Content uploaded by Suleiman Alhadidi
Author content
All content in this area was uploaded by Suleiman Alhadidi on Nov 24, 2016
Content may be subject to copyright.
Performative Architectural Practice
Building Collaboration Platforms for Multidisciplinary Architecture Business
Suleiman Alhadidi (Author)
BVN & University of New South Wales
Sydney, Australia
Heather Mitcheltree (Author)
University of Melbourne
Melbourne, Australia
Abstract— The last decade in architectural practice has
witnessed a significant shift in our understanding and use of
digital tools. The most significant recent development in the field
of architecture is the realization that software processes aren’t
simply tools designed by software developers - they can become
the very material from which designs are made. New design
disciplines like interaction design, robotics, smart facades, data-
driven design and artificial intelligence are evolving design
practice to a module that is more agile, collaborative and project-
idea centred. Fuelled by emergent technology and the application
of computational processes to design problems, emergent design
practices have reintroduced performance as a creative driver.
These new generation practices are developing business modules
that prioritise intelligent 'building matrixes' as an avenue
through which to achieve design solutions that are based on
scientific research.
This paper discusses the changing landscape of architectural
practice, the introduction of new design disciplines into
architectural practice, and the emergence of multidisciplinary
architecture business models that are based on design
performance. Through a series of case study examples, this paper
examines recent advancements in performance-based
computation, and how the use of Computational Design and
emergent technologies are enabling, and driving, innovations in
architectural practice.
Keywords—Innovation; Architectural Practice Futures; Data-
driven Design; Computational Design
I. INTRODUCTION
A. Design in 21st Century : From Idea Provider to Solution
Provider
Architecture is currently experiencing a paradigm shift in
the process and conceptualisation of design and professional
practice. Fuelled by the introduction of technology into the
design process, the architectural profession is seeing the
emergence of innovative models of interdisciplinary and
collaborative practice that merge generative design and
emergent computational processes with experimentation and
research (Long, 2016; Moser, 2013). The shift towards
parametric data-driven modelling processes and the
involvement of smart technology in design processes don’t
deny the role of the architect, nor devalue traditional praxis.
Rather, it offers new modes of multi-disciplinary and
collaborative practice centred on performance-based
computational frameworks that allow for rapid feedback and
testing of design iterations (Chaszar & Joyce, 2016).
Emergent technology such as artificial intelligence,
generative design processes, smart technology, data-driven
design, augmented reality, and sensing technology are rapidly
developing and changing the business models, the types of new
start-ups, and product and service development across a range
of industries. Within architecture, these rapidly shifting
technological innovations are having a profound effect on the
architectural industry - modifying architectural service
provision typologies, modes, practices and products. Within
this morphing practice landscape, we see a shift away from the
conceptualisation of design-as-idea provision to one of design-
as-solution provision.
This hybrid practice model places an emphasis on
multidisciplinary collaborative experimentation and research
that moves beyond basic solution driven outcomes and pushes
the boundaries of traditional design confines. The introduction
of computational design processes and the availability of big
data, have enabled transformations in design processes and
outcomes. The ability to utilize live and/ or static data to
inform the design process, in conjunction with the capacity for
iterative and rapid computational design processing, have
enabled data-driven design processes that are premised upon
the concept of design-as-solution. This has resulted in the
ability for practices to move beyond an approach that focuses
on a limited design-as-ideas approach, to one in which we see
the provision of design-as-solution through the transformation
a data driven ideas process and data-informed computational
design.
In his book, Architecture 3.0: The Disruptive Design
Practice Handbook, Cliff Moser outlines an emerging trend
within the architectural profession in which architectural
practice is moving towards a model centred around solution
provision rather than building design. According to Moser,
this paradigm shift was instigated by the global financial crisis
of 2008 and the subsequent downturn within the construction
industry (Moser, 2013). The sudden financial downturn left in
its wake a volatile and highly competitive market, which, in
conjunction with a growing perception of architecture as an
industry that had “overspecialized, under-delivered, and […]
served no purpose” (Moser, 2013, p.1), forced practices to re-
examine the purposes, modes and models of architectural
praxis. Tethered by the “vestigial legacy” (Moser, 2013, p.2)
of traditional professional roles, contractual relationships, and
modes of production, architects had to radically re-think
practice typology.
