Artificial Intelligence and Sustainable Design — Papers from the AAAI 2011 Spring Symposium (SS-11-02)
Knowledge Based Integration of Sustainability
Issues in the (Re)Design Process
Irem Erbas, Rudi Stouffs, Sevil Sariyildiz
Delft University of Technology
Postbox 5043 2600 GA Delft, the Netherlands
i.erbas@tudelft.nl, r.m.f.stouffs@tudelft.nl, i.s.sariyildiz@tudelft.nl
implications of each design modification on the qualities,
due to time constraints, to assess the integrated impact of
diverse design modifications, nor to consider the
optimization of the design to these different aspects.
However, in order to understand why one design
performs better than another requires an integral approach
looking closely to the interrelationships among the various
aspects. While it is well known that energy-efficiency
interventions have an impact on indoor climate
performance and vice versa, design research has not yet
holistically considered how individual aspects of redesign
interventions in terms of layout, energy, indoor air quality
and thermal comfort interrelate, with reflectance on the
cost. There is a need both to better understand how
conditions of indoor environmental quality are correlated
with measures to improve the energy use of a building and
to make this knowledge available to the architect in the
design process in an integrated way.
This paper discusses how a framework, employing
advanced modeling techniques, can be drawn with which
we can assess the performance of a design in terms of
various interrelated design performance aspects.
Specifically, adopting a knowledge base to explicate the
design requirements and potential actions will help the
architect to consider and deal with the trade-offs coming
from the complexity of the design and to solve the conflicts
between different dimensions and aspects (high
performance material vs. cost, area vs. comfort, etc.).
Abstract
The research project here described aims to contribute to the issue
of sustainability of buildings by improving the architectural
design process with the development of a decision support tool
for the architect. In particular, the research adopts the
improvement of existing designs, namely encouraging energyefficient redesigns while improving indoor environmental quality
as its strategy to promote sustainability. Redesign strategy is
considered not only to extend the life cycle of a building but also
to contribute to the realization of the overall transition towards an
efficient and clean climate. The starting point for this research is
the question of how to develop an integral framework which
enables the modelling of design knowledge through more energyefficient dwellings with acceptable indoor comfort in the
sustainability context so that it would be possible to deal with
qualitative, quantitative, complex and contradictory information
at the same time and integrate these into design decision-making
processes. This modelling approach is considered to provide a
link to developing a tool or a link to be embedded in an existing
tool. In the development of such an approach, how Artificial
Intelligence (AI) can facilitate an integral understanding of the
aspects is raised as a methodological question in terms of
information processing and knowledge integration in the form of
a design decision support tool. By this way it will be possible to
assess the performance of the end result with respect to design
choices, beforehand.
Introduction
Building design studies consider a rather limited amount of
design aspects since there is no methodology yet available
to deal simultaneously and in an integrated way with the
numerous amounts of relevant aspects. Within the
architectural design process, designers often assess the
qualities of a design based on their personal experience,
knowledge, and intuition in a subjective manner rather than
using scientific methods to approach the problem. When
they do consider the use of computational tools to evaluate
the quality of a design with respect to a particular aspect,
they are generally still unable to assess the impact and
Knowledge Modelling as a Potential for an
Integral Approach to Design
In the redesign of dwellings towards more energyefficiency, a variety of criteria stand out, among others,
technical, design, occupant health and economical. These
criteria often interrelate or even conflict, leading to a
complex design information space. For instance, improving
the building envelope’s thermal condition with thick
insulation layers and sealing the cracks to protect against
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unintended ventilation heat loss will have negative effects
on natural air exchange. As another example, improving
natural ventilation for better indoor air quality will
decrease the dependency on mechanical ventilation
systems, however, a problem of heat loss control or
humidity related issues might appear as a result.
In the process of (re)designing, to identify constraints
and possibilities to present improvement options and to
translate them into integral design decisions are vital. As
Lawson (1997) stated,
Considering various actions and their compound effects,
the tool will enable architects to decide on a solution that
satisfies both criteria to a greatest extent, while offering the
ability to weigh satisfaction of both criteria against cost.
When using the tool envisaged, the architect will first
identify a key aspect to start with, either from an energyefficiency point of view, such as mitigating a heat loss
problem, or from an indoor climate point of view, such as
reducing overheating. From the knowledge base, the
architect will then be able to choose a key enabler for this
aspect, e.g., insulation or the air tightness level. To support
the selection of possible actions from the knowledge base,
data will be gathered from the existing building model and,
where necessary, from relevant analysis tools. Multiple key
actions may be selected considering different enablers for
the aspect in question, or addressing consequences of
previous selected actions. The selected actions will then be
applied to the building model, resulting in a redesign
model. The tool will offer an automatic comparison of the
redesign model with respect to the previous model in terms
of how much the proposed actions improve both criteria.
This process can be repeated for all possible key aspects
identified by the architect, or until the desired quality level
is reached (Figure 1).
the activity of design involves a highly organized
mental process capable of manipulating many kinds
of information, blending them all into a coherent set
of ideas and finally generating some realization of
those ideas.
