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Use of parametric approach for user-oriented development in building design: preliminary investigations
Building design is a highly interdisciplinary research field integrating technological, architectural, structural, social and other aspects. Participatory design, or co-design, already used in other disciplines, is now facilitated by the diffusion of Building Information Modelling which offers greater control of the interdisciplinary aspects in building design. But unlike other disciplines, architecture is characterized by a high number of requirements, partly formalizable, quantifiable and optimizable and partly only intuitive. Furthermore is difficulty to employ a collaborative design framework because designer and end user work on different knowledge levels: one works on satisfying classes of requirements, and the other is unable to abstract his needs and therefore properly formalize requirements or desires. The use of simple parametric models in the pre-design phase, based on algorithms capable of generating geometries dependent on multiple modifiable variables, could overcome this problem. This paper offers a preliminary investigation on the possibility of integrating bottom-up design aspects by giving parametric models to possible end users and allowing them to explore the design space, identifying preferential outputs and overcoming some of their technical gaps. Working in parametric environments in the pre-design phase opens to the integration of tools such as evolutionary multiobjective optimization algorithms (EMOA). New fitness functions can be defined to bring design closer to the end users’ proposed outputs without neglecting performance optimization, which is typical in parametric design. The framework proposed differs from existing “product configurator”, used in industrial design, which allows the personalization of aesthetic characteristics. This paper aims at a greater understanding of the end user’s will for satisfying them better in the subsequent design phases. The technological tools currently available to make this framework possible will be analysed, identifying shortcomings and problems, along with methodological implications.
1. Introduction
This
paper aims to study a new framework of design development in the architectural
field, capable of satisfying the needs of the end user with a participation
process based on the use of parametric models. The expected results are
indications that the designer can apply in the various design phases. The whole
proposed framework is close to the principles of open-source architecture
(OSarc) (Ratti, et al., 2011) in particular to the development of designs that
are optimized in the terms set by the users themselves. This type of
involvement allows users to propose project output and try to engage them in
the whole generative logic behind the project.
This new
role given to the end user is fundamental, as it underlines the detachment from
traditional participation methods based on the involvement in the form of “questionnaires
and working group” that are often too abstract and could generate not in
my backyard (NIMBY) attitudes. The proposed framework instead seek to give
to the end user a simplification of what is the design process in progress,
through the use of modern means of participation. Furthermore, the author is
convinced that other than the aimed degree of participation, it is important
also the medium used to obtain it (McLuhan, 1964).
The
proposed framework can be considered as a platform to involve a plurality of
subjects, intercepting the social dimension of sustainable development (United
Nations General Assembly, 2015) as well as the objective of Smart Governance
(Giffinger, et al., 2007)
2. Parametric approach
The
parametric approach to architectural and engineering project is based on
linking the final output of a design to generic initial variables, named Px
in Figure 1, through the execution of instructions which can be in the form of
algorithms, programming codes, graphs and more. By combining the different
states that the parameters can assume, it is possible to associate to a single
set of instruction a large number of outputs. These outputs can be analyzed to
identify the best solutions comparing fitness function that may concern
structural, environmental and economic aspects and much more. The set of
possible outputs is called design space, which also needs to be designed
in the meta-programming phase. Is clear the parametric approach to
architectural and engineering project concerns more an “attitude of mind” than
a set of tools (Whitehead, 2010). With this approach, it is necessary to
integrate different disciplines from the earliest design stages for the
instruction sets to produce enough defined outputs. This can put the
traditional authoritarian model in difficult, and so became necessary to study
processes in which the designer also assumes the role of a “director” (Sacchi,
2015) who receives input from different subjects. In the proposed framework
input from end users are received.
3. Proposed design framework
In the
typical parametric approach (Tedeschi, 2014), we design with a loop, dark part
of Figure 1, based on parameter variations, output
generation following instruction and analysis of the results obtained. In the
proposed framework, the author theorises of developing a parallel design loop,
based on simplified parameters, instruction and outputs. Through these parallel
instruction, the end user can propose design solutions characterized by a high
level of geometric definition. This new
set of outputs can be analyzed with innovative tools, described in this paper,
to orient the design space exploration in the parametric approach.
