classandheight for theVegetation class. Considering thatmanyBIM softwaretools like Graphisoft ArchiCAD and Autodesk Revit support CSV input,this format is used to express the results of SPARQL queries for bareground and body of water classes. To generate the topography model,a CSV file with x, y, and z coordinates of each recorded topographypoint is used as the output of SPAQRL queries. Using the geometries inthe CSV format, a terrain model is created as triangulated irregularnetwork (TIN) surfaces in the BIM environment.

However, it is not easy to directly transfer the results of SPARQLqueries for vegetation class. In this study, the XML format is used toreturn the results of SPARQL queries to the building model. Currently,an XML document is not supported by BIM authoring tools; thus, weapplied our proposed conversion methodology to transform the resultsto an XML representation of IFC data, ifcXML (e.g., substitutionGroup asthe semantic relation SubClassOf). Table 1 indicates the mappingbetween a small portion of the XML results and an ifcXML schema. Incertain cases, some additional components might be missing withinthe XML code since the RDF file might not be detailed enough. The

equivalent IFC entity (e.g., IfcSite) is probably the most obvious andalso the most important part that may be specified by the user. In addi-tion, a predefined container structure including header information,unit(s) of serialization, and mapping of EXPRESS entities and attributesfor the ifcXML documents is defined and used to translate and fill allquery results into an ifcXML document corresponding to the givenIFC entity. For the vegetation class, we make use of the SPARQL“CONSTRUCT” query form to create new graphs and to represent avegetation element itself (equal to xs: element in ifcXML) and a neigh-boring bare ground sharing common latitude and longitude values as itshost. In other words, we define an explicit dependency between eachvegetation element and a host element where we can place the vegeta-tion element. To generate the topography model, all elements in theTerrain class must first be placed in BIM and then the Vegetation classcan be imported.

Having developed the construction site topography in the BIM envi-ronment, the next step is tomodel temporary facilities and integrate thesite model and the building together into a single environment. Thebuilding’s spatial geometry aswell as thematerial being used is definedat this stage. To locate tower cranes, as an example of temporaryfacilities, in a GIS environment we represented the locations of supply(loading) and demand (unloading) points by the centroid of an areawhere the material components are assigned. In this study, Autodesk®Revit Architecture 2013was used to develop the buildingmodel. Detailsabout modeling and locating tower cranes on construction site can befound in Irizarry andKaran [23]. IFC is used as the data repository for ad-dressing geometry, relations and attributes of the BIMmodel. In the casestudy, the proposed methodology is applied to convert the IFC file toRDF format. This process takes advantage of available URIs to annotatethe EXPRESS entities and relations among them [46]. The generatedRDF file is imported into the ontology editor environment. Consequent-ly the ontology is evaluated and revised.

For the purpose of determining the geometric layout of supply anddemand points with their maximum load, SPARQL is used to issuequeries based on the RDF such as “select Class: ifc:IfcBuildingStorey toget all the concrete slabs located in a given story,” “select Class:

9E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

ifc:IfcLocalPlacement to determine the local coordinate system of agiven slab,” and “select Class: ifc:IfcQuantityWeight to get the totalweight of the slab.”We used “IfcQuantityWeight” to provide weight in-formation of supply and demand points. Considering that CSV is one ofthe several table formats that GIS software can read, the results ofSPARQL queries for locating tower crane is expressed as CSV formatthat has x, y, and z coordinate representation of each supply and de-mand point. This CSV file became the input to the GIS software, ESRI®ArcGIS. This CSV file containsmore than 150 supply and demandpoints,posted in WGS84 datum. Considering the lifting capacity of each crane,feasible areas for locating the tower cranes are categorized under thecriterion of minimized possibility of conflicts between tower cranesand other facilities.

