2024 European Conference on Computing in Construction
Chania, Crete, Greece
July 15-17, 2024
A DIGITAL FRAMEWORK FOR GENERATING AND EVALUATING DIGITAL
CIRCULAR TWINS
Anastasia Wieser1, Sophia Pibal2, Stefan Schützenhofer1, Matteo Bosco3 and Iva Kovacic1
1Department of Integrated Planning and Industrial Building, TU Wien, Vienna, AUT
2AIT Austrian Institute of Technology GmbH, Vienna, AUT
3Department of Visual Computing and Human-Centered Technology, TU Wien, Vienna, AUT
Abstract
The Architecture, Engineering, and Construction indus-
try is crucial in promoting circular economy principles to
mitigate resource scarcity and negative environmental im-
pacts. Taking the lead in the global extraction of rawmate-
rials and making significant waste contributions, the sec-
tor incorporates circular economy assessments into build-
ing design decisions. This paper introduces a concept
using visual programming language for End-of-Life al-
gorithms linked to Building Information Modelling Data.
The goal is to create a decision-support tool for the early
design stage using common architectural software cou-
pled to visual programming. The results display a com-
putational solution for implementing relevant parameters
through utilised software.
Keywords: Circular economy, digital twins, generative de-
sign, virtual reality, digital ecosystem, end of life, material
building passports, EU taxonomy
Introduction
The growing interest in the circular economy (CE) repre-
sents a paradigm shift from the prevailing linear economy,
driven not only by the need for assessment for several certi-
fications and EU regulations but also because of its signif-
icant advantages compared to the traditional linear model
(United Nations Environment Programme, 2022). These
benefits extend to environmental, social, and economic
concerns, addressing crucial challenges such as resource
scarcity and environmental degradation (Kovacic et al.,
2020a; Ellen MacArthur Foundation, 2024; Gorgolewski,
2018; Korhonen et al., 2018; Cramer, 2022). Defining
the basic concepts of circular economy and sustainabil-
ity is essential to provide a framework for this research.
The Brundtland Report (1987) offers a widely recognised
definition of sustainable development as ”development
that meets the needs of the present without compromis-
ing the ability of future generations to meet their own
needs.” (Brundtland, 1987, p.37). Barbier (1987) was
among the first to describe sustainability’s three pillars:
economic, environmental and social, with this paper con-
centrating on the environmental aspect (Barbier, 1987).
Cramer (2022) introduces the 10 R’s, which ranks circu-
larity actions by their impact, placing reuse higher than
recycling due to its more significant contribution to cir-
cularity (Cramer, 2022). Moreover, Geissdoerfer et al.
(2017) define Circular Economy as ”a regenerative sys-
tem in which resource input and waste, emission, and en-
ergy leakage are minimised by slowing, closing, and nar-
rowing material and energy loops. This can be achieved
through long-lasting design, maintenance, repair, reuse,
remanufacturing, refurbishing, and recycling.” (Geissdo-
erfer et al., 2017, p.759), positioning it as essential for sus-
tainability.
A transition to a circular system is particularly crucial in
the construction industry, contributing to around 37% of
global energy consumption and process-related CO2 emis-
sions (United Nations Environment Programme, 2022).
Furthermore, this industry’s significant role in resource
depletion, being the largest contributor to global raw ma-
terial extraction (Almeida et al., 2016), highlights the need
to transition to circular systems. However, this transition
challenges existing design processes and policy frame-
works, making revisiting and altering current approaches
necessary (Korhonen et al., 2018). Therefore, there is a
need for a framework to better include circular economy
principles, such as reuse or reduce and especially guide
cooperation between designers, fabricators and clients in
the construction industry (Svilans et al., 2019; Çetin, 2023;
Geissdoerfer et al., 2017; Çetin et al., 2021).
Digital tools are seen as critical enablers for efficient data
sharing, monitoring, optimisation and enhanced commu-
nication between stakeholders throughout the design and
construction process (Antikainen et al., 2018; Antova and
Tanev, 2020; De Wolf et al., 2020; Martínez Rocamora
et al., 2021; Çetin, 2023). Moreover, initiatives like Eu-
rope’s Digital Decade (European Commission, 2021) and
the 2020 EU CE Action Plan highlight the importance
of digital tools in promoting sustainability and support-
ing the circular economy (European Commission, 2021;
Çetin, 2023).