Figure 1 – Proposed design framework. In dark
the typical steps of the parametric design process proposed by A. Tedeschi
(2014). In white the implementations proposed by the author in which the
subjects involved in the partecipation operate.
1.1. Expectation
The
author claims that the proposed framework can improve end user satisfaction by
generating positive effects in several fields. First, the end user is pushed to
work with a visualization of a real design object. This can bring to the end
user a better understanding on the process that the designer uses to respond to
his requirements. Using the widely known schematization in Figure 2 (Alexander, Notes on the synthesis of form,
1972), in the proposed framework the end user would work on the “actual world”,
using a mental and diagrammatic project that the designer has already
developed: this means that the end user is not able to explore an indefinite
design space, but will be able to move in a field of solutions already
validated in the meta-programming phase. This can allow the end user to
overcome some of his technical gaps compared to the designer.
Figure 2 -Role of the designer and end user of
the framework proposed in this paper in Alexander’s self-conscious design
scheme
A second
mechanism that the proposed framework can generate is an increased involvement
in the participatory process. In the past, attempts have been made to involve
the end user in the knowledge not only of the final output but also of the pattern
language that the designer uses to achieve it (Alexander, Silverstein,
Angel, Ishikawa, & Abrams, 1975). Some limits that these experiments have
faced are now surmountable thanks to the use of a parametric-based design
environment as a platform for implementing the participation process.
A
further enabling factor is a greater ability to interpret the generated output
thanks to developments in the field of Big Data Analysis. Fundamentally,
today we can increase the number of users involved in the participatory process
until reaching the critical mass necessary for the virtuous open-source process
to be established (Ratti, 2014)
Finally,
the author believes that the proposed framework can establish among the
subjects involved a type of communicative action , where “participants are not
orientated primarily to their own success but to the realisation of an
agreement which is the condition under which all participants in the
interaction may pursue their own plans” (Habermas, 1982) through cooperation.
1.2. Similarities
with already proposed frameworks
Thanks
to several enabling technologies such as Building Information Modeling (BIM),
visual programming, machine learning, concurrent engineering software and more,
we can fully realize different framework traced for several years. In
particular, it is possible to create a generative design process that responds
and reacts to various stimuli (Alexander, Schmidt, Moore Alexander, Hanson,
& Mehaffy, 2005). We can design not deterministic output but open schemes
in which the designer became and an intermediary (Negroponte, 1975). Finally,
the designer can be involved in the process of creating software and procedures
capable of triggering new design schemes, as showed in The Generator of
Cedric Price (The Musem of Modern Art, 2002). Introducing an additional level
of participation, allowing actions that modify not only the output but also the
instruction to achieve it, we can make the proposed framework capable of
exploring, still on a performance basis, a significantly greater design space:
this possibility of changing the designed procedure, following the principles
of a well-known article (Pask, 1969), would bring the unfolding of the design
process closer the functioning of the human thought. However, the
implementation of this last point is particularly difficult outside of the
academic environment as there are some cultural gaps: end users typically lack
in notions of algorithm-thinking necessary to propose viable changes to
the instruction set. In other cases, however, it has been recorded that an end
user can make changes in a design canvas already validated (Nuijsink, 2008) by
working directly in Alexander’s actual world.
1.3. Tools for
implementation
To work
with the proposed framework, we must set up an environment composed of a
user-friendly visual editor capable of modifying the states of parameters and a
real-time 3D visualization of the proposed output. The entire generative
instructions could be given to the end user if the possibility of activating a
modification process on it exist. The effectiveness of the proposed framework depends
on the correct choice of the parameters given to the end user. They must be in
a limited number but capable of significantly modifying the final outputs.
Design parameters that have strong impacts on social aspects such as the
average size of an apartment or the characteristics of its functional layout
could be given to the end user.
However,
considering some limitations on the software currently available to designers,
some parametric procedure could not be repeatable on an open-source platform.