Centred around a series of case studies, this paper looks at
the shifting terrain of architectural practice, and the impact of
technological innovation on the architectural profession.
Through an examination of emergent modes of practice, this
paper examines how technology based workflows can be
implemented within practice to facilitate and promote building
performance as a driver for change.
B. Design with Data: Information/Data Driven Design
Developments such as the Internet of Things (IoT), a
network of devices that collect and exchange data, are
providing new opportunities for the architecture, engineering
and construction industries. Whilst within contemporary
practice designers have been using data to inform the design
process, for the most part, this is structured as a value-add
process in which in-built assumptions and parameters define a
data-enabled/enriched design outcome (Deutsch, 2015). A
common example of such processes is the use of weather data
to assist in compliance with solar access requirements for
residential design. Within the state of NSW in Australia,
section 4A of the State Environmental Planning Policy No65
(SEPP65, 2015) sets out design guidelines, objectives and
criteria for solar and daylight access within residential
apartment developments. Utilising local weather data,
designers are able use data-enabled parametric design
processes to set solar access values and parameters to modify
building orientation, building fenestration and design solar
shading devices so as to comply with SEPP 65 guidelines.
Whilst such processes enable designers to run a large number
of models and iterative design variables that would
traditionally not have been feasible due to their complexity and
labour intensive requirements, outcomes are limited by in-built
assumptions and parameters that are pre-set at the outset, or
heavily value based.
Fig. 1. Sepp 65 residential development script which account for code
requirements - developed at BVN Architecture in Sydney
Data-enabled design processes such as the one described
above, typically utilize existing and known data, and do not
allow for a truly responsive design process. Data is subordinate
within this design process, creating a data-enabled (notional)
rather than data-driven (responsive) systems process. This
restricted application of computational design processes is one
that is highlighted by Deutsch as a current limitation within the
architectural industry (Deutsch, 2015). Deutsch claims that
architectural practices could learn from the gaming industry as
well as pioneering IT companies (such as Apple and Google)
that have created data rich and responsive design processes.
One example of the way in which emergent gaming
developments have been adopted for design applications is in
the development by AECOM of the Sustainable Systems
Integration Model (SSIM). AECOM’s system, adapts a data-
driven decision making capacity to a modelling system that
creates a data-rich and immersive environment with which to
run comparative testing simulations for urban planning
(AECOM; ElectronicArtsInc., 2014). SSIM allows designers,
clients and the public to generate multiple development
alternatives and understand the environmental and financial
consequences when parameters are varied. Advancements in
gaming technology have enabled a rich complexity of urban
planning decision feedback processes and predictions. SimCity
for example, is able to deliver an unprecedented depth of
simulation by allowing for multi-dimension design decisions -
where by decisions and design changes that you make on a city
that you have designed within the game, impact on both the
individual player city and the region. This “butterfly effect”
which was embedded in the game script, has the potential to
enable players and architects, if the same strategy is
implemented in architecture CAD programs, to explore and test
the implications of design decisions through modelling and
simulations within data-driven habitats.
Fig. 2. Types of data versus design methodology. (Adapted from Deutsch,
2015)
Big Data is providing a new paradigm for design. In his
book, The Architecture of information: Architecture,
interaction design and the patterning of digital information,
Dada-Robertson claims that the spatial patterns which we make
in our environment are a primary means of human
communication” (2011, p. 144). According to Dada-Robertson,
“information only becomes relevant once it is linked to action”,
and it is only through the “shaping of our experience of space
and influencing our behaviours, [that] computational
information acts as an intermediary between place and action
and software becomes as ‘architectural’ as its more traditional
‘hardware’ counterparts” (p.144).
In simple terms, IoT is modifying the way we think about
design, and contributing to a scenario in which design is driven
by a process of information production, capture and synthesis
that is embedded in, and feeds back into, the design process.
Within this scenario, architects are designing for the behaviour
of their designs, which are in turn influenced by the data
captured and analysed within the design process. The
emergence of such processes provides the ability for designers
to create smart buildings (or building parts) that are able to
adapt to serve real-time performance needs. One example of
this is the development of smart cities initiatives that are based
on the integration of information and communication
technology, real-time sensors, data collection and monitoring.