As a result, finding computational solutions to embrace
a circular economy is among the most recent topics in
the Architecture, Engineering and Construction (AEC) in-
dustry, as it can tackle today’s pressing environmental
problems (De Wolf et al., 2020; Kovacic et al., 2020a;
Cramer, 2022). Building Information Modelling (BIM)
and Material Passports, for example, promise to address
sustainability barriers and facilitate the automatic inclu-
sion of reliable datasets (Honic et al., 2019; Çetin, 2023;
Martínez Rocamora et al., 2021; Santos et al., 2019). How-
ever, there are challenges in integrating these tools, partic-
ularly when considering one of the most commonly used
methods, Life Cycle Assessment (LCA) (Santos et al.,
2019). The reliability of LCA results is affected by un-
certainties coming from assumptions, such as energy con-
sumption and product lifespan. Additionally, challenges
in integrating specific, local information from manufac-
turers and the lack of detailed data on material compo-
sition in the early design stages further affect the accu-
racy of LCA outcomes (Martínez Rocamora et al., 2021;
Santos et al., 2019; Honic et al., 2019). This highlights
the need for more flexible and comprehensive tools within
the BIM environment to simplify the integration of envi-
ronmental analysis in the design process (Martínez Ro-
camora et al., 2021; Çetin, 2023). Research into BIM-
based LCA tools shows that most require a combination
of BIM and other software to measure environmental im-
pacts effectively. This process, while beneficial, often re-
quires manual input and thus is time-consuming, contrary
to the goal of automating these assessments (Martínez Ro-
camora et al., 2021; Honic et al., 2019). The need for a
multidisciplinary approach comes with its hurdles, as the
link between digital tools and circular economy principles
requires skills spanning environmental science, architec-
ture, engineering, and programming - raising another lim-
itation: the interoperability issue between different soft-
ware (Santos et al., 2019). Nevertheless, this collaborative
effort is vital for developing tools that are both functional
and beneficial (Martínez Rocamora et al., 2021; Ilhan and
Yaman, 2016). Despite the identified potential of digital
tools to integrate circular economy principles in the AEC
industry, there remains a significant gap in creating user-
friendly, integrated solutions that effectively bridge the gap
between theoretical concepts and their practical applica-
tion. This highlights the ongoing academic and practi-
cal need for research to develop accessible tools that en-
able sustainable construction practices within the circular
economy framework(Ilhan and Yaman, 2016; Çetin, 2023;
Martínez Rocamora et al., 2021).
The ongoing research presented in this paper addresses
these challenges by aiming to optimise architectural de-
signs in terms of circularity and sustainability in the early
design stage, as it promises fundamental advantages, rep-
resenting a vital phase where materials can enter (or re-
enter) a new life cycle, and design concepts are still in
their initial adjustable stage, thus more flexible to adapt
(Gorgolewski, 2018; Honic et al., 2019; Çetin, 2023; Çetin
et al., 2021). This paper emphasises the need to flexi-
bly document, validate and benchmark environmental im-
pacts to ensure current activities support a sustainable fu-
ture and bridge the gap between abstract principles and
practical implications (Çetin, 2023; Geissdoerfer et al.,
2017). Therefore, this paper considers key indicators, in-
cluding the building’s overall mass, Global Warming Po-
tential (GWP-total), Acidification Potential (AP), and Pri-
mary Energy Non-Renewable, Total (PENRT). These indi-
cators were chosen because they are used in the Austrian
klimaaktiv OI3 Eco Index calculation to assess the envi-
ronmental impact of materials (IBO – Österreichisches In-
stitut für Bauen und Ökologie, 2023), and are also relevant
for compliance with the EU Taxonomy classification sys-
tem (European Commission, 2024).