Fig. 6 shows the processes included in the research methodology forthe “IfcQuantityWeight” element, including the EXPRESS and IFC data

ENTITY IfcElementQuantity;

SUBTYPE OF (IfcPropertySetD

Name: OPTIONAL Base Quantities: SET [1:?] O

END_ENTITY;

#... = IFCELEMENTQUANTITY(Global

Description, …, (#[line number of IfcP

#... = IFCQUANTITYLENGTH('Weight

rdf:Label Name Description

PhysicalQuantity Weight null

… … …

IFCELEMENTQUANTITY(GIFCELEMENTQUANTITY

Ifcff ElementQuantitytt ;IfcElementQuantity

EX

PR

ES

S E

nti

ty

IFC

Cla

ss

BIM Environment

RDF/OWL Classes

<owl:Class

rdf:about="…IfcElementQuantity"

<rdfs:subClassOf rdf:resource=

"…IfcPropertySetDe�inition"/>

</owl:Class>

<owl:DatatypeProperty

…Quantities">…

<hasSetOf

rdf:resource="…

IfcPhysicalQuantity"/>

</owl:DatatypeProperty

<owl:DatatypeProperty rdf:about="…WeightValu

<rdfs:domain rdf:resource="…IfcQuantityLengt

<owl:NamedIndividual rdf:about="…WeightValu

<…WeightValue …datatype="…real">750</…W

GIS Environment

Fig. 6. Semantic transformation of we

models in a BIMenvironment, the resulting RDF graph andOWLClasses,and the descriptive attributes in a GIS environment. Thewords in italicsare attribute values, and their modeling environments are shown inbold. We use “named individual” axioms to declare that a given entityis an individual. The CSV format for expressing the results of a SPARQLselect query is useful in the case study scenario. This header row willbe used as the headers of each field in the attribute tables in a GIS envi-ronment. Up to this point, the physical properties of supply and demandpoints defined in the BIM model are combined together in the GISmodel.

Fig. 7 shows the four steps of modeling the topography and thebuilding in a graphical way. The aim of these two scenario exampleswas to demonstrate the feasibility of using semantic web techniquesfor transforming preconstruction-related information back and forthbetween BIM and GIS modelling environments.

e�inition);

Quantities ;

F IfcPhysicalQuantity;

ID, OwnerHistory, 'BaseQuantities' ,

hysicalQuantity goes here));

', Description, 'lb', 750);

Unit Value

lb 750 … …

Attribute

Values

<owl:NamedIndividual

rdf:about="…Name">

<…Name rdf:datatype="…

string"> BaseQuantities

</…Name>

</owl:NamedIndividual>

e">

h"/>…</owl:DatatypeProperty>…

e">

eightValue> …</owl:NamedIndividual>

ight property from BIM into GIS.

Fig. 7. Four process steps and corresponding outputs in case study.

10 E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

6. Validation

After writing and editing the ontology in a machine-processableontology language, the syntax of the RDF models was validated usingthe RDF validation service at www.w3.org/RDF/Validator. Although

Vegetation

Bare Ground

Water

Obst

Geospatial Knowledge

Vegetation

Bare Ground

Water

Obst

BIM Environment

Relational Data >> RDF/OWL Classes >Ontologies >> SPARQL XML Results >>

BIM Environment

Bare Ground

Raster >> Points Data >> CSV >> Re-Create

the Model in BIM

Fig. 8. Comparison between the semantic (proposed method) and syntactic (curre

the lightweight ontology was carefully verified and validated usingthe ontology editor tool, the results of the query need to be used to en-sure its quality and conformance to standards. That is, we should be ableto verify and validate the models based on the query output in the BIMenvironment. The primary objective of this study was to extend the

ructions

Facility Location

Facility Type

ructions

Facility Location

Facility Type

> Mapping BIM and GIS IfcXML >> 3D BIM Model

Facility Location

Vector >> CAD Data >> Annotation >> Import into BIM as 2D model

Sem

anti

c In

tero

pera

bilit

y

usin

g th

e M

etho

dolo

gySy

ntac

tic

Inte

rope

rabi

lity

(C

urre

nt P

ract

ice)

nt practice) interoperability of GIS data exchanged in the use case examples.

