=Paper=
{{Paper
|id=Vol-2636/08paper
|storemode=property
|title=ifcOWL-DfMA a new ontology for the offsite construction domain
|pdfUrl=https://ceur-ws.org/Vol-2636/08paper.pdf
|volume=Vol-2636
|authors=Edlira Vakaj,Franco Cheung,Abdel-Rahman Tawil,Panagiotis Patlakas,Kudirat Alyania
|dblpUrl=https://dblp.org/rec/conf/ldac/VakajCTPA20
}}
==ifcOWL-DfMA a new ontology for the offsite construction domain==
https://ceur-ws.org/Vol-2636/08paper.pdf
Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
ifcOWL-DfMA a new ontology for the offsite construc-
tion domain
Edlira Vakaj Kalemi1, Franco Cheung1, Abdel-Rahman Tawil1, Panagiotis Patlakas1
Kudirat Alyania2
1 Birmingham City University, United Kingdom
2 University of East London, United Kingdom
edlira.vakaj@bcu.ac.uk
Abstract. Architecture, Engineering and Construction (AEC) is a fragmented in-
dustry dealing with heterogeneous data formats coming from different domains.
Building Information Modelling (BIM) is one of the most important efforts to
manage information collaboratively within the AEC industry. The Industry Foun-
dation Classes (IFC) can be used as a data format to achieve data exchange be-
tween diverse software applications in a BIM process. The advantage of using
Semantic Web Technologies to overcome these challenges has been recognised
by the AEC community and the ifcOWL ontology, which transforms the IFC
schema to a Web Ontology Language (OWL) representation, is now a de facto
standard. Even though the ifcOWL ontology is very extensive, there is a lack of
detailed knowledge representation in terms of process and sub-processes explain-
ing Design for Manufacturing and Assembly (DfMA) for offsite construction,
and also a lack of knowledge on how product and productivity measurement such
as production costs and durations are incurred, which is essential for evaluation
of alternative DfMA design options. In this article we present a new ontology
named ifcOWL-DfMA as a new domain specific module for ifcOWL with the
aim of representing offsite construction domain terminology and relationships in
a machine-interpretable format. This ontology will play the role of a core vocab-
ulary for the DfMA design management and can be used in many scenarios such
as life cycle cost estimation. To demonstrate the usage of ifcOWL-DfMA ontol-
ogy a production line of wall panels is presented. We evaluate our approach by
querying the wall panel production model about information such as activity se-
quence, cost estimation per activity and also the direct material cost. This ulti-
mately enable users to evaluate the overall product from the system.
Keywords: Offsite Construction, IFC, Ontologies, Linked Open Data
Copyright © LDAC2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
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1 Introduction
The Architecture, Engineering and Construction (AEC) sector has been criticised as
low in productivity as compared with that of other sector’s, e.g. the productivity of
manufacturing, automotive, and aerospace sectors. One holistic approach to improve
productivity is the application of Design for Manufacture and Assembly (DfMA).
DfMA, first developed for product design, aims to improve production so that products
produced are consumed by the manufacturing process as quickly as possible with the
least amount of waste and redundant works. In practice, it involves a continuous eval-
uation of the manufacture and assembly processes by designers. It is now widely ac-
cepted within the AEC that one major direction to improve productivity is to move the
production activities offsite [1]. The application of DfMA thus enable designers to con-
sider alternative offsite production approaches with automation in mind.
The use of Building Information Modelling (BIM) in building projects offers opportu-
nities to extract properties and data of a building easily but there is generally a lack of
attention to data collected during the construction process. Typically, process-related
data in BIM are only used for scheduling purposes. The actual use of process data for
informing manufacturing or off-site decision-making is limited. This paper proposes a
semantic approach for linking process data with life cycle costs and carbon emissions
to give an accurate production costs and carbon footprint. The estimations from the
semantic knowledge-based system will give an objective measure to inform designers
in evaluating DfMA options.