The concept of this research merges Algorithm Aided De-
sign (AAD), Building InformationModeling (BIM) (Pibal
et al., 2022), and Virtual Reality (VR). It supports sustain-
able architectural planning by automating variant genera-
tion, conducting circularity assessments, and using a VR
platform interface to facilitate decision-making in the early
design stage, aligning with Circular Economy (CE) princi-
ples and objectives. The framework of the project is based
on:
1. a BIM-based architectural model (in software Archi-
CAD)
2. an End-of-Life (EoL) design algorithm to conduct
the variant study generating an assessable geometric
twin, an identical digital illustration of the variant in
the VR platform
3. an EoL- assessment algorithm for attributing and
evaluating different building variants
4. VR platformwith includedVirtual Agent for planning
support
5. An enriched spreadsheet-based object catalogue con-
taining relevant data from material databases such as
the Austrian database ”baubook” (baubook, 2024),
essential for conducting defined assessments
This research builds on the previous publication, Wohnen
4.0 (Pibal et al., 2023), Digital Ecosystem to enable Cir-
cular Buildings – The Circular Twin Framework Proposal
(Schützenhofer et al., 2024) and Digital Platform for Af-
fordable Housing - a Framework Proposal (Kovacic et al.,
2020b). The overall context established is visualised in
Figure 1, which provides an overview of the proposed
framework. This paper focuses on Module 2 and Module
3, shown in Figure 1. These EoL algorithms are imple-
mented in the visual programming environment Grasshop-
per 3D within Rhinoceros 3D and their connection to the
architectural design model in ArchiCAD. It discusses the
challenges of incorporating these EoL algorithms to en-
hance the effectiveness of circular design approaches to
incorporate crucial data regarding building materials and
their reuse potential, as well as Life Cycle Assessment
(LCA) and EU taxonomy compliance (European Commis-
sion, 2024).
The paper is structured as follows: First, the methodology
section reviews the different digital tools used and outlines
the intended workflow between Grasshopper and Archi-
CAD. Followed by illustrating in more detail the steps cre-
ated in the EoL algorithms to generate the needed data flow
between the programs. Finally, the paper discusses find-
ings and suggests ways for further improvement in inte-
grating these algorithms effectively.
Methodology - Data Integration for EoL- Al-
gorithms
The method is an interdisciplinary research approach. A
specific set of digital tools has been selected to facilitate
the research objectives. ArchiCAD, employed as a BIM-
model and digital planning tool (IFC data/OpenBIM), was
selected not only because it is one of the most used digital
tools in the AEC industry but also for its ability for efficient
data storage and its inclusion of essential data required
Figure 1: Overview of the proposed framework and software interfaces
for diverse Life Cycle Assessment calculations. Addi-
tionally, Grasshopper as a visual programming tool, with
an established Live Connection to ArchiCAD (Graphisoft
Deutschland, 2024), handling spreadsheet-based docu-
ments and including Python/C# script, has been incorpo-
rated because of its multifaceted benefits. Furthermore,
Grasshopper enables the Virtual Agent in the VR platform
to access and modify various components, a crucial re-
quirement for this project. This selection of tools was anal-
ysed, leading to the development of an initial prototype
of End-of-Life (EoL) algorithms by utilising these diverse
interfaces. The conducted literature review led to assess-
ment collection (LCA), which defined the necessary data
and basic information and connections required to create
the Grasshopper script obtained from the BIM model, are
shown in Figure 2 and described below.
Data Input and Model Reconstruction
• Extracted from the architectural design model:
Basic geometric data, the quantity of elements es-
sential for subsequent calculations, and spatial refer-
ence line accurately generating the geometric twin in
Grasshopper for access by the Virtual Agent in the
VR platform.
• From ArchiCAD composites (products):
The composite name, crucial for efficient product
search within the spreadsheet-based object catalogue
and the retrieval of accurate data for LCA calcula-
tions, and the dimensions of the (new) elements, nec-
essary for precise volume calculations used in mass
determination.
Using basic geometric data, the visual geometric twin of
the preliminary architectural design is established within
the Rhino/Grasshopper environment. This is achieved
through the Grasshopper – ArchiCAD Live Connection
(Graphisoft Deutschland, 2024) and direct model integra-
tion. Recreating the architectural design in Grasshopper
is crucial because it cannot perform basic operations with
ArchiCAD components. As a result, the geometric infor-
mation must be filtered, and the model reconstructed ac-
cordingly in the Grasshopper environment.
Algorithm Development
a) EoL- design algorithm
After successfully incorporating the geometric form of the
case study and the subsequent visualisation of the ’circu-
lar twin’, the process continues with the extraction of de-
sign parameters derived from the data imported from the
case study and the BIM-based object database created in
ArchiCAD. This process uses several components from
the ArchiCAD Plug-in within the Grasshopper environ-
ment while maintaining a live connection to ArchiCAD.
This allows users to select different materials and compo-
sitions from the BIM object database to visualise and gen-
erate different variants of the initial case study.
b) EoL- assessment algorithm
In the following part, connecting the user-selected data
from the EoL- design algorithm and assessment informa-
tion, such as the Key Performance Indicator GWP(total)
for an LCA calculation through an EoL- assessment algo-
rithm, is essential.