These initiatives provide the ability to develop data-driven
design and management solutions across a range of scales,
from discrete site based solutions to large urban data-driven
solutions. Technology based providers such as IBM and Cisco
have taken a leading role in driving the business of smart cities
through the design, development and delivery of smart-
technological innovations. However, within the technology
implemented workflow which these companies currently
provide, design does not necessarily serve as the primary driver
for data-centric solutions and service provision.
The aforementioned processes challenge the current ways
in which we approach Building Information Modelling (BIM).
Through the inclusion of integrated data-rich computational
systems within design processes, such systems function not
only as a tool for documentation or design, but also as a
platform with which to produce data-responsive and dynamic
designs. Within these models, the dynamic feedback system
serves as a design driver (such as performative design, kinetic
design, interactive design and smart design), and creates a
platform for real-time inputs, analysis and iterative design
testing. Through the design and real-time building feedback
process, such systems provide the potential to translate models
such as Facility Management (FM) Models into a live system
similar to SimCity; enabling the end user to access and modify
design and performance parameters based on feedback data.
This information, if connected with Rich Data that is
synthesized from Big Data processors in real-time, provides the
potential to turn the building into a smart object that is able to
inform the user of the current building behaviour and predict
future building behaviour.
II. REDEFINING INNOVATION IN THE DESIGN INDUSTRY
A. Current architectural practice: the design technology
dilemma
Within many architectural practices, the position of BIM
manager is structured as a technical role, the function of which
is to provide support to the design team. Typically, within large
scale architectural practices, technology teams are separate
from design teams. This separation, or siloing of knowledge
and certain aspects of the practice base, has led to an industry
wide situation in which design teams may not be fully
equipped to perform certain design tasks. The rapidly
expanding introduction of new technology into architectural
practice is resulting in an ever expanding knowledge gap
within the industry - creating a schism between those who
function in a technology-centric role and those who operate in
a design-centric role within practice. However, an awareness of
the ramifications of this schism, and its impact on practice
models and the efficacy of design teams is only just starting to
emerge. Few architectural practices to date are working to
address this schism and develop a design technology team who
is able to implement design software-tools and drive innovation
in architecture practices.
B. Technology to drive innovation: Computational design
introduction to the industry
Continuing technological advances and developments in
Computational Design have opened up a range of new design
processes and opportunities for architects, allowing designers
to develop software tools tailored for specific design processes
and applications. When based on real data, the development of
these tools enables data-driven design processes and opens up
new avenues for the production of creative outcomes (Davis,
2015). Within this shifting paradigm, architectural practice
shifts away from business models focused around a design
idea-centric service provision to one that is a design solution-
centric service provision model. Within Australia, one
example of where this is happening is at BVN Architecture,
where computation is being used as a driver for innovation in
design within the practice. BVN’s strong focus on building
performance metrics, has enabled them to implement data-rich
computational design processes, and test and develop designs
based on environmental, social, and experiential metrics.
Fig. 3. Viewshed Analysis – Syndey City Wide View Analysis using
Automated List Processing in Grasshopper, Rhino 3D
Within architectural practice, computational design also has
the ability to be used as a learning platform, facilitating
knowledge acquisition about the impact of aspects such as
environmental, statutory, and social requirements through a
modelling-test-feedback loop script process. This process
allows users to see and test the impact of parameter changes on
the design, and develop a better understanding of the complex
interplay of factors that impact on design processes,
performance and outcomes. For example, a script developed
for a commercial tower complex, has the potential to facilitate
continuing professional learning on issues such as requirements
for Premium or Class-A office buildings through the feedback
it provides when parameters are changed.
Computational design is facilitating multilevel innovation
within architecture practice, fuelling the development and
implementation of new technologies and enabling the
application and expansion of R&D within architecture practice.
Whilst the research and development capacity of computational
design systems has, to date, had limited uptake within sections
of the architectural industry, practices and research institutions
such as those of Carlo Ratti and the MIT Media lab are
embracing the R&D potentials of computational design.
Through the incorporation of sensing technology within the
design process, Carlo Ratti has created a hybrid computational
design-theory-research practice approach that embraces the
potential of technology to change the way we understand,
design and ultimately live in cities (Ratti & Claudel, 2015). At
the MIT Media lab, the focus is on technology as a driver for
multidisciplinary collaboration and innovation. The model
employed at MIT utilises a design through technology process
coupled with the convergence of multidisciplinary teams in
technology, multimedia, sciences, art and design, to promote
innovative design research and development.