Semantic Technologies and Linked Open Data have been broadly used in the domains
of AEC. The usage of these technologies is driven by the need to operate with hetero-
geneous data formats from different sources and domains, support data interoperability,
flexible data exchange and distributed data management. An extensive literature review
conducted by Pauwels et al. [5] has emphasised the crucial role semantic technologies
and logic-based applications play in systems that require the integration of information
from multiple application areas. The standard schema for the exchange of BIM data is
IFC [11]. It has a strong focus on 3D geometry [11] and is modelled using EXPRESS
[10]. Semantic Technologies where applied to implement a direct mapping of IFC
EXPRESS schema to ifcOWL ontology [6]. IFC schema and ifcOWL ontology con-
cepts of design differs from those used for DfMA as the latter is production led, focus-
ing on the manufacture and assembly process. One example would be product classifi-
cation in DfMA design in which products come with details of sub-assemblies, gener-
ally are under-represented in the IFC schema.
The proposed ifcOWL-DfMA ontology aims to provide the AEC community with a
vocabulary of commonly understood concepts and relationships to represent the domain
of offsite construction, as well as a means to publish linked open DfMA data. This is
achieved by contributing to the development of domain knowledge that handles inter-
disciplinary information exchange among different participants during the life-cycle of
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design for manufacturing. In addition it provides a basis for future development of
smart tools that will be able to provide answers for practical scenarios.
2 Domain Knowledge and State of the art
2.1 Design for Manufacturing and Assembly
Traditionally, design and construction are separated with the relevant responsibilities
assigned to different parties. The role of a contractor is mainly an integrator that focuses
on the delivery of buildings with little attention to the potential benefits of factory pro-
duction. The call for improvement of the AEC sector in terms of productivity and prod-
uct performance has led to a change in some market segments of the sector to consider
alternative approaches to design and construct buildings. DfMA is a design approach
that is composed of two parts: design for manufacture (DfM) and design for assembly
(DfA). Through engineering a building design – often, a standardised design, the goal
of DfMA is to minimise waste and redundant operations. Examples of specific targets
for DfM are the selection of materials that minimise wastage and handling, optimising
processes and sub-processes, optimising parts and systems fulfill tolerance require-
ments, and those for DfA are minimising number of modules for assembly and opti-
mizing assembly. In practice, it is a continuous task of reviewing, evaluating, rational-
ising, standardising and optimising the functionality, producibility, handling and fixing
of design.
The task is very knowledge intense and complex, and requires input from experts of
various disciplines - some of them such as production engineers are not traditionally a
part of the building design team. As the knowledge is not readily accessible, there is a
need to systemise the knowledge to enable the evaluation of building design by indi-
vidual discipline owners. The current approach for evaluation relies heavily on either
heuristic (“rule of thumb”) or high-level estimations with little effort spent on under-
standing how processes and sub-processes are related and interacted. For instance, an
estimated cost – as a measure for rationalizing or optimizing - is calculated based
largely on historical high level per unit cost without taking into account on how cost
actually incurred. This is problematic as the economy of off-site manufacturing, a core
element of DfMA, is process-driven and can only be evaluated properly if the estimate
reflects the cost implications of processes. For instance, the cost for a static production
process, i.e. the use of mainly labour for production would be different from an auto-
mated production, i.e. the use of mainly machine or robot for production. The
knowledge however is not typically kept in the system of the current status quo. The
argument that construction processes and sub-processes are premature to consider in
the design stage in traditional approach does not apply if DfMA is to be adopted as
building design is based on standardised design. Standardised design makes product,
production and assembly data to be kept and reuse in a more efficient manner. System-
ising knowledge of product, production and assembly for DfMA through creating an
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accurate representation of the relationships of the process and sub-processes can auto-
matically generate estimates of productivity and performance matrics such as produc-
tion cost, life cycle cost, duration or CO2 emissions.