Visualisation - Virtual Reality Integration
The visualisation aspect of the conducted research is not
the main focus of this paper. However, it is essential to
emphasise that during the development of the algorithm,
we needed to consistently consider that the Agent within
the VR platform, who interacts with the user, must be able
to access and control changeable parameters in Grasshop-
per.
Framework
To better understand the implementation within the vi-
sual programming environment Grasshopper, Figure 3 dis-
plays a simplified version of the Grasshopper script and its
structure, focusing on wall elements within a test build-
ing. Achieving a seamless intersection with the BIM
model data required the utilisation of a custom Plug-
in within Grasshopper, tailored to receive and process
data from ArchiCAD dynamically via the bi-directional
Grasshopper-ArchiCAD Live connection was necessary
(Graphisoft, 2023).
The framework described will be further validated through
its application to a specific use case, showcasing its practi-
cal utility and effectiveness in a real-world scenario. This
use case involves an architectural design project where the
integration of circular economy principles is paramount.
By implementing the digital framework within this con-
text, the seamless interaction between Grasshopper and
ArchiCAD facilitates a comprehensive analysis and re-
configuration of wall elements, demonstrating the frame-
work’s capacity to adapt and optimise based on circular
design criteria. This application not only highlighted the
robustness of the custom Plug-in and bi-directional live
connection but also underscored the potential of the devel-
oped EoL algorithms to significantly enhance the sustain-
ability of architectural projects through informed decision-
making and design optimisation. The following section
describes parameter mapping, variant and assessment gen-
eration of our status quo.
Concept of Parameter Mapping
In order to integrate ArchiCAD element types, such as
walls, facades, or columns, from the BIM model, it is nec-
essary tomanually select each architectural design element
per type for every floor within ArchiCAD, done by right-
clicking on the parameters used for including a collection
of ArchiCAD element types. This method enables the user
to subsequently change each element type on each floor
separately. However, this initial step cannot be achieved
using the Agent in the VR platform, as it cannot access
components through right-clicks.
After successfully incorporating all elements, the subse-
quent phase involves extracting crucial information. The
custom components within the ”Deconstruct” group of the
Live Connection Plug-in were required to achieve this.
Each element type parameter was linked to its correspond-
ing ”Deconstruct” component to obtain further details, in-
cluding Brep (Polysurfaces) and reference lines. The ob-
tained information is used to generate new variants at the
exact location and assemble the visual digital geometric
twin of the architectural design by merging all ”Brep” out-
puts, which can be accessed by the Agent and displayed in
the VR platform.
However, it turned out that retrieving the net volume, cru-
cial for assessment methods, was not achievable using this
method. Thus, a custom property set (CPset) had to be in-
cluded within ArchiCAD to automatically retrieve the net
volume for every element via the ”Get Property Settings”
component in Grasshopper. This workaround enabled the
inclusion of the missing geometrical data in the script to
calculate the overall mass of the building design.
Notably, during the development of the Grasshopper
script, to ensure the accurate functionality of new connec-
tions, minimise dataflow time, and maintain proper data
structuring. Initially, the focus was on a single ArchiCAD
element type (wall) and one floor. Later, the other Archi-
CAD element types were integrated for each floor.
Concept of Variant Generation
The tailored Grasshopper component ”Composite” repre-
sents products from the current ArchiCAD project and is
used to include a selection of products from the object cat-
alogue in the algorithm, for which information is available
for the evaluation calculation. In order to make it acces-
sible to the Virtual Agent, it was essential to link com-
ponents that the Agent could modify to the customisable
information in the script. In this case, the Virtual Agent
could use the ”Number Slider” to run through predefined
products to select from a list, facilitated by the Grasshop-
per ”List Item” component.
Once all the required information had been imported and
retrieved, it was used to generate new ArchiCAD type ele-
ments using the ’Design’ group components to create new
ArchiCAD elements, such as Walls or Columns along the
acquired reference data, creating new design variations.
Figure 3 shows a simplified representation of a variant in
the Grasshopper environment.
