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Levels of Mapping in Nature-informed Studies A case study on informed wall

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Nature provides a vast amount of information to be learnt in various scales with different level of complexities in architecture. Today, the increasing role of computational design and advents in new fabrication technologies enable architectural praxis to incorporate data coming from various disciplines in the design process. Among them, data coming from nature with its animate and inanimate parts are began to be revisited more than before via different approaches. In this study, information transfer from nature to architecture is described as a mapping process defined with different levels depending on the complexity of the information transfer process. Present study explains these levels and exemplifies through the study conducted in Nature-informed Computational Design course.
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Levels of Mapping in Nature-informed Studies
A case study on informed wall
Müge Kruşa Yemişcioğlu1, Arzu Gönenç Sorguç2, Ozan Yetkin3
1,2,3Middle East Technical University, Ankara, Turkey
1mugek@metu.edu.tr 2arzug@metu.edu.tr 3oyetkin@metu.edu.tr
Nature provides a vast amount of information to be learnt in various scales with different
level of complexities in architecture. Today, the increasing role of computational design
and advents in new fabrication technologies enable architectural praxis to incorporate
data coming from various disciplines in the design process. Among them, data coming
from nature with its animate and inanimate parts are began to be revisited more than
before via different approaches. In this study, information transfer from nature to
architecture is described as a mapping process defined with different levels depending on
the complexity of the information transfer process. Present study explains these levels and
exemplifies through the study conducted in Nature-informed Computational Design
course.
Keywords: Biomimetics, Geomimetics, Data Mapping, Nature-Informed Studies
INTRODUCTION
Throughout the history of design and
architecture, nature is studied/decoded, and
information gained from these studies are reflected
back into various challenges that architects are
facing with. In these explorations, responsiveness,
and fitness as a part of research on performance in
buildings become trending subjects for which
nature become the major source of information.
Responsiveness in architecture, not only as the
ability of change but also as a way to
preserve/produce energy turns to be an important
matter to re-direct architecture towards nature
(Negroponte, 1970), (Reichert, Menges, & Correa,
2015) (Blok, 2016) (Gronostajska & Berbesz, 2019).
Similarly, fitness which is defined in nature “as
complex dispositional property of organisms”
referring being an inhabitant, survival and
reproductive (Mills & Beatty, 1994) becomes a
strategy for efficiency, adaptiveness and
responsiveness in architecture (Menges, 2012)
(Öztoprak, 2018).
Today, with new observation/data capturing
technologies and increasing data crunching
capacity, we are more capable of retrieving and
processing information from nature more than ever.
Dealing with such massive data, needs new
strategies, models, classifications, and thus novel
methods. Most of these strategies mainly focus on
the direction of this information transfer as either
from biology to technology or from technology to
biology (Baumeister, 2012) (Gebeshuber, 2008)
(Oxman, 2016) (Speck, Harder, Milwich, Speck, &
Stegmaier, 2006) (Vattam, 2007) (Benyus, 2002).
Along with these strategies, classifications
depending on the scale of living being (Knippers,
Nickel, & Speck, 2016) are scrutinized. Studies are
Volume 1 – Co-creating the Future – eCAADe 40 | 271
extended to consider Form, function, performance
or behavior in nature and architecture (Zari, 2018) .
Currently these studies shift their focus on
material research (Rahimizadeh, Sarvestani, Robles,
& Ashrafi, 2022), building systems (Castriotto,
Carvalho, & Celani, 2019), component design
(Stachew, Houette, & Gruber, 2021) (Son, Kim, & Syal,
2022), and urban design (Zari & Hecht, 2020). As each
field brings unique performance criteria, different
level of complexity on the retrieved and transferred
information from nature to architecture is
unavoidable
In this context, this study proposes a new
perspective approaching nature-based studies as a
data mapping process to meet the needs of any
specific problem defined in architecture. This
approach introduces data, feature and behavior
extraction, feature matching, data modelling as
parts of this mapping process. Then, the levels
constituting the mapping process are described
from inspiration to information, from idea to
knowledge in accordance with the complexity of the
problem.
Levels are also implying strategies both for
learning from animate or inanimate nature, based
solely on data transfer, whatever the strategy is (top-
down or bottom-up). The only scale that defines the
levels is the complexity which arises from the
problem of concern, captured information, and
character of the information transfer process.