2.2 Building Information Modeling
Building Information Modeling is a digital process for the representation and pro-
cessing of all information relevant to the Building Life Cycle (BLC). Typically, the
foundation of a BIM process will be a three-dimensional (3D) model of the architectural
design, detailing the positioning and dimensions of a buildings components (walls, win-
dows, doors etc.) and facilitating the inclusion of non-physical building features such
as building cost, accessibility, safety, security and sustainability [3]. In a BIM model
not only the geometric features are included but also the semantic attributes are in-
cluded and the associated properties [5]. BIM is an intelligent model-based process that
connects AEC professionals so they can design, build and operate buildings more effi-
ciently. BIM is also used for creating data for infrastructure associated with physical
and functional characteristics. BIM projects are implemented from the start as either
closed or open models. The latter uses the Industry Foundation Classes (IFC) [11], a
standardised platform-neutral schema, for data exchange. An IFC data model in prac-
tice focuses on building geometry representation in the design stage. In the work pre-
sented here, the Open BIM approach is the assumed adoption.
In the UK, BIM implementation is defined according to different levels of maturity
starting from BIM Level 0 to Level 3 - a cloud-based implementation where data from
different domains can be integrated seamlessly without any data loss [2]. The Semantic
Technologies and Linked Data principles proposed can also play a very important role
in achieving Level 3 BIM.
2.3 IFC and ifcOWL Ontology
IFC files represent BIM components using the EXPRESS modelling language [10].
Using IFC data and instance serialization formats, BIM data can be exchanged between
heterogeneous software applications. A basic overview of IFC hierarchy is given in
Figure 1. However, IFC is not a web-compliant one, therefore there is a requirement to
use semantic standards and technologies [11] like the Web Ontology Language (OWL)
for which the Linked Data standards was proposed. Initially OWL was integrated with
IFC [11] to produce ifcOWL ontology. Later, a direct mapping of EXPRESS schema
to OWL [4] was introduced and implemented in the current version of ifcOWL ontol-
ogy. The ifcOWL is now under buildingSMART [8] International, where it eventually
became a part of the ISO 16739 standard [7].
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Figure 1Part of the hierarchy of classes in ifcOWL ontology.
The ifcOWL ontology is an extensive ontology. In the latest version, i.e. IFC4, con-
sists of 1293 classes and 1572 object properties. This makes reasoning and management
very hard and inefficient, and inevitability, increases the need to develop separate mod-
ules based on the core IFC modules. The proposal to implement a modular ifcOWL
ontology was proposed by [15] and has started to be adopted by different authors. Even
more recently the need for modularity and extensibility was explicitly from the authors
a [14] when they introduced the BOT - Building Topology Ontology.
3 ifcOWL-DfMA Ontology Development
The aim of the ifcOWL-DfMA ontology is to present an ontology that defines the key
terms and relationships present in the DfMA approach to building design, while simul-
taneously acting as an extension of the ifcOWL ontology, in order to maintain compli-
ance with core IFC concepts.
3.1 Ontology Development Methodology
As illustrated in Figure 2, the first step taken to design ifcOWL-DfMA ontology was
conducting a literature review in terms of: i) existing ontologies designed of IFC where
ifcOWL was identified and analyzed; ii) existing ontologies for offsite construction,
DfMA and related domain; iii) general DfMA and related domains literature review in
order to extract the main concepts and relation of the domain. The literature review
confirmed that there is no existing ontology that represents offsite construction and life
cycle assessment with the DfMA approach.
As a second step, a set of competency questions was drafted based of the guide for
developing an ontology from Stanford University [16].The competency questions have
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guided the discussion with the stakeholders and experts involved including architects,
production engineers, structural engineers, steel supplier, client and cost consultant on
one to one interviews and group discussions. An iterative approach was adopted to the
ontology design process to reflect the feedback from the experts and improve the on-
tology.
Competency
Validation
Questions
Literature
Expert Interview
Review
Figure 2 Methodology used to develop ifcOWL-DfMA ontology
3.2 Overview of ifcOWL-DfMA Ontology
ifcOWL-DfMA define a model of categories within the offsite manufacturing Universe
of Discourse (UoD), plus sufficient knowledge about those categories to allow for them
to be reasoned upon and classified automatically. Our aim is to use ifcOWL-DfMA as
a COmmon REference, or CORE model for offsite manufacturing. The proposed on-
tological model is language independent, using the broader term ‘terminology’ for a
semantic model linked to the offsite manufacturing domain.