11E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

semantic interoperability between the BIM and the geospatial domains.Therefore, current practice and the proposed methodology are used inparallel to verify the results of the study. As can be seen from Fig. 8,the geospatial ontology is semantically richer than the BIM otology interms of the topographic features; thus, we cannot retrieve most ofthe concepts defined in a GIS model due to missing semantic informa-tion attached with GIS models (syntactic interoperability). In thecurrent practice, the bare ground concept should be represented as aset of data points and then re-created in the BIMenvironment. The facil-ity location should be represented as 2D CAD data and then annotatedwith appropriate keywords. The BIM user can search the data just bythose keywords and retrieve the geometry as a 2D drawing. The exam-ples described in this paper show how the research methodologyenables us to transfer topographic features and relationships betweenthese features to BIM. In contrast to the current methods, we couldretrieve all the topography classes defined in the GIS model. One ofthe effective methods of validating the results of the proposed methodis to load the resulting IFC models into a BIM authoring tool. Note thatthe resulting ifcXML files have been successfully loaded into the BIMauthoring tools.

7. Limitations of system

Since semantic web technologies are still developing and maturing,it is often time consuming to find efficient ways of using these technol-ogies. As can be seen from this study, there are very few globally agreedontologies available for the construction domain. Hence, multidisciplin-ary professionals involved in AEC projects develop their own ontologiesindependently that limits the effective transfer of information amongproject teammembers. GML is used in the proposedmethodology to ex-press geographic information in a manner that can be readily encodedand shared. However, GML is a text-based document format whichmakes it incredibly inefficient for network, processor and storageperformances. One of the ways to overcome this performance barrieris to use specialized hardware accelerators, use compression ap-proaches, and use binary GML to replace the text-based GML format[52]. To do this, a GMLdocument is transformed into a parser data struc-ture that separates the syntax structure of the GML document from itscontent. According to the syntactic structures (e.g., start of each GMLelement, represented by the element name), the content is groupedby events into different blocks where each block has a head includingthe coordinates of the geometric bounding box specially. This compactrepresentation of the GML document can be converted into the populargeneral compressor gzip [49].

Because an RDF file contains a bunch of information about all of theentries of each resource, converting building or GIS data to RDF tends toproduce too large outputs. Keeping this very large number of triples inone big file may not be the best option to query and retrieve the databecause it reads the entire data set for each query. One way is to use adatabase manager optimized for RDF triples; however, this concept isnot applied in this study as the aim of the study is to investigate thefeasibility of applying semantic web technology to enable semanticinteroperability between BIM and GIS. The same problem applied toifcXML files because they are very long and complex files even forvery simple query results. The SPARQL result used in this paperconsisted of only eight variables, yet the ifcXML file was almost 3,000lines of code.

In many cases, the lack of integrated SPARQL outputs is one of themain challenges that the current study had to address. Although thereare several formats to represent the query results, there is nouniversallyaccepted IFC-compatible standard to represent the information. In thisstudy, the ifcXML format is used to represent the results of SPARQLqueries. The language binding between the XML format of SPARQLqueries and the ifcXML is not always deterministic; there are caseswhere the same XML component can be mapped into different ifcXMLcomponents while keeping the same semantic content.

8. Conclusions and future work

BIMwas initially used for theplanning and designphases of a projectand is now used in the construction phase for a wide range of applica-tions. Despite these successful applications, the use of BIM forpreconstruction planning has not gained wide acceptance as in otherphases of the project. Although spatial analysis tools such as GIS canbe used to extend BIM implementation during the preconstructionphase, a higher level of integration is needed to share informationwith and between BIM and GIS data sets. Enabling interoperability atthe semantic level is an important issue for the link between BIM andGIS. Semantic web technology is used in this study to convey meaning,which is interpretable by both construction project participants as wellas BIM and GIS applications processing the transferred data. The poten-tial usefulness of the proposed methodology was validated through acase study. The development of construction site topography usingGIS and thenmodeling that in a BIMenvironment andmodeling of tem-porary facilities using BIM and locating them in a GIS environmentweretwo examples utilized in this study. The proposed interoperabilitymechanism in this study is not limited to only preconstruction phaseof a project. The authors have extended the methodology to managethe maintenance and repair processes of facility management, as oneof the last stages of a project [28]. Toward enabling BIM and GIS integra-tion at the semantic level, future/ongoing research effort by the authorsincludes the development of an interoperable framework for managingconstruction supply chain during the construction stage.