The role of the context and understanding of
scale, which is defined as a measure of complexity in
nature-informed studies, are found vital. Here, this
scale determines how far the mapping process can
go from inspiration to information.
MAPPING IN NATURE-INFORMED
STUDIES
As briefly introduced, the proposed approach
aims to describe nature-based studies in the realm of
architecture as a data transfer process. Focusing on
data and data transfer, domain is defined as the
dataset consisting of captured data in nature and
similarly, codomain is the datasets of objectives,
constraints of the targeted problem/performance.
Accordingly, term mapping is employed as the
synonym of transfer function (Weisstein, 2022)
denoting information transfer from nature (N) to
architecture (A):
𝑓:𝑁↦𝐴 (1)
Frequently, asymmetry in the scale of nature and
architecture in terms of their complexities is the
Figure 2
Some of the
examples of feature
matching, retrieved
from archdaily.com
Figure 1
Levels of Data
Mapping
(developed by
authors)
272 | eCAADe 40 – Volume 1 – Co-creating the Future
major concern of the proposed mapping process. In
general, obtaining related data, associating this data
with its context, modeling and reflecting the
information into architectural domain are seen as
the main issues.
In relation with the problem, the complexity of
the mapping process varies. As the depth and the
complexity of the acquired/required information is
increased, the transfer process from domain (nature)
to codomain (architecture) changes from inspiration
to information, and likewise, the codomain/
outcome turns from idea to knowledge. This study
refers these two levels as (1) Feature Matching, and
(2) Model Mapping which determines the
complexity of the transfer process.
In feature matching, the process includes
single or multiple features to be related among
domain and codomain, disregarding their
dependencies, coherences, and impacts on each
other, and thus mostly resulting visual
resemblances.
It is seen that, in feature matching the behavior
of the subject of interest is mostly not to be
conveyed into codomain. Hence, feature matching
in architecture is mostly depicted as formal
inspirations like in tiling, ornaments etc. From this
perspective, the studies shown in Figure 2 can be
acknowledged as the examples of feature mapping
in different scales from tiling to façade, and from
building to port design resembling/imitating
animals, plants, and textures.
For the model mapping process, on the other
hand, features are transferred among domains with
their dependencies, coherences, and impacts to
each other in regard with their context according to
the observed behaviors, performances, and
functions. In other words, models need to be
constructed for the information transfer. In this
approach, success of transfer, precision, level of
information in the process are closely connected
with the requirements of the problem, captured data
in the reference domain and the relation found
among them rather than the complexity of the
model.
As presented in Figure3, Stuttgart Airport
Passenger Terminal, Germany (1996) designed by
Meinhard von Gerkan with its tree-like columns, ICD-
ITKE pavilions: HygroScope: Meteorosensitive
Morphology (ICD), and “HydroSkin” mapping the
hygroscopic actuation of plant cones (Reichert,
Menges, & Correa, 2015) and Eastgate Centre
designed by Mick Pearce in Harare, Zimbabwe (1996)
modelling the self-cooling mounds of African
termites for less energy consumption (Doan, 2012)
can be counted as examples of model mapping.
There are also number of machine learning
applications that can be counted in this level like
“Machine Learning Model Inspired by Insects’
Nervous Systems” (Hannes & Paul, 2020) or the study
focusing on climate change to understand dynamic
systems (Sibanda, 2020).
To sum up, these examples illustrates that the
proposed schema can help to understand the
complexity of information transfer as well as the
impact on the final outcome. This understanding
may help to contribute to nature-informed studies
incorporating various methods coming from data
sciences (*author’s publication).
Moreover, nature-informed studies have already
been adapted into the curricula of schools in
different levels (Speck & Speck, 2021). The schema
explained here is applied to the outcomes of the
course developed and offered by the authors named
“Nature-informed Computational Design”.
Figure 3
Some of the
examples of model
mapping [1][2][3][4]
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FROM NATURE TO WALL COMPONENTS:
A SERIES OF MAPPING STUDIES
In this part of the study, the mapping process
explained briefly above was implemented in the
course named “Nature-informed Computational
Design "conducted with 41 students from METU
Department of Architecture. Students were initially
given an ill-defined problem as “the wall” that its
function was expected to be defined by the
students. Within the very first weeks of the study, the
function of the wall was determined as “collecting
the water”. Following this stage, groups consisting of
2-3 students design various components of the wall
referring to different references in nature.