A high level schema (upper ifcOWL-DfMA ontology) is a prerequisite for categori-
sation and integration, as illustrated in Figure 3:
• Fits closely with building standards especially in applications for design and
manufacturing assembly or in the retrieval and classification of ifcOWL-DfMA
concepts.
• Sufficiently general to be used in different applications for decision support and
interoperability.
• Formally defined in OWL Description Logic (DL) and can be considered a gen-
eral-purpose modelling language for offsite manufacturing.
• Supports OWL-DL reasoners to allow for core ifcOWL-DfM concepts to be
combined to create new descriptions of classes and instances constructed accord-
ing to constraints implemented within the ontology.
• Support intuitive and practical collaboration between different groups, being eas-
ily understood and application independent.
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OWL
Thing
Activity_Cost_Drivers
Cost Activity_Cate- DfMA_Produc-
gory tion_Process
Platform
Produc- Activities Resources
tion_Cost Time
Location Production_Ac- Labour
Direct_Cost Indirect_Cost tivities
Supporting_Activ- Material
Transport
ities
rdfs:subClassOf Over-
head
Plant
Figure 3 A high level schema of ifcOWL-DfMA ontology
In ifcOWL-DfMA, the primary breakdown is into:
• DfMA_Production_Process, defines both production and supporting activi-
ties
• Resources, defines labour, material overhead and plant
• Activities, defines production activities (e.g., gladding assembly line auto-
mation, frame assembly line) and resources (e.g., labour, material and com-
ponent, overhead etc.)
• Modality, defines platform, time, location and transport, representing a heter-
ogeneous grouping for usage in associations with production processes and
activities.
A secondary structure is superimposed over the primary, aiming to capture DfMA
production process, activities and resources. Table 1 shows ifcOWL-DfMA taxonomy
of major elementary categories associated with the production process. The category
labelled DfMA_Production_Process represents the disjunction of two main categories,
activities and resources which can be observed.
As an ontology for offsite design and manufacturing, ifcOWL-DfMA further divides
production processes into production activities e.g., Cladding_Asssembly_Line,
Frame_Assembly_Line and supporting activities such as loading, packaging and trans-
porting. For example, a cladding assembly line is an automated activity that is defined
as a production activity which begins only after frame assembly line is completed and
consumes some labour.
Cladding_Assembly_Line_Automated and beginsAfter only Frame_Assembly_Line
Cladding_Assembly_Line_Automated and consumeLabour some Labour
Loading isSubClassOf Suppoting_Activity and consumeLabour some Labour
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Resources are further divided into MaterialandComponents (e.g., Direct_Matrial and
Packaging_Material), Overhead (e.g., Cleaning, Security) and plant (e.g., Mova-
ble_Tools, Static_Tools). MaterialandComponents is used to group Direct_Material to-
gether including external wall cladding, internal wall cladding, wall finishing, wall fix-
ing and wall framing.
Table 1. ifcOWL-DfMA taxonomy of major elementary categories
Entity Example
DfMA_Production_Process
Activity
Production_Actvity
Cladding_Assembly_Line
Adhesive_Station T31_Feed_Adhesive, T32_Dispense_Adhesive
Briquette_Apply_Sta- T35_Place_Briquettes
tion T34_Feeding_Briquettes
Briquette_Load_Station
…
Frame_Assembly_Line T13_Return_Conveyor
Conveying_Station T5_Rivet_Joints, T6_Move_Frame_to_Lift,
Frame_Riveting_Station T7_Lift_Frame
Frame_Transfer_Station T9_Transfer_Frame
FrameBeam_Load_Sta- T1_Deliver_Pallets, T2_Se-
tion lect_and_Load_Beam, T3_Clamp_Beam
…
Supporting_Activity
…
Resource
Labour
Direct_Labour
Casual_Labour hasLabourHrRate "9.0"^^xsd:double
Semi-Skilled_Operative hasLabourHrRate "13.0"^^xsd:double
workingOnActivity T1_Deliver_Pallets
workingOnActivity T2_Select_and_Load_Beam
MaterialandComponent
Direct_Material
EXT_Wall_Cladding
INT_Wall_Cladding
Wall_Finishing
Overhead
Plant
Subcategories share common characteristic from which a single constraint may be
inherited, but are otherwise disjoint and heterogeneous. In addition, ifcOWL-DfMA
recognises, UnitsOfMeasures such as miles, kilometres, metre, kilogram, minute, cur-
rency etc. which are used as part of quantities.