III. BUSINESS APPROACH FOR THE NEW DESIGN ERA
In 1965, the cofounder of Intel and Fairchild
Semiconductor, wrote a paper which outlined the exponential
increase in technological component complexity over time
(Lopes, Tenreiro Machado, & Galhano, 2016). This
mathematical description of the relationship of technological
progress to time, came to be known as Moore’s Law. With the
strong role of technology within architectural practice and
production modes, this exponential growth in technological and
machine capacity has a profound potential for flow on impacts
on innovation and developments within the architectural
industry – a potential that is at present under-realised.
With the rapid pace of technological developments, the 21st
century economy transforms information into products,
processes, and services that fuel economic growth, and create
employment and wealth at an ever increasing pace (Case,
2016). This has created the situation in which design has
become a hybrid platform where science, programming,
technology and design are emerging as one. To thrive within
this context, practices need to look at ways in which to:
1- Focus on investing in the development and enrichment
of the expertise contained within their teams,
2- Experiment with entrepreneurial business models, and
3- Structure their work place practice models so that they
promote and are inclusive of interdisciplinary skills sets and a
more diverse knowledge base.
IV. PERFORMANCE BASED COMPUTATION THROUGH DESIGN
CASE STUDIES
A. Multidisciplinary Bottom-up Implementation Approaches
in Solution Computing: Redefining Multi Disciplinary
Teams
During the last decade, many of the large scale architecture
practices have started developing their own tools and investing
in R&D to allow for a greater in-house computational design
capacity. This has enabled designers to craft their design tools
to suit their own project and R&D needs (Davis, 2015).
Exploratory projects are utilised as a mechanism by which to
enable designers to implement computer science processes to
solve design challenges. While such processes have succeeded
in enabling faster resolution of discrete problems such as solar
access and form finding, their application to whole-of-design
generation approaches is still limited. An exception to this is
Gehry Technologies, a practice that has merged tool creation
and design generation to create an innovative and evolving
practice model that interlaces interdisciplinary technological
developments with research, design and innovation.
Fig. 4. Traditional architecture business model based on building design
B. New design discipline START UPS : Pilot research
projects
In the current data-driven design era, we are witnessing a
shift in the roles, skill sets and knowledge base of those
involved in design processes. Increasingly, multidisciplinary
teams are required to design and deliver new projects. Business
models based around the creation of integrated solutions
require integrated interdisciplinary teams that may be
comprised of quite diverse members such as data analysts,
programmers, IoT experts, artists and designers. In this
shifting landscape of emerging hybrid practices, architectural
businesses need to re-think the types of roles, and skill set mix
of their staff if they want to remain competitive.
Fig. 5. Business model based on solution based architectural practice
Solution driven design strategies can be developed within
practice as part of the architectural business model if they are
coupled with applied pilot projects that engage
multidisciplinary research teams and serve as a testing ground
for the implementation of innovative design processes. Within
this context, the practice workspace serves as a research lab for
the testing of new ideas, materials, designs and practice modes.
At BVN, an Australian based firm, the research team has
invested in a one year study to investigate the potential to
incorporate robotics into their design process. Within their
research, BVN are examining how robotics can be used to
improve design processes and business workflows through:
1. The utilization of the 6 axis robot arm tool to produce
designs that enable the creation of more flexible morphologies
(such as kinetic ceilings);
2. Product exploration and development into areas such
as streamlined production processes, and novel materials
research and applications.
Fig. 6. Future architecture business model based on the development of
extended business disciplines (in sensing, robotics and performance based
design) within architectural practice
C. Internal and External Collaboration Modules: Creating a
Business Community of Technology Driven Change
The ability for practices to engage in pilot research projects,
start-ups, and interdisciplinary collaborative processes, has
been greatly facilitated by the development of collaborative
platforms. Over the last five years, the ability to design for
solutions has increased due to the effective use of collaboration
platforms such as Slack (real time communication messaging),
Newforma (software that manages project information using
multiple apps, enabling architects to access all project files
from anywhere), Trello (collaborative tracking of project task
lists) and Citrix (a cloud based computing server established to
maximize collaboration).