This is the case, for example, for procedures based on physical form-finding,
which could require the use of codes and software under copyright. Implementations
with these form-finding may require an additional interdisciplinary effort to
overcome intellectual property problems.
Software
suites able to manage the proposed framework are partially existing and there
can be a transfer of knowledge from the product configurators, widely
used in industrial design. A typical suite used in the parametric approach
consists of the software Rhinoceros, Grasshopper and related plugins. We can
implement the technology propsed by Shapediver on a cloud basis to move the
computational effort from the end user to a server and make possible to explore
the design space on multiple types of device (Shape Diver, 2020). Being able to
receive proposals not only from computers but also from smartphone and tablets
would significantly enlarge the people reachable by the proposed framework.
Figure 3 A product configurator by MAK
STUDIO, implemented on the ShapeDiver Tecnology. On the left we can see the
visual editor and on the right the real time 3d visulization. Available on
https://mystuff.makecoolstuff.us
1.4. Tools for
analyzing the received outputs
It is
necessary to transform the collected data into design indications, both
qualitative and quantitative. The parametric approach can be used with
different levels optimization: for the proposed framework, it is necessary to
use tools which can identify performing solution while leaving a consistent
autonomy to the designer. The chosen tools depends on how the design space is
explored: the “N-Times” step in Figure 1 could be done with an iterative process or
could be automatized with the use of EMOA. In this paper, the implementation of
“heatmap” and “cluster data analysis” for obtaining useful design indications
will be briefly described.
Heatmaps
are one of the most popular methods for visualizing complex data structure on a
matrix basis (Wilkinson & Friendly, 2008) . Data that can be extracted from
heatmaps and may be useful for the proposed framework concerns how an end user
explores the design space. We can collect and visualize the times in which they
linger in the modification of a parameter. This information can be used to
hierarchize the parameters on which the participation process is based, to
better direct the design effort towards what, consciously or unconsciously,
causes changes in the end user’s perceived satisfaction. These tools are today
widely used in the design of User Interface (UI) because of their economic
implemention and agile consultation. (Hergul, 2020)
Figure 4 (a) Use of heatmap in UI,
source: wikipedia.com
Figure 4 (b) Qualitative frequency of parameter
modification
Cluster
analysis, or clustering, is a useful tool for identifying, within a large
amount of data, homogeneous groups that share similar characteristics. In the
architectural field, they have been used to optimize the production and
construction of building elements. This is the case of the executive design of
the Soumaya Museum’s façade, Figure 5, in which clustering was used to
divide the surface into families of homogeneous panels to reduce its cost
(Ghery Technologies, 2013). The author identifies that this tools, with
reference to the k-means algorithms (Mitra, 2019), can be used in both
qualitative and quantitative terms in the proposed framework. The cluster data
analysis done with k-means algorithm is used for obtaining quantitative
indications (Garbade, 2018) because fitness function that reward project
outputs closer to the proposal of homogeneous groups of end users can be
implemented.
Figure 5 – Museo
Soumaya – Source: Diego Delso, delso.photo
4. Conclusions
The
proposed framework can be pursued, as has been shown into the implementation
and analyzing paragraphs, with more rigorous or more qualitative approaches.
The transformation of external inputs and proposal into action and design
strategies shows how a framework can have an important role in the quality of
architecture (Purini, 2018). The author believes that the proposed framework
does not influence the formal themes of architecture, as they can be
predetermined by working consciously on the explorable design space in the meta-programming
phase. Rather, these external inputs push to work on a performative level,
transforming an end user’s proposal into a requirements to be satisfied without
distorting the system and design principles already identified by the designer.
It is
necessary to recognize that including participatory aspects in the architectural
design requires, to some extent, a greater effort than with the authoritarian
model. Additional skills are required to the designer because it is necessary
to be more receptive and agile throughout the whole participatory process that
must not be reduced to transcribing what an end user desires, but it is a
dialectical moment between a multiplicity of subjects. (De Carlo & Bunčuga,
2000)
Finally,
it is necessary to recognize that, thanks to the development in the field of
data analysis, ethical risk may arise in the use of the proposed framework
because the shown participatory tools can be used for a false democratic
justification of already taken design decisions. The author acknowledges that
these are risks common to most participatory process.