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ifcOWL-DfMA Attribute Hierarchy
The taxonomy of ‘attributes’ (or ‘semantic link types’) is influenced by and supports
the outlined category taxonomy. The primary distinction here is between object and
data properties. While data properties (e.g., hasUnitRate, hasLabourHrRate, hasCount)
describe what kind of values a triple with the property should have by relating individ-
uals to literal values (e.g., strings, numbers, datetimes, etc.), object properties (e.g., be-
ginsBefore, beginsAfter, isComponentPartOf) relates concepts together to define rela-
tionships across concepts.
Rules in the form of the Semantic Web Rules Language (SWRL) are used to provide
more powerful deductive reasoning capabilities than OWL alone. For example, the rule
below determined the cost of labour for an activity by multiplying the processing time
with the labour hourly rate.
Activities(?a), Labour(?s), hasLabourHrRate(?s, ?r), hasProcessTime(?a, ?p),
workingOnActivity(?s, ?a), multiply(?result, ?p, ?r) -> hasActivityCost(?a, ?result)
3.3 Alignments with ifcOWL ontology
As mentioned in previous sections ifcOWL-DfMA ontology is developed inde-
pendently from the ifcOWL ontology but is aligned with it such that every dfma:Build-
ing is an IfcBuilding and every dfma:Product is an IfcProduct.
ifc: Building ifc: Element
rdfs:subClassOf
dfma:Product
rdfs:subClassOf
dfma:SubAssembly
dfma:Building dfma:OffsiteSystem
dfma:Components
dfma:Assembly
Figure 4 Alignment between ifcOWL-DfMA ontology and ifcOWL ontology.
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Developing ifcOWL-DfMA as a separate domain of the existing ifcOWL ontology was
a conscious choice. Naturally, many aspects of a completed DfMA project, such as the
building geometry or the material properties, fit ifcOWL concepts and can be repre-
sented accordingly. However, as a process with roots in industrial engineering, DfMA
engages more with procedural and optimisation aspects, and introduces concepts, such
as “assembly” or “sub-assembly”, with different semantics from current BIM and con-
struction technology practice. As such, a separate ontological domain was considered
necessary in order to avoid semantic and ontological conflicts, as well as to implement
DfMA concepts appropriately. Ideally, ifcOWL-DfMA will be able to facilitate a two-
way conversation: enable AEC practitioners to apply DfMA design concepts in a BIM
workflow, while simultaneously acting as an introduction to the DfMA concept to BIM-
literate AEC practitioners.
At the same time, the need for a separate domain suggests that there are some limita-
tions to the current ifcOWL ontology. Attempting to capture all possible aspects of a
building in a single hierarchical ontology, mapped to a super-schema, has innate limi-
tations and lacks the flexibility to accommodate different design concepts. DfMA is a
characteristic case study on that: future innovative philosophies and practices are likely
to face similar challenges in BIM implementation.
4 Using ifcOWL-DfMA ontology in practice
The ifcOWL-DfMA ontology is applied on the production of a wall panel system for a
house design using DfMA. The application captures its production process and adopts
a manufacturing costing approach, namely Activity-Based Costing (ABC) to classify
cost data. The process-based costing method measures the activity costs of cost objects
(i.e. various cost centres for wall panels) attempting to give accurate and traceable cost
information. Decision makers are thus presented with more in-depth information that
encourages corrective actions. For instance, it allows users to identify cost drivers of
an off-site wall panel production such as factory rent and production volume. A separate
process mapping exercise for DfMA production is carried out and a process map for
proposed off-site production line for DfMA house wall panels has been produced. The
wall panels are modeled by describing their attributes such as the components that com-
pose a wall panel but also the production line detailed in terms of activities carried to
produce a wall panel as illustrated in Figure 5.
