=Paper=
{{Paper
|id=Vol-3081/01paper
|storemode=property
|title=Ontology based anamnesis and diagnosis of natural stone damage for retrofitting
|pdfUrl=https://ceur-ws.org/Vol-3081/01paper.pdf
|volume=Vol-3081
|authors=Al-Hakam Hamdan,Peter Katranuschkov,Raimar Scherer
|dblpUrl=https://dblp.org/rec/conf/ldac/HamdanKS21
}}
==Ontology based anamnesis and diagnosis of natural stone damage for retrofitting==
https://ceur-ws.org/Vol-3081/01paper.pdf
Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
Ontology based anamnesis and diagnosis of natural stone
damage for retrofitting
Al-Hakam Hamdan, Peter Katranuschkov and Raimar J. Scherer
1 Institute of Construction Informatics, Technische Universität Dresden, Dresden, Germany
al-hakam.hamdan@tu-dresden.de
Abstract. During the inspection and assessment of existing buildings, a large
portion of the recorded data is stored in documents or models that are not com-
puter interpretable. Managing this data digitally is a complex process because the
evaluation of the building components and the affecting structural damages in-
volves a significant amount of manual tasks that can be error-prone and time
consuming. Therefore, a knowledge-based approach has been developed, which
utilizes web ontologies to store building and damage information in a semantic
representation and processes them in an automated assessment via predefined
rulesets. The concept is specified and applied for structures made of natural stone
and is specifically tested and verified on the common case of a damaged façade.
A newly developed software platform is presented, which provides functions for
managing and evaluating a damage ontology as well as linking damage infor-
mation with a geometry-based BIM model by utilizing an Information Container
for linked Document Delivery (ICDD) according to ISO 21597-1 and adapting a
general-purpose BIMification approach for retrofitting.
Keywords: Natural stone, Retrofitting, Damage ontology, SHACL,
BIMification
1 Introduction
Building Information Modeling (BIM) is rapidly advancing as an efficient new
approach to cooperative building design and construction. However, in the inspection
and assessment of existing buildings, a large portion of the recorded data is stored in
documents that are not computer interpretable. This is especially important in the con-
text of European construction, which considers the retrofitting of the aging building
stock as high priority issue, especially regarding energy efficiency and cultural heritage
building preservation. It requires a modified BIM-based process, specifically suited to
the stepwise development of retrofitting planning, in which an initial building model
does not exist and the occurrence of damages and defects that often lead to deterioration
of structural components has to be dealt with. We name this new model-based retrofit-
ting process BIMification, which we have proposed as a structured method to obtain
from an existing real building a BIM model and use that model in a clearly defined 3-
stage design process of Anamnesis, Diagnosis and Therapy (Scherer & Katranuschkov,
2018. However, to apply that process successfully it is not sufficient to only generate a
Copyright 2021 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0)
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
3D geometry model of the building. A knowledge-based approach built upon semantic
web ontologies is additionally needed, aiming at the digital representation of detected
damages and their automatic evaluation to determine appropriate maintenance
measures, provide important information that is missing or incompletely presented in
BIM and reason about that information to enable efficient process application.
In this paper, we describe how the generic BIMification approach is applied for the
anamnesis and diagnosis of natural stone damages, with specific practical emphasis on
the damages on stone façades of historical buildings, which is a frequently occurring
task with high societal relevance. In chapter 2 we outline the related work upon which
the suggested new ontology-based approach is built. In chapter 3, the overall BIMifi-
cation methodology and the developed ontology framework are presented and in chap-
ter 4 its application within a newly developed software platform is discussed on the
example of a specific test case. The paper concludes with a summary of the results
achieved so far and outlines current and envisaged further development work.
2 Related Work
The idea of a structured 3-stage design process has been initially suggested by van
Balen, Verstrynge and others (2016) as a generic approach towards the structural reha-
bilitation of cultural heritage buildings. Albeit not related to BIM use, it enables a deep
understanding of the existing building and drafts a picture of the pros and cons of the
investments for retrofitting at the earliest stages of the design process.