With advances in technology, there is a strong need for
business to educate industry of the advancement in order to
effect change within business as a whole (Dade-Robertson,
2011). Through the sharing of ideas and the promotion of an
interdisciplinary dialogue about innovation, the architectural
industry can connect with and capitalise from research and
developments that are taking place across a range of different
sectors. One example of a forum facilitating this dialogue and
knowledge exchange is the Sydney Computational Design
Group, a forum that invites international guest speakers to
present on cutting edge technological developments, research
and innovative practices to wide range of audience members
such as builders, engineers, planners, programmers and
architects (SCDG).
Fig. 7. View analysis based on contextual data gathered from Circular Quay,
Sydney by 3XN and BVN
V. DISCUSSION AND FUTURE EXPERIMENTS
With continual technological advances, research and
development is an important part of the practice base for any
firm. Fuelled by emergent technology and the application of
computational processes to design problems, to be competitive,
emergent design practices have to reintroduce performance as a
creative driver and instigator of change. The shift towards data-
driven design is necessary one, if practices are to achieve
responsive workflows and create innovative solutions.
Technological innovations, Big Data availability and the rise of
the IoT, have enabled firms to adopt solution driven creative
design processes. New generation practices are developing
business modules that prioritise intelligent 'building matrixes'
(multi-dimensional rich-data sets that drive design) as an
avenue through which to achieve design solutions that are
based on scientific research and data synthesis. These
multidisciplinary business models are based on design
performance, and enable the development of data-rich solutions
based design processes. The introduction of new design
disciplines into architectural practice is a must in order to
thrive during this ‘smart’ era in which the ubiquity of
technology mandates diverse interdisciplinary design teams in
order to facilitate the translation from speculative R&D to
product development.
REFERENCES
[1] AECOM. Sustainable Systems Integration Model. Retrieved from
http://www.aecom.ca/vgn-ext-
templating/v/index.jsp?vgnextoid=eb0e8c248660c210VgnVCM100000
089e1bacRCRD&vgnextchannel=4720d13d3b681310VgnVCM1000000
89e1bacRCRD&vgnextfmt=default
[2] Case, S. (2016). The third wave: An entrepreneur's vision of the future.
New York, NY: Simon & Schuster.
[3] Chaszar, A. a. c. s. e. s., & Joyce, S. C. (2016). Generating freedom:
Questions of flexibility in digital design and architectural computation.
International Journal of Architectural Computing, 14(2), 167-181.
doi:10.1177/1478077116638945
[4] Dade-Robertson, M. (2011). The Architecture of Information:
Architecture, Interaction Design and the Patterning of Digital
Information Retrieved from
https://ezp.lib.unimelb.edu.au/login?url=https://search.ebscohost.com/lo
gin.aspx?direct=true&db=cat00006a&AN=melb.b4342110&site=eds-
live&scope=site
[5] Davis, D. (2015). The Next Generation of Computational Design.
Architect, (July 31, 2015). Retrieved from
http://www.architectmagazine.com/technology/the-next-generation-of-
computational-design_o
[6] Deutsch, R. (2015). Data-driven design and construction : 25 strategies
for capturing, analyzing and applying building data. Hoboken, New
Jersey: John Wiley & Sons Inc.
[7] Long, S. (2016). ACTIVE PRAXIS* HYBRID PRACTICE.
Landscapes/Paysages, 18(2), 26-29.
[8] Lopes, A. M., Tenreiro Machado, J. A., & Galhano, A. M. (2016).
Empirical Laws and Foreseeing the Future of Technological Progress.
Entropy, 18(6), 1-11. doi:10.3390/e18060217
[9] Moser, C. (2013). Architecture 3.0. [electronic resource] : The
Disruptive Design Practice Handbook: Hoboken : Taylor and Francis,
2013.
[10] Ratti, C., & Claudel, M. (2015). Open-source architecture: London :
Thames & Hudson.
[11] SCDG. Sydney Computational Design Group. Retrieved from
http://www.meetup.com/Sydney-computational-design-group/
[12] SEPP65. (2015). State Environmental Planning Policy No 65 - Design
Quality of residential Apartment Development (SEPP 65). Sydney,
NSW: NSW Department of Planning and Environment.