All activities are connected with each other by keeping track of which activity should
perform first(hasStartingActivity) and which activity takes place next(hasNextActivity)
or in parallel. Further on, the knowledge represented in the ontology is used to estimate
cost (hasDirectCost, hasMaterialCost, hasActivityCost etc.) per each activity and over-
all cost of producing one product in this case a wall panel. By estimating the cost per
each activity the designer can get insights in which activity are occurring overhead costs
and optimise their design if possible.
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Figure 5 Modeling wall panel instantiation
Apart from costing, Table 2 gives some example queries that a designer might possibly
ask regarding the DfMA house composed of 32 wall panels to the instantiated ontology,
which are the estimates of potential productivity and performance matrices. The queries
are expressed in SWRL or SQWRL and the reasoning is made by Pellet reasoner.
Table 2. Example queries in SWRL/SQWRL and respected results.
Question Query(SWRL/SQWRL) Result in Protégé Comment
Q1. What is labor Semi-Skilled_Operative(?s) ^ Activities(?a) "1.17"^:SSO_1 The result displayed
cost for each ^ workingOnActivity(?s, ?a) ^ hasProcess- "2.6" ^:SSO_4 shows the labour cost of
semi-skilled oper- Time(?a, ?p) ^ hasLabourHrRate(?s, ?r) ^ "5.85"^:SSO_6 building a single wall
ative working on swrlb:multiply(?result, ?p, ?r) -> "1.4689"^:SSO_3 panel automatically on
each activity of sqwrl:sum(?result) ^ sqwrl:select(?s) "3.237"^:SSO_5 the production line with
the wall panel five operatives support-
production? ing different activities
done by the robots to
form the panel.
Q2. What is total Prod- "3075.81918" This result aggre-
direct material uct(LSF_3BED_01_LHS)^hasDirectMateri gates the cost of each
cost for producing alCost(LSF_3BED_01_LHS, ?m) -> components/materials
panel? sqwrl:select(LSF_3BED_01_LHS) ^ used in building up the
sqwrl:sum(?m) panel of a semi-detached
house (with identity 01)
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Q3. What are the Product(?p) ^ hasComponentPart(?p, ?Com- mmS mmSS This results show the
components of ponent) -> sqwrl:select(?Component) mmStud mmHT components used in
WallPanel01? mmHT mmBT building the wall panel
mmCS mmStud 01 which includes
mmBT mmBT Studs, Head Track, Base
mmBT mmFS Track, Cripple studs of
mmStud different sizes.
Q4. What is the Product(LSF_3BED_01_LHS) ^hasStart- LSF_3BED_01_LH This result displays
starting and up- ingActivity(LSF_3BED_01_LHS, ?Star- S, T1_Deliver_Pal- the sequence of activi-
coming activity tActivity) ^ hasNextActivity(?StartActivity, lets, T2_Selectand- ties carried out by oper-
for producing ?NextActivity)->sqwrl:se- LoadBeam atives in building the
LSF_3BED_01_L lect(LSF_3BED_01_LHS, ?StartActivity, wall panel with identity
HS wall panel? ?NextActivity,) 01
5 Conclusion and Discussions
This article proposes a new domain specific ontology ifcOWL-DfMA ontology which
expands ifcOWL ontology as a separate module deriving from core element of IFC.
The ifcOWL-DfMA ontology is however on the early versions of development and
further improvements can be done. In order to ensure interoperability this ontology is
rooted in the de-facto standard ontology for IFC (ifcOWL) and follows the Linked
Data principles. To address the complexities that ifcOWL has, the World Wide Web
Consortium (W3C) Linked Building Data Community Group is standardizing the
Building Topology Ontology (BOT) [14] that will interlink different domain specific
ontologies more efficiently and when this is needed. BOT uses Linked Data approach
to describe the buildings by only using the fundamental properties and if more detailing
are required the linking with other relevant ontologies is enabled. This is the direction
that ifcOWL-DfMA is planning to take after wider evaluation with the community of
interest.
Acknowledgments
This research was supported by an Innovate UK funded project “Collaborative
Knowledge- Based DfMA approach to build cost efficient, low impact and high perfor-
mance houses”. The authors would like to thank the participating industrial partners of
the research project, particularly Walsall Housing Group and Northmill Associates for
their contribution in this study.
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