Concept of Assessment Generation
The final segment of the Grasshopper script involved in-
corporating crucial data to evaluate the circularity poten-
tial and environmental impacts of the user-generated vari-
ants, such as environmental product declarations (EPDs)
and material densities, which could not be retrieved
through BIM (Honic and Wolf, 2023). The spreadsheet-
based object catalogue based on material databases was
incorporated through a Python script developed for the
EoL- assessment algorithm. This script imports the den-
sity required for calculating the overall mass of the build-
ing, along with the net volume of the products obtained
through the customised CPset in ArchiCAD. Additionally,
it provides the necessary data for evaluating the designs in
terms of the circular economy. The Python script contains
the following inputs:
• The names of the new products of all the elements
modified or not modified as a list.
• The location and name of the external spreadsheet-
based database
ArchiCAD Pilotproject
Deconstruct Elements
Basic geometric data
Quantity of Elements
Reference in Space
ArchiCAD Composites
Name of Composite
Dimensions
ALGORITHM
Import
VISUALISATION
geom. digital Twin from 
case study
Merge
Initial geometry
Extract
Reference geometriesExtract
ArchiCAD CT Property Set
Net Volume of Elements
Excel- based Data
LCA KPI Data
Density of Composites
Import
Changing Composites
INPUT
Showing different Variants
Deconstruct new Elements for 3D 
information
New Variant V1
New Variant V2
New Variant Vn
Composite of chosen 
Variants
Quantity of Elements
Density of new elements
Database
Overall Mass of new 
variant
INPUT
Showing KPI information
Interaction with 
VR Agent
geom. digital Twin from 
Variant
INPUT
INPUT Volume of new elements
List of Composite for 
Variants
List of all new composites
List of all new elements (ArchiCAD 
ID)
KPI information of new 
variant
Figure 2: The process and data requirements to develop the EoL algorithms
• The Key Performance Indicator (KPI) the script
should search for, e.g., Total Global Warming Poten-
tial (GWP - total) and the Material Density
Given the constant requirement for material density, the
Python script has been duplicated. This allows the Vir-
tual Agent to use a ”Number Slider” to modify the search
input for the calculations in the first Python script while
providing a stable estimate of the total mass in the second.
However, the outputs of both scripts consist of lists outlin-
ing the requisite values for each product. Consequently,
these lists must first be aggregated using the Grasshopper
”Mass addition” script before being made accessible to the
Agent, enabling user visualisation in the VR platform.
Discussion
Throughout the development of the EoL- assessment algo-
rithm, a consistent pattern emerged: the need for practical
solutions to overcome various challenges concerning the
interaction between the software used. These challenges
primarily revolved around accessing andmanaging diverse
data formats and flows.
One notable solution involved integrating spreadsheet-
based circular economy assessment data and material den-
sity values using a Python script. This approach was nec-
essary because of the inherent limitations of extracting
such information directly from the BIM file (Honic and
Wolf, 2023) using the Live connection and its ArchiCAD
Grasshopper Toolset components or the customised CPset.
Nevertheless, while effective, this solution revealed a vul-
nerability: the dependence on the spreadsheet-based file’s
location. However, having an external database for up-
dates can be beneficial as more and more products on the
market are expected to receive environmental information
(baubook, 2024). This allows easy access to the latest data
for design decisions and variant generation with the EoL
algorithms.
To address the challenges associated with the extended
processing time of the script, which arises from heavy
data flow, including constant API interaction, visualisa-
tion of 3D elements, and execution of Python scripts for
accessing and searching external data, the generation of
variants becomes excessively time-consuming. To tackle
this issue, exploring innovative approaches becomes im-
perative. One option worth considering is integrating the
” API - Grasshopper Plug-in” from (Wilk et al., 2023),
which promises faster integration of the architectural de-
sign model and its data. However, as it relies primarily on
textual input and output, it will require a hybrid script to
ensure 3D visualisation in the VR platform.
Another challenge is the need for manual selection
of ArchiCAD elements, as explained in the section
”Grasshopper Customisation - Parameter Mapping”. This
requirement places a disadvantage on the user, requiring
access to the Grasshopper script and understanding the ba-
sic concept and workflow of the EoL algorithms. How-
ever, one method currently under development involves
a C# script in Grasshopper, connected via an Add-On in
ArchiCAD, to collect and export the required data from
ArchiCAD, which must be integrated into the developed
algorithms structured according to the Grasshopper-script
requirements. This approach would make the steps of
Figure 3: Simplified representation of the Grasshopper script using a test building focusing on four existing walls, showing how the
mass [kg] and, in this example, the embodied carbon (GWP-total) for phases A1-A3 (product stage) [kg CO2e] are calculated
picking ArchiCAD elements manually per floor obsolete,
enhance the script processing, and mitigate the risk of
overlooking elements in ArchiCAD. It may improve user-
friendliness, and other initial architectural designs can
used.