The case study results of the proposed methodology demonstratethat semantic web technology can be a way to enable semantic interop-erability between building and geospatial heterogeneous data. As thefirst step, all spatial and non-spatial content is represented as RDFtriples. Then a set of standardized ontologies for construction andgeospatial domains are used to integrate and query the heterogeneousspatial and temporal data in semantic web data format. Finally,SPARQL query language is used to access and acquire the data. Althoughthe methodology demonstrates how to represent IFC contents as RDFtriples, one of the future improvements of this work is to developtools for automatic generation of semantic data models based on IFCmodels. The format of query output is often considered themost impor-tant component of SPARQL query result. Because there is no IFC-compatible standard to represent the query results, future work shouldfocus on the development of an interface for query and access toontology-based web services. This will allow BIM users to query andaccess building and geospatial data at any time over the web fromdata providers.

Linked data, as a concept that ariseswithin the paradigmof semanticweb, help to overcome interoperability challenges to enhance informa-tion exchange in the AEC domain. There are four principles to publishour BIM andGIS data as linked data [7]: (1) useURIs as names for things,(2) use the standards (e.g., RDF, SPARQL) to declare a list of terms andtheir relationships, (3) use HTTP URIs to describe the resource that theURI identifies, and (4) include links to other URIs so various RDF vocab-ularies have a specific relationship to another resource on the web. Thispaper focuses only on the first two principles to publish data in a formthat machines can naturally understand. Future work can extend thismethodology to consider all principles of linked data.

References

[1] B. Akinci, H. Karimi, A. Pradhan, C.-C. Wu, G. Fichtl, CAD and GIS interoperabilitythrough semantic web services, J. Inf. Technol. Constr. 13 (2008) 39–55.

[2] C. Anumba, J. Pan, R. Issa, I. Mutis, Collaborative project information management ina semantic web environment, Eng. Constr. Archit. Manag. 15 (1) (2008) 78–94.

[3] ARQ, ARQ - A SPARQL Processor for Jena, vol. 20132011.[4] V.K. Bansal, Application of geographic information systems in construction safety

planning, Int. J. Proj. Manag. 29 (1) (2011) 66–77.[5] J. Beetz, Facilitating distributed collaboration in the AEC/FM sector using semantic

web technologies, Eindhoven University of Technology, 2009.

12 E.P. Karan, J. Irizarry / Automation in Construction 53 (2015) 1–12

[6] J. Beetz, J. Van Leeuwen, B. De Vries, IfcOWL: a case of transforming EXPRESSschemas into ontologies, Artif. Intell. Eng. Des. Anal. Manuf. 23 (01) (2009) 89–101.

[7] T. Berners-Lee, Linked data, vol. 20132006.[8] Y. Bishr, Overcoming the semantic and other barriers to GIS interoperability, Int. J.

Geogr. Inf. Sci. 12 (4) (1998) 299–314.[9] A. Borrmann, T.H. Kolbe, A. Donaubauer, H. Steuer, J.R. Jubierre, M. Flurl, Multi‐scale

geometric‐semantic modeling of shield tunnels for GIS and BIM applications,Comput. Aided Civ. Infrastruct. Eng. 30 (4) (2015) 263–281.

[10] BuildingSmart, BuildingSmart-IAI International. International Alliance for Interoper-ability, IFC for GIS, vol. 20132012.

[11] M. Cheng, J. O'Connor, ArcSite: enhanced GIS for construction site layout, J. Constr.Eng. Manag. 122 (4) (1996) 329–336.

[12] H.-Y. Chong, T.-H. Phuah, Incorporation of database approach into contractualissues: methodology and practical guide for organizations, Autom. Constr. 31 (0)(2013) 149–157.

[13] S. Daum, A. Borrmann, Processing of topological BIM queries using boundary repre-sentation based methods, Adv. Eng. Inform. 28 (2014) 272–286.

[14] J. Davies, D. Fensel, F.V. Harmelen, Towards the Semantic Web: Ontology-DrivenKnowledge Management, John Wiley & Sons, Chichester, UK, 2003.

[15] J. Döllner, B. Hagedorn, Integrating urban GIS, CAD, and BIM data by servicebasedvirtual 3D city models, Urban and Regional Data Management- UDMS Annual2007, Stuttgart, Germany, 2007, pp. 157–160.