Considerable results have been reached for the creation of a geometry-based digital
representation of an existing building, which we denote as part of the geometrical BIMi-
fication. Since the introduction of IFC as standardized exchange format for BIM models
according to ISO 167391, multiple extensions for the IFC schema has been proposed
that add entities and properties for representing the as-damaged state of deteriorated
constructions (Artus & Koch, 2020; Hüthwohl et al., 2018). However, the integration
of maintenance related data in the IFC model schema can lead to problems, such as the
required bilateral extension of compatible IFC Tools or the difficulty of controlling and
managing one consistent supermodel. For this reason, more modular approaches have
been developed that store information about detected damages in separate ontologies
typically formalized in the Web Ontology Language (OWL)2. Cacciotti et al. (2015)
have developed a web ontology in the project MONDIS, which allows for modelling
damages in cultural heritage buildings, together with the corresponding damage causa-
tions. Simeone et al. (2019) have developed a platform for heritage representation, in
which data from a geometry-based BIM model is mapped to an ontology for represent-
ing restoration processes.
To assess the state of the damaged structure and interpret properly collected facts
about the deteriorated components for concluding about potential maintenance
measures, artificial intelligence methods such as Machine Learning (ML) as well as
1 ISO 16739-1 (2018): Industry Foundation Classes (IFC) for data sharing in the construction and
facility management industries – Part 1: Data schema, ISO, Geneva, 1474 p.
2 https://www.w3.org/TR/owl2-overview/
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
knowledge-based approaches have been proposed. Current ML approaches usually ex-
ploit convolutional neural networks to classify and consequently evaluate detected
damages (Li et al., 2018). However, such approaches evaluate damages solely based on
their characteristics, e.g., the width or height of a crack. Contextual information about
the damaged component or building, such as the building material or age, is usually
ignored. Taking a different AI approach, Lee et al. (2016) have developed an OWL
ontology, which is used in a case-based reasoning process for evaluating construction
defects. Thereby, a set of previously assessed defect cases is stored in an ontological
database. Using SPARQL queries, case-based reasoning for similar defects can then be
applied. Contrary to ML, which relies on correlations between training data and input
data, knowledge approaches utilize rules that are based on logic and expert know-how.
For example, Hu et al. (2019) successfully apply rules formalized in N3Logic to reason
the causations of detected tunnel damages and provide decision making support.
3 Methodology
3.1 Workflow
Based on the BIMification concept of Scherer & Katranuschkov (2018) the proposed
retrofitting process can be structured in three major phases, namely (1) Anamnesis,
(2) Diagnosis and (3) Therapy. The latter is not different from the typical BIM approach
where multiple variants are examined and is therefore not further discussed here. This
generic process can be easily adapted for the domain of natural stone damage. The
overall workflow shown in Fig. 1 does thereby remain valid but sub-processes, inputs
and outputs are adjusted to the specific characteristics of the application domain.
BIMification Process BIM-based Retrofit Design
Anamnesis Diagnosis Therapy
BIM of the existing building The models from the Anamnesis BIM of the retrofitted
and Ontology Framework extended by an ontology of building
of the building topology proposed renovation measures
and the stone damages
Fig. 1. Overall workflow
The Anamnesis phase is dedicated to the survey and collection of facts about the
building and the affecting damages. In this phase a geometry-based BIM model of the
existing building is created and linked via an ICDD with gradually constructed ontolo-
gies providing semantic representations of the construction and the affecting damages.
The process is divided into several sequential sub-processes (see Fig. 2). It starts with
the creation of a pure geometric model based on the existing building by utilizing BIM
authoring tools like Revit, which allow for an IFC export. In subsequent steps, the
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
resulting IFC model is enriched with additional information. The resulting ontological
models described in detail in section 3.3 comprise a subset of the geometry data ob-
tained from the BIM model, but also topological (Topological BIMification) and infra-
structure information (Neighbourhood BIMifcation) as well as semantic data about the
embedded building components (Element- and Subcomponent BIMification).