Moreover, to increase the efficiency and versatility of the
presented framework’s application, the architecture model
must be created following certain rules and structures.
This involves the creation of specific ArchiCAD layers to
more clearly distinguish between element types, such as
internal and external walls. This step is essential due to
the different structural functions of these elements and,
therefore, different dimensions and product specifications.
In addition, an ArchiCAD template should facilitate the
seamless integration of the EoL algorithms into other ar-
chitectural designs. However, it is important to note that
doors and windows have been excluded from the EoL- de-
sign algorithm for this paper. This decision is due to the
fact that the door/window ArchiCAD library part can only
be accessed and modified by right-clicking on the ”Win-
dow Settings” component in Grasshopper, which is inac-
cessible to the Agent.
The complexity is additionally increased by successfully
storing selected variants to ensure the comparability of
generated design decisions. Whenever the Agent modifies
the algorithm, the previously selected variant is replaced
with the new one. In an initial attempt to address this issue
and preserve the variants, a Python script was developed
to save generated information through a repetitive loop.
However, as the results of the loop were insufficient, ei-
ther the Python script needs adjustment or an alternative
solution must be pursued.
Table 1 outlines each limitation with its respective descrip-
tion and proposed future steps to address challenges that
occurred during framework conceptualisation.
Conclusion
This paper outlines the possibility of automated decision
support through EoL algorithms via a VR platform for de-
sign optimisation in terms of circularity and sustainability.
It successfully implemented a case study and its building
elements and obtained outcomes related to circular econ-
omy. While modifying the elements and generating a vari-
ant were successful, numerous technical and design chal-
lenges remain. However, assuming the intended changes
and workarounds are well implemented, the potential for
success is promising. It can provide a basis for further re-
search to combine digital tools to better determine the fea-
sibility of the respective interdisciplinary methods. This
way, a computational approach and strategy for continuous
environmental impact assessment in the construction in-
dustry can be developed. By addressing challenges head-
Table 1: Outline of limitations with descriptions and proposed future steps
Limitation Description of Limitation Future Steps
Integration Challenges with
Diverse Data Formats and
Flows
Managing and accessing various
data formats between software
tools presents significant chal-
lenges
Develop and implement middle-
ware or APIs that can automati-
cally translate and integrate data
across platforms
Dependence on External
spreadsheet-based File
Locations
Utilising spreadsheet-based data
for assessment data introduces a
dependency on the file’s location
Transition to cloud-based
databases for dynamic access
and updates, reducing reliance
on static spreadsheet-based files
Extended Processing Time
Due to Heavy Data Flow
The extensive data flow leads to
time-consuming variant genera-
tion processes
Optimise algorithms for effi-
ciency and explore parallel pro-
cessing to manage and process
data more quickly
Manual Selection of
ArchiCAD Elements
Manual selection of elements in
ArchiCAD for every floor adds
complexity and time to the de-
sign process
Automate the selection process
with AI or develop intuitive UIs
that simplify and expedite the se-
lection process
Structural Requirements for
Architectural Models
Specific rules and structures
must be followed for effective
integration of EoL algorithms
Create templates and guidelines
that standardise model struc-
tures, facilitating smoother inte-
gration
Exclusion of Doors and
Windows from the EoL-
Design Algorithm
Doors and windows are excluded
due to limitations in access-
ing and modifying ArchiCAD li-
brary parts
Develop enhancements or Plug-
ins for the VR platform to allow
direct manipulation of doors and
windows
Difficulty in Preserving
Selected Variants for
Comparison
Storing selected variants be-
comes complex when the Agent
modifies the algorithm, leading
to loss of previously selected
variants
Implement a versioning system
within the software that auto-
matically archives and tracks
changes to design variants
on and continuously iterating on the process, the EoL algo-
rithms move closer to facilitating decision-making in the
early design process, enabling circular building structures
and integrating End-of-Life concepts.
Acknowledgment
The research described in this paper was carried out as part
of the project ”Circular Twin - Ein digitales Ökosystem
zur Generierung und Bewertung kreislauffähiger Digitaler
Zwillinge”, funded by the Austrian Ministry for Climate
Action, Environment, Energy, Mobility, Innovation and
Technology through the Austrian funding agency (FFG),
grant number 899167, which facilitated the conduct of this
research.
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