[16] C. Eastman, P. Teicholz, R. Sacks, K. Liston, BIM Handbook: A Guide to Building Infor-mation Modeling for Owners, Managers, Designers, Engineers and Contractors, JohnWiley & Sons, 2011.

[17] T. El-Diraby, C. Lima, B. Feis, Domain taxonomy for construction concepts: toward aformal ontology for construction knowledge, J. Comput. Civ. Eng. 19 (4) (2005)394–406.

[18] GeoTools, GeoTools, vol. 2013, Open Source Geospatial Foundation, 2013.[19] K. Gidado, Enhancing the prime contractor's pre-construction planning, J. Constr.

Res. 5 (01) (2004) 87–106.[20] M. Goodchild, M. Egenhofer, R. Fegeas, C. Kottman, Interoperating geographic infor-

mation systems, Kluwer Academic Publishers, Norwell, Massachusetts, 1999.[21] I. Hijazi, M. Ehlers, S. Zlatanova, T. Becker, L. van Berlo, Initial investigations for

modeling interior utilities within 3D geo context: transforming IFC-interior utilityto CityGML/UtilityNetworkADE, advances in 3DGeo-Information Sciences, Springer,2011. 95–113.

[22] IFD, International Framework for Dictionaries, vol. 20132008.[23] J. Irizarry, E.P. Karan, Optimizing location of tower cranes on construction sites

through GIS and BIM integration, J. Inf. Technol. Constr. 17 (2012) 351–366.[24] J. Irizarry, E.P. Karan, F. Jalaei, Integrating BIM andGIS to improve the visualmonitor-

ing of construction supply chain management, Autom. Constr. 31 (2013) 241–254.[25] Y. Kalfoglou, Cases on semantic interoperability for information systems integration:

practices and applications, Information Science Reference, Hershey, Pennsylvania,2009.

[26] T.W. Kang, C.H. Hong, A study on the lightweight BIM shape format (LBSF) structuredevelopment to represent the large volume BIM geometry objects based on GIS asthe viewpoint of the building facility management, J. Korea Spat. Inf. Soc. 21 (3)(2013) 79–87.

[27] E. Karan, R. Sivakumar, J. Irizarry, S. Guhathakurta, Digital modeling of constructionsite terrain using remotely sensed data and geographic information systems analyses,J. Constr. Eng. Manag. 140 (3) (2014) 04013067.

[28] E.P. Karan, J. Irizarry, Developing a Spatial Data Framework for Facility ManagementSupply Chains, Construction Research Congress 2014—Construction in a GlobalNetwork, ASCE, Atlanta, GA, 2014. 2355–2364.

[29] H.A. Karimi, B. Akinci, CAD and GIS integration, Auerbach Publications, Boca Raton,Florida, 2009.

[30] H. Kim, K. Anderson, S. Lee, J. Hildreth, Generating construction schedules throughautomatic data extraction using open BIM (building informationmodeling) technol-ogy, Autom. Constr. 35 (0) (2013) 285–295.

[31] S.A. Kim, D. Shin, Y. Choe, T. Seibert, S.P. Walz, Integrated energy monitoring andvisualization system for Smart Green City development: designing a spatialinformation integrated energy monitoring model in the context of massive datamanagement on a web based platform, Autom. Constr. 22 (0) (2012) 51–59.

[32] R. de Laat, L. van Berlo, Integration of BIM and GIS: The Development of the CityGMLGeoBIM Extension, in: T.H.K. Kolbe, G., C. Nagel (Eds.),Advances in 3D Geo-Information Sciences, Berlin, Germany, 2011, pp. 211–225.

[33] A. Lapierre, P. Cote, Using Open Web Services for Urban Data Management: ATestbed Resulting from an OGC Initiative for Offering Standard CAD/GIS/BIM Ser-vices, in: M. Rumor, V. Coors, I. Fendel, S. Zlatanova (Eds.),Urban and RegionalData Management. Annual Symposium of the Urban Data Management Society,Stuttgart, Germany, 2007, pp. 381–393.

[34] J.-K. Lee, J. Lee, Y.-s. Jeong, H. Sheward, P. Sanguinetti, S. Abdelmohsen, C.M.Eastman, Development of space database for automated building design reviewsystems, Autom. Constr. 24 (0) (2012) 203–212.