Basic BIMification Advanced BIMification
Geometrical Topological Neighbourhood Element Subcomponent
BIMification BIMification BIMification BIMification BIMification
BIM/IFC geometry Building Topology BOT extended with BOT further extended BOT ext. for stones (SCO)
Ontology (BOT) infrastructure context with element and material Damage Ontology (DOT)
(arch. style, climate etc.) description (DICBM)
Fig. 2. Anamnesis phase
The diagnosis phase is dedicated to the analysis and interpretation of the collected facts
from the preceding anamnesis phase to obtain the necessary understanding of the build-
ing’s damage state and propose possible retrofitting measures. In this phase the infor-
mation of the ontologies that were linked in an ICDD to the BIM model in the previous
steps as semantic enrichment are processed by a reasoning engine to infer additional
information about the damages and their impact on the inspected building. Rules are
applied to infer potential maintenance measures, which are defined via the Shapes Con-
straint Language (SHACL)3. The process is structured in two sub-processes (see Fig.
3). In the first sub-process, the anamnesis results recorded in the ontology framework
are assessed via SPARQL queries as described in section 4.1. On that basis, in the sec-
ond sub-process variants for potential retrofitting measures as well as an ontology for
representing potential renovation measures (SRMO) are generated. This ontology is
used later in the Therapy phase to create and examine different retrofitting actions, and
evaluate these in terms of resources, cost, and time.
Extended BIMification
Damage Variability
BIMification BIMification
Anamnesis Damage Proposed retrofitting measures and
results assessment variants obtained via SHACL rules
(SPARQL queries) to create retrofit ontology (SRMO)
Fig. 3. Diagnosis phase
3 https://www.w3.org/TR/shacl/
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
3.2 Multimodel environment
The largest portion of the modelling data needed in a retrofitting project is geometry
related information about the existing building. Two sets of such data have to be gen-
erated in the geometric BIMification step: (1) the BIM data for the building, represented
as an IFC data set comprising the building structure composed of storeys, spaces and
building elements (slabs, walls, columns, windows, doors etc.), and (2) the BIM data
for the natural stone subcomponents that have to be inspected and evaluated with regard
to damages. The first of these data sets is comprised mostly of instances of standardized
well-defined IFC object classes. In contrast, the second data set contains aggregated
elements, which are usually defined through proxy classes and share the same geomet-
rical space as the higher-level building element they belong to. For example, an IfcWall
in the first data set, which represents a stone façade, is represented at the same time by
many IfcProxy objects in the second data set that describes the aggregated individual
stones. A decomposition of the building structure at that level of detail does not exist
in the IFC schema and has to be provided by other means. In addition, various non-
geometric modelling data about the building need to be considered as well such as the
building age or climatic and environmental conditions. Because this information is not
represented in a single model, we apply the Multimodel approach by utilizing the stand-
ardised Information Container for linked Document Delivery (ICDD) of ISO 21597-14.
Thereby, the separate models in the ICDD are provided as payload documents and are
linked together via link ontologies that are called payload triples, providing the connec-
tion semantics between the elements of the models. In the domain addressed herein,
this semantics is broadly as follows:
• Stone elements are linked to the corresponding building elements in the BIM/IFC
model and later to semantic representations in the ontological damage model as well.
• Data about the building are linked to the IfcBuilding object in the BIM/IFC model;
• Material data are linked to the building elements in the BIM/IFC model and through
that to the stone elements in the detailed stone models; however, if stones of different
material are used that link is reversed and material is directly linked to the proxy
elements representing the stones;
• Stone damages are linked to the stone elements within the ontology framework,
thereby indirectly linking them to the containing building element if necessary.
Based on the BIM/IFC information a non-geometric ontological representation is
generated, which provides a knowledgebase for further semantic reasoning. The links
to the geometry data in BIM are thereby preserved and can be used whenever needed.