Initially, the discussions were based on well-
known studies of Benyus (2002), Knippers et. al.
(2016), Zari (2018) and Speck et.al. (2021) together
with numerous examples and applications in the
context of computational design approach.
Moreover, students are also acquainted with
concepts like emergence, swarm behavior,
stigmergy etc., Also, along with these discussions,
the notion of model, the precision of model and
information transfer as well as feature-behavior
extraction, are discussed in depth.
Class discussions guide students to develop
their own strategies for which the complexity of the
process is determined by their own targeted
performance which yield different levels of
information transfer process exemplifying
aforementioned “levels of data mapping”.
The features and behaviors of the following
natural beings are studied; sunflower, succulent,
sponge, namib beetle, butterfly, cell cycle,
hummingbird, fiddlehead, glow-worm, bismuth
crystal, tent caterpillar, neuron, mimosa pudica,
slime mold, blood vessels. Information gathered in
these studies are then used to develop wall
components, which later merged to constitute the
adaptive wall system as it is aimed. Each entity’s
mapping process has a different purpose and
workflow, and therefore each study constructs a
different level in the introduced schema.
The studies required to extract one or multiple
features in relation with their aim are presented in
Table 1 and the studies examining and modeling the
natural behaviors and mapping these models into
their targeted performance are listed in Table 2.
Among the feature matching examples; the
sponge module is shown in Figure 4. As it can be
seen in this example, the features related with the
form of the sponge is directly matched with the form
of the component.
For the sunflower part, which is an example of a
model mapping process, the location is studied and
modeled by the students (Figure5). This model is
mapped into a kinetic model being controlled with
sensors, motors and Arduino board depending on
the wind direction. This movement was found
important for the students to enhance the water
preservation capacity of the wall.
The modules, which are based on 15 natural
beings, are tested not only on the digital
environment through simulations, but also with
functional prototypes revealing the outcome
whether the resultant models respond to the
students’ targeted performances i.e., form, kinetic
behavior, heat distribution etc.
Figure 4
Feature matching
of sponge module
Figure 5
Model Mapping of
sunflower module
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Table 1
Feature Matching
Examples
Volume 1 – Co-creating the Future – eCAADe 40 | 275
Table 2
Model Mapping
Examples
276 | eCAADe 40 – Volume 1 – Co-creating the Future
Finally, all these modules are brought together on a
single wall by finding and defining the relations and
dependencies with each other (Figure6). The wall
which is called “Frankie” with reference to the
monster of Victor Frankenstein is composed of 16
components informed by 16 different references
from animate and inanimate nature (Figure7). In this
process, students had also experienced their design
in immersive environment to assess their design in
terms of its scale and visual characteristics.
To conclude, Nature-informed Computational
Design course is an example of current approach. It
is fair to say that the task given students which is
merging the components was almost an impossible
one, especially during lock down and only working
on online collaboration boards and meeting in
online platforms. Yet, they succeeded in finding the
correlations and common purpose to link their
findings and their designs in one project. Hence,
students achieved the following objectives of the
course: the ability to understand, decode any
information in nature and transfer them into
architecture following the phases of modeling
process namely problem definition, abstraction/
simplification, mathematical/ computational
modeling, and assessment.
Figure 6
The relations
between the
components to
form the wall
Volume 1 – Co-creating the Future – eCAADe 40 | 277
CONCLUSION
In this paper, levels of data mapping are
introduced based on data transfer from nature to
architecture in relation with the complexity of
problem and thus the process. Although this model
is developed for nature-informed studies in
architecture, it is likely to be applicable to be used in
any information transfer process among two
different domains.
It is important to note that the levels introduced
in this study varies according to the problem of
concern. Therefore, the structure of the transferred
data is solely dependent on the problem that direct
designers towards the nature in the first place. It is
well known that a good model is just complex as it is
necessary. Henceforth the transfer processes are
required to be designed as complex as necessary.
This fact is emphasized in the class several times and
reflected back the works in the conducted design
studio.