References
Alexander C. Notes on the synthesis
of form. Harvard: Harvard University Press; 1972.
Alexander C, Schmidt R, Moore
Alexander M, Hanson B, Mehaffy M.. Generative Codes. The path to building
welcoming, beautiful, sustainable neighbourhoods; 2005. Retrieved April 4,
2020, from https://www.livingneighborhoods.org/library/generativecodesv10.pdf
Alexander C, Silverstein M, Angel S,
Ishikawa S, Abrams D. The Oregon Experiment. New York: Oxford University
Press; 1975.
De Carlo G, Bunčuga F. Conversazioni
su architettura e libertà [trad. Conversations on architecture and freedom. Milano: Elèuthera; 2000.
Garbad MJ. Understanding K-means
Clustering in Machine Learning; 2018, September. Retrieved April 4, 2020,
from Towards Data Science: https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1
Ghery Technologies. Museo Soumaya.
Facade design to fabrication; 2013.
Giffinger R, Fertner C, Kramar H,
Kalasek R, Milanovic NP, Meijers E. Smart cities. Ranking of European
medium-sized cities. Vienna: Centre of Regional Science; 2007.
Habermas J. A reply to my critics. In:
Thompson JB, Held D, editors. Habermas: critical debates. London:
Macmillan press LTD; 1982.
Hergul S. Understanding Simple Heat
Maps for Smarter UI Design; 2020. Retrieved April 28, 2020, from uxpin.com:
https://www.uxpin.com/studio/blog/understanding-simple-heat-maps-smarter-ui-design/
McLuhan M. Understanding Media: the
extensions of man. New York: McGraw-Hill; 1964
Mitra M. K-Means clustering in machine
learning - a review. The Peer Research Nest 2019;1(4):1-14.
Negroponte N. Soft Architecture
Machines. Cambridge: MIT Press; 1975.
Nuijsink C. "Less money, More
creativity", interview with Alejandro Aravena. Mark Magazine 2008;15:175-183.
Pask G. The architectural relevance of
cybernetics. Architectural Design 1966:494-496.
Purini F.
(2018). Il BIM. Un parere in evoluzione. [trad. The
BIM. An evolving opinion.]. Op.cit. Selezione della critica d'arte
contemporanea 2018;162;5-16.
Ratti C. Architettura open source.
Torino: Giulio Einaudi Editore; 2014.
Ratti C, Antonelli P, Bly A, Dietrich
L, Grima J, Hill D, Habraken J, Haw A, Maeda J, Negroponte N, Obrist HU, Reas
C, Santambrogio M, Shepard M, Somajni C, Sterlin B. Open Source Architecture
(OsArc). Domus 2011;948:I-IV.
Sacchi, L. La fine del disegno? [trad. The end of Drawing?]. Op. cit. Selezione della critica d'arte contemporanea 2015:153;5-16.
Shape Diver. Shape Diver | Online
Parametric Technology Overview. Retrieved April 4, 2020, from https://www.shapediver.com/overview/
Tedeschi A. AAD_Algorithms-Aided
Design. Brienza: Le Penseur; 2014.
The Musem of Modern Art. Epilogue -
Cedric Price. In Riley T, Deyong S, De Michelis M, Apraxine P, Antonelli P, di
Carlo T, Cline B, The changing of the avant-garde: visionary architectural
drawings from the Howard Gilman Collection. New York: Distributed Art
Publishers; 2002. p.156
United Nations General Assembly. Transforming
our world: the 2030 Agenda for Sustainable Development; 2015.
Whitehead H. (2010). Foreword. In
Woodbury R. Elements of parametric design. New York: Routledge; 2010.
p.1
Wilkinson L, Friendly M. The History
of Cluster Heat Map. The American Statistician 2009;63-2;179-184