3.3 Ontology framework
The ontology framework, which is used for creating a representation of deteriorated
components made of natural stone, consists of 3 ontology domains each serving a
4 ISO 21597-1 (2020): Information container for linked document delivery - Exchange specifi-
cation, Part 1: Container, ISO, Geneva, 41 p.
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
specific function. The representation of the natural stone components in their as-built
state is achieved by applying an extension of the Building Topology Ontology (BOT)
proposed by Rasmussen et al. (2017), which we call Stone Component Ontology (SCO)
(Seeaed & Hamdan, 2019). It provides terminology for defining stone and gap repre-
sentations and their topological relations with each other, specifically considering ad-
jacencies and aggregations. In addition, the Digital Construction Building Material on-
tology (DICBIM) proposed by Valluru et al. (2020) can be used for specifying material
properties of the stone and gap representations.
To model the as-damaged state of a building, damage representations that are related
to deteriorated components must be further defined. For that purpose, the Natural Stone
Damage Ontology (NSD)5 proposed in Seeaed & Hamdan (2019) is used. It is an ex-
tension of the generic Damage Topology Ontology (DOT) developed by Hamdan et al.
(2019). While DOT6 provides generic classes for defining damage individuals, NSD
extends the DOT concept and adds specific classes and properties describing damages
on natural stone structures, such as fractures or blistering. In this regard, NSD is based
on the classified damage types of the ICOMOS glossary of the International Scientific
Committee for Stone (ICOMOS, 2010). As already mentioned in section 3.1, an inte-
gral part in the diagnosis stage is the inference of appropriate renovation measures.
Thereby, new individuals that represent potential stone renovation measures are rea-
soned by processing predefined SHACL rules. To characterize the newly inferred ren-
ovation measures the Stone Renovation Measure Ontology (SRMO)7 has been devel-
oped as extension of the Construction Tasks Ontology (CTO) 8 (Bonduel, 2021). In this
regard, various measure methods are structured in a taxonomy as subclasses of the class
srmo:StoneRepairTask, which is a subclass of cto:RepairTask and could be assigned to
deteriorated stone components via the object property cto:isSubjectOf.
Fig. 4. Main Classes required for representing a damaged stone component and its renovation
5 https://w3id.org/nsd#
6 https://w3id.org/dot#
7 https://w3id.org/srmo#
8 https://w3id.org/cto#
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
Fig. 4 provides an example demonstrating the links between components of the pro-
posed ontology framework, which forms only the base knowledgebase for semantically
representing a damaged stone component and its associated potential renovation
measures. Therefore, an information extension through utilizing additional ontologies,
e.g., for defining structural functions or energy parameters, is recommended.
4 Application on Test Case
As proof of concept, the methodology described in the preceding chapter has been ap-
plied for a deteriorated natural stone façade. Accordingly, the geometry is represented
in two IFC models, the first defining the geometry of the whole building and its general
building components, and the second defining the detailed stone geometry of the dete-
riorated wall. The IFC Model that defines the stone geometry is linked to the corre-
sponding IFC entity representing the whole stone façade through a linkset in an ICDD.
The ICDD creation and management of the IFC models is processed by a software
platform, which was developed in the frames of this research and is published on
Github9. Thereby, the IFC management and processing features utilize the APSTEX
IFC framework by Tauscher & Theiler (2018). Fig. 5 below shows an example of the
performed test case.
Fig. 5. IFC models for representing the natural façade and its stone components
The developed software platform allows automated generation of an ontology from
the IFC model by utilizing the BOT and SCO ontology definitions, similar to the IFC
to Linked Building Data Converter (Bonduel et al., 2018). In the generation process all
individuals that relate to a building element are linked with their IFC counterparts
9 https://github.com/Alhakam/bimsage
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
through an additional ICDD linkset. The generated ontology is then enriched with ad-
ditional individuals that define damages, which affect the deteriorated stone compo-
nents, by utilizing the DOT and NSD terminology. In the test case shown in
Fig. 5, a total number of 1115 IFC entities were transformed into instances of
sco:StoneComponent. Furthermore, 227 damage individuals were created and assigned
to the ontological stone representations.