As a result of examination of current studies and
the case study on wall components, this mapping
definition is found applicable for many existing and
future studies aiming to transfer information.
However, the applications using biomaterials as in
Hy-Fi: The Organic Mushroom-Brick Tower (Stott,
2014) or co-creation with animals like Silk Pavilion,
Silk Pavilion II and plant-based materials like Bio-
Plastic Column (Oxman, Neri Oxman, 2022) are the
exceptions considering natural beings take a direct
role and thus, scale difference does not cause a
problem and they are not included in the proposed
taxonomy.
To sum up, the proposed approach can serve to
deepen the discussions on nature-informed studies
and possible new strategies, models, and methods.
ACKNOWLEDGEMENT
As authors, we would like to thank our students
who put enormous effort and experienced this
extraordinary process with us; Ahmet Batuhan
Akdemir, Aslı Zeynep Doğan, Yaşar Emir Karcı,
Ahmet Öztürk, Yiğit Akyol, Ege Doğan, Anıl Koç,
Ceren Şahin, Handan Akyürek, Uzay Doğan, Canberk
Kocaoğlu, Zeynep Şan, Özge Altuntop, Gizem Elbiz,
Atike Yağmur Köseoğlu, Ömer Faruk Secim, Esra
Zehra Aras, Defne Erçetin, Alireza Maali Esfangareh,
Ege Soyer, Davut Balcı, Meryem Eroğlu, Onurcan
Mızrak, Ezgi Tuncay, Beyza Bozkurt, Dilara Güney,
Nihan Mutlu, Ozan Uysal, Merve Bozkurt, İrem
Hancıoğlu, Mehmet Oğuz Nas, Egemen Yıldırım,
İlkim Canlı, Fatma Şule Kalyoncu, Reyhan Nazlıaydın,
Hüseyin Mert Yılmaz, Şevval Çöloğlu, Ahmet Can
Karakadılar, Behice Nur Özer, Abdullah Zamir,
Oğulcan Dandan.
Figure 7
Overall wall design
with the integrated
components
278 | eCAADe 40 – Volume 1 – Co-creating the Future
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In this paper, we reflect on the concept of nature that is presupposed in biomimetic approaches to technology and innovation. Because current practices of biomimicry presuppose a technological model of nature, it is questionable whether its claim of being a more ecosystem friendly approach to technology and innovation is justified. In order to maintain the potentiality of biomimicry as ecological innovation, we explore an alternative to this technological model of nature. To this end, we reflect on the materiality of natural systems and explore a natural model of nature, which is found in the responsive conativity of matter. This natural model of nature enables us to conceptualize biomimicry as conative responsiveness to the conativity of the biosphere PLEASE SEND AN EMAIL TO VINCENT.BLOK@WUR.NL IF YOU WANT TO RECEIVE A PDF OF THE EDITED/FORMATTED VERSION.
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This book comprises a first survey of the Collaborative Research Center SFB-TRR 141 ‘Biological Design and Integrative Structures – Analysis, Simulation and Implementation in Architecture’, funded by the Deutsche Forschungsgemeinschaft since October 2014. The SFB-TRR 141 provides a collaborative framework for architects and engineers from the University of Stuttgart, biologists and physicists from the University of Freiburg and geoscientists and evolutionary biologists from the University of Tübingen. The programm is conceptualized as a dialogue between the disciplines and is based on the belief that that biomimetic research has the potential to lead everyone involved to new findings far beyond his individual reach. During the last few decades, computational methods have been introduced into all fields of science and technology. In architecture, they enable the geometric differentiation of building components and allow the fabrication of porous or fibre-based materials with locally adjusted physical and chemical properties. Recent developments in simulation technologies focus on multi-scale models and the interplay of mechanical phenomena at various hierarchical levels. In the natural sciences, a multitude of quantitative methods covering diverse hierarchical levels have been introduced. These advances in computational methods have opened a new era in biomimetics: local differentiation at various scales, the main feature of natural constructions, can for the first time not only be analysed, but to a certain extent also be transferred to building construction. Computational methodologies enable the direct exchange of information between fields of science that, until now, have been widely separated. As a result they lead to a new approach to biomimetic research, which, hopefully, contributes to a more sustainable development in architecture and building construction.