[35] C. Nagel, A. Stadler, T.H. Kolbe, Conceptual requirements for the automatic recon-struction of building information models from uninterpreted 3D models, Proceed-ings of the International Archives of Photogrammetry, Remote Sensing and SpatialInformation Sciences, Vancouver, Canada, 2009. 46–53.

[36] M. Niknam, S. Karshenas, A Semantic Web Service Approach to Construction CostEstimating, ASCE International Workshop on Computing in Civil Engineering, LosAngeles, CA, 2013. 484–491.

[37] OGC, OGC Web Services Architecture for CAD GIS and BIM, vol. 20132007.[38] OGC, Open Geospatial Consortium. OpenGIS® City Geography Markup Language

(CityGML) Encoding Standard, vol. 20132008.[39] T. Park, T. Kang, Y. Lee, K. Seo, Project Cost Estimation of National Road in Prelimi-

nary Feasibility Stage Using BIM/GIS Platform, 2014 International Conference onComputing in Civil and Building Engineering, Orlando, FL, 2014, pp. 423–430.

[40] R. Peachavanish, H.A. Karimi, B. Akinci, F. Boukamp, An ontological engineeringapproach for integrating CAD and GIS in support of infrastructure management,Adv. Eng. Inform. 20 (1) (2006) 71–88.

[41] G. Schraw, Promoting general metacognitive awareness, Instr. Sci. 26 (1–2) (1998)113–125.

[42] R. Sebastian, M. Böhms, P. van den Helm, BIM and GIS for low-disturbance construc-tion, Proceedings of the 13th International Conference on Construction Applicationsof Virtual Reality London, UK, 2013, pp. 469–479.

[43] M.H. Sebt, E.P. Karan, M.R. Delavar, Potential application of GIS to layout of construc-tion temporary facilities, Int. J. Civ. Eng. 6 (4) (2008) 235–245.

[44] C. Tam, T.K. Tong, A.W. Leung, G.W. Chiu, Site layout planning using nonstructuralfuzzy decision support system, J. Constr. Eng. Manag. 128 (3) (2002) 220–231.

[45] L. Van Berlo, T. Dijkmans, J. Stoter, Experiment for integrating dutch 3D spatialplanning and BIM for checking building permits, ISPRS annals of photogrammetry,remote sensing and spatial information sciences, Istanbul, Turkey2013. 279–284.

[46] D. Van Deursen, IFC2X3 ontology, vol. 20132010.[47] N. Vos, H. van der Meijden, E. Denessen, Effects of constructing versus playing an

educational game on student motivation and deep learning strategy use, Comput.Educ. 56 (1) (2011) 127–137.

[48] A.F. Waly, W.Y. Thabet, A virtual construction environment for preconstructionplanning, Autom. Constr. 12 (2) (2003) 139–154.

[49] Q. Wei, A query-friendly compression for GML documents, database systems foradvanced applications, Springer, 2011. 77–88.

[50] I.C. Wu, S.H. Hsieh, Transformation from IFC data model to GML data model:methodology and tool development, J. Chin. Inst. Eng. 30 (6) (2007) 1085–1090.

[51] Q. Yang, Y. Zhang, Semantic interoperability in building design: methods and tools,Comput. Aided Des. 38 (10) (2006) 1099–1112.

[52] C. Zhang, W. Li, T. Zhao, Geospatial data sharing based on geospatial semantic webtechnologies, J. Spat. Sci. 52 (2) (2007) 35–49.

[53] J.P. Zhang, Z.Z. Hu, BIM- and 4D-based integrated solution of analysis and manage-ment for conflicts and structural safety problems during construction: 1. Principlesand methodologies, Autom. Constr. 20 (2) (2011) 155–166.

[54] P. Zhang, F. Harris, P. Olomolaiye, G. Holt, Location optimization for a group of towercranes, J. Constr. Eng. Manag. 125 (2) (1999) 115–122.

[55] D. Zhong, J. Li, H. Zhu, L. Song, Geographic information system-based visual simula-tion methodology and its application in concrete dam construction processes, J.Constr. Eng. Manag. 130 (5) (2004) 742–750.