4.1 SPARQL-utilized Damage Assessment
When assessing the damaged structure, several parameters about the detected dam-
ages and the affected stone components must be considered, such as the damage type,
the stone material, or the estimated impact of the damage on the construction. To re-
trieve this information, SPARQL queries are appropriate. Two information sets are
thereby of high importance for the assessment of the addressed specific application do-
main, i.e. (1) information about the damaged area in relation to the overall façade area,
and (2) information about the damage index for each detected damage type as suggested
by Fitzner et al. (2002).
The SPARQL query in Listing 1 is used for determining the damaged area in relation
to the overall stone area of a façade. For that purpose, two sub queries are used. The
first computes the damaged area and the second the area of all considered stones. The
area values of each damage and component are asserted as literals of props:area, which
is calculated by multiplying the length and width of the corresponding entities surface.
The construction ontology generated from the BIM/IFC model determines the compo-
nent areas in advance, while the damage areas are asserted in the diagnosis process.
SELECT (?damageAreaSum / ?stoneAreaSum AS ?damageSpread)
WHERE {
#sub-query for damage area
SELECT (SUM(?damageAreaValue) AS ?damageAreaSum)
WHERE {
?damageArea rdf:type dot:DamageArea .
?damageArea dot:aggregatesDamageElement ?damage .
?damage rdf:type dot:Damage .
?damageArea props:area ?damageAreaValue .
#sub-query for stone area
SELECT (SUM(?stoneAreaValue) AS ?stoneAreaSum)
WHERE {
?damageArea rdf:type dot:DamageArea .
?damageArea props:stoneArea ?stoneAreaValue .
Listing 1. SPARQL query for calculating the damaged area in relation to the façade area
Another important assessment information is the damage index, which is calculated
according to the below formula from (Fitzner et al., 2002). Thereby, the damage index
can be calculated through a linear or progressive function. Since SPARQL does not
provide the functionality for calculating mathematical square root functions, only the
required data parameters, namely the damaged area (props:area) and damage impact
(nsd:damageImpact) are queried, and the formula is processed in a separate algorithm.
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
Fig. 6. Formulas for determining the linear and progressive damage index (Fitzner et al., 2002)
Applying the SPARQL queries on the test case ontology delivered the results shown
in Table 1.
Table 1. Detected damage types on natural stone façade of the test case
NSD-class Number of damages Damaged Area DIlin DIprog
nsd:HairCrack 207 20,7 % 1,063 2,299
nsd:StarCrack 14 1,0 % 0,050 0,500
nsd:Fracture 3 0,6 % 0,030 0,387
nsd:Lichen 1 0,4 % 0,020 0,316
nsd:Plant 2 0,5 % 0,025 0,354
4.2 SHACL-based Reasoning
Besides the general reasoning through OWL axioms that are implemented in the ontol-
ogies, additional rules can be applied through separate rule models formalized in
SHACL. This allows for a more modular use of certain rule sets, depending on domain
specific needs, such as damage assessment based on structural viewpoints or construc-
tion durability. Consequently, various rule sets for damage evaluation could be defined
and applied to the proposed ontology framework. In this regard, an exemplary rule set
has been developed and applied on the test case for the inference of potential renovation
measures that are appropriate for certain damage types and properties.
Since new individuals of the type srmo:StoneRepairTask are generated in the infer-
ence process, the SHACL-based reasoning is processed in two stages. First, it is inferred
if a stone component has a recommended renovation measure of a certain type.
Thereby, the reasons for a required renovation are primarily dependent on the charac-
teristics of damages that affect the component. Listing 2 shows an exemplary rule for
inferring filling of cracks (srmo:CrackFilling) as potential renovation measure for
stones that are affected by star cracks (nsd:StarCrack) and have a damage impact value
between 2 and 3 as estimated during inspection. If the conditions are fulfilled a new
statement is added, in which the sco:Stone instance is related to a new object through
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
an auxiliary object property like rm:needsCrackFilling, which is defined as subproperty
of cto:isSubjectOf inside SHACL.The URI of the new individual is generated randomly
via a SPARQL function. In the second reasoning step, the previously inferred individ-
ual is further characterized (see Listing 3). Thereby, it is important that an appropriate
subclass of srmo:StoneRepairTask is assigned.
sco:Stone
a rdfs:Class , sh:NodeShape ;
sh:rule [
a sh:TripleRule ;
sh:order 1 ;
sh:subject sh:this ;
sh:predicate cto:isSubjectOf;
sh:object [rm:RandomURIGeneration] ;
sh:condition [
sh:property [
sh:path dot:hasDamage ;
sh:class nsd:StarCrack ;
sh:qualifiedValueShape [
sh:path nsd:damageImpact ;
sh:minCount 1 ;
sh:minInclusive 2 ;
sh:maxInclusive 3 ; ] ;
sh:qualifiedMinCount 1 ; ] ; ] ] .
Listing 2. SHACL Rule for inferring crack filling measure for deteriorated stones
rm:UnclassifiedRenovationMeasure
a sh:NodeShape ;
sh:targetObjectsOf rm:needsCrackFilling ;
sh:rule [
a sh:TripleRule ;
sh:order 2 ;
sh:subject sh:this ;
sh:predicate rdf:type ;
sh:object srmo:CrackFilling ; ] .
Listing 3. SHACL Rule for classifying an inferred renovation measure individual
In a next step, SHACL rules for inferring appropriate renovation measures were applied
on the damage ontology. Thereby, for each instance of sco:StoneComponent that is
affected by at least one damage instance, a corresponding set of renovation measures
was assigned (see Table 2).
Table 2. Inferred renovation measure recommendations in the test case
SRMO-class Retrofitted Damage Types
srmo:CrackFilling nsd:HairCrack ; nsd:StarCrack
srmo:StaticConstructiveMeasure nsd:Fracture
srmo:StoneCleaning nsd:Plant ; nsd:Lichen
Some inferred renovation classes comprise of multiple sub-measure representations
that could be applied. For instance, the class srmo:StoneCleaning has subclasses such
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
as srmo:MechanicalCleaning or srmo:ChemicalCleaning which in turn also have sev-
eral subclasses. Consequently, all subclasses of an inferred renovation measure are rec-
ommended as potential retrofitting solutions. Furthermore, it must be emphasized that
the inferred measures are only recommendations. When deciding for an appropriate
renovation measure, a cost and risk analysis need to be made, which is part of the Ther-
apy phase of the retrofitting process and has not been performed in the current test case.
Nonetheless, the definition of additional rules for this subsequent step is possible and
hence a subject of further research.
5 Conclusion
In this research, a methodology has been proposed for ontologically representing
damaged structures made of natural stone in OWL and performing a logic-based eval-
uation on them utilizing SPARQL-queries and SHACL shapes. In this regard, the ex-
isting building and its aggregated components are represented geometrically through
an IFC model. By utilizing the ICDD Multimodel, the IFC model is linked with an
OWL ontology that functions as semantic model for storing information about the to-
pology, the damage entities that are detected on each stone, as well as additional
metadata. Currently, the damage assessment is focussed on the reasoning of recom-
mended renovation measures. An inference of information through additional ontology
extensions and rules, such as reasoning the consequential damages from the current
damage state, or a detailed classification and risk analysis based on detected damage
properties would be a conceivable enhancement of the presented approach and is sub-
ject of further research. Furthermore, the ontology framework and evaluation queries
and rulesets have been designed specifically for the assessment of natural stone struc-
tures. In future research, this approach could be extended for other construction or dam-
age types.
Acknowledgments
This research work was enabled by the support of the German Ministry for Education
and Research (BMBF) in the frames of the BIM-SIS project (Grant no.
01IS18017/2018) and partially by the preceding EU FP7 program in the frames of the
eeEmbedded project (Grant no. 609349/2013).
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