Encompassing different kinds of thought and knowledge
(Lawson, 1997) and accordingly assessing the performance
output beforehand is challenging for the architect. The
purpose of the research project presented here is to
investigate possible methods to bring the scattered and
fragmented knowledge together and represent it in a
meaningful way so that new knowledge on sustainability
related aspects could be elicited from. This kind of
knowledge integration is considered to be achieved via
knowledge modelling.
However, there is no integrated knowledge model that
will combine existing technical information and spatialfunctional aspects related to comfort and cost.
Development of knowledge-based tools would improve
decision making process in the sense that they would
provide a better insight in building’s energy performance
particularly in relation to comfort performance due to
integrated approach. This requires an organization and
encapsulation of energy-efficient redesign aspects in
correlation with comfort or other criteria related aspects.
Specification of the Knowledge Model and Its Use
The knowledge based model abovementioned must assist
the architect to assess energy-efficiency and indoor climate
performance, to select actions for improving the design
with respect to either performance aspect, to gain insight in
both positive and negative consequences of applying such
actions and, generally, in exploring the knowledge model
and gaining knowledge through this process. The
knowledge model is formed from the analysis of existing
bodies of knowledge, such as standards, best practices, and
the related literature. The knowledge model must be
expandable in order to be able to incorporate findings from
new studies and knowledge about new technological
solutions.
This kind of knowledge base will also support the
development of a decision-support tool to assist the
architect in analyzing the current performance of an
existing dwelling, proposing sets of actions to improve the
indoor climate quality level or energy consumption level,
and assessing the consequences of the redesign actions.
Figure 1 – Process Model
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consuming to run and difficult to be used by the architects
due to the detailed input they require. What would interest
architects more is a support facilitating an integral
understanding of the sustainability problem that will be
able to respond to the aspects with a broader and reliable
basis matching human reasoning (Erbas et al., 2010).
Soft Computing As Part of Artificial
Intelligence
Soft computing, introduced by Prof. Zadeh as a part of AI
is a collection of methodologies that aim to exploit the
tolerance for imprecision and uncertainty to achieve
tractability, robustness, and low solution cost (Zadeh,
1994). Referring to Chaturvedi, one of the important
features of soft computing is given as the acquisition of
knowledge/information from inaccurate and uncertain data
and having high tolerance for imprecision in the data on
which it operates (Chaturvedi, 2008).
Soft computing is described an alternative tool to model,
and analyze very complex phenomena including
probabilistic considerations and human reasoning
(Ciftcioglu et al., 2007a).
In this research domain, combination of two soft
computing methods is considered as suitable to be
facilitated; fuzzy logic and neural tree. Fuzzy logic will be
used in reasoning process whereas neural tree will be used
for structuring information – representation of a feedforward hierarchical classification of energy efficiency and
indoor comfort related design aspects effective on overall
design performance value (Ciftcioglu et al., 2007b).
Figure 2 -Relating design aspects to energy and indoor comfort
Figure 3 - Simplified process model
Limitations of Existing Tools
In this research, knowledge is evaluated more as a capacity
for rational action which is the same concept of knowledge
adopted and used in Artificial Intelligence. Referring to
Newell (1981):
Knowledge Model: An Informed Architect
Figure 4 shows what type of data is being used for what
purpose and their connectedness in the overall system. This
helps to identify the structure of the overall process (how
the architect will use the tool, what output the tool will
provide to the architect) and the knowledge base for this
process (where the reasoning comes from). So which
aspect is connected to which and what type of input could
be used for a specific type of output. In the comparison
phase, the assessment model will judge how much the
existing situation is improved.
The decision support tool needs to assist the designer
both in selecting appropriate (re)design actions towards
improving the performance of the building under design
and in assessing the actual performance improvements
resulting from these actions. This paper deals with the first
part which is about the explication of the interrelationships
between (re)design actions based on a knowledge model
being set up by relevant aspects. This type of model will
represent the knowledge within the domain of energy
efficiency and indoor comfort to assist the architect in
creating ‘what-if’ scenarios. The second part is about
providing the background information to assess
performance values and to allow for making comparisons
Knowledge is something to be processed according to
the principle of rationality to yield behavior.
Knowledge provides the action to be taken at certain
situations, and actions can add knowledge to the
existing body of knowledge.
In the design domain, experiences expand the body of
knowledge which is not possible to be handled without an
intelligent support.
In terms of supporting the sustainable design process in
regards to performance assessment it can be commented
that the capabilities of existing tools for architects are
limited in terms of information processing. They have
limited or no intelligence and learning capabilities. In
addition, many of them do not directly handle complex
issues of design evaluation and design decision-making. In
that sense, for complex decision-making, they cannot aid
the designer and the decision maker. For instance analysis
tools (e.g. building performance simulation tools) make
detailed calculations based on certain models for a given
design in order to transfer crisp values however they do not
provide any assessments. In addition, these tools are time
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which is represented via a case study in another publication
(Erbas et al., 2011).
value as a result of chosen action(s), the system will warn
the architect (Figure 5).
As it can be seen in the figures, the steps can be traced in
such a knowledge based system. It can be jumped between
the levels and a selection of one problem on a lower level
can be made. For instance either the problem of heat loss
can be investigated or more specifically the solutions to
reduce transmission heat loss can be looked into.
One problem or a combination of problems can be
selected to overcome. A selection process can be run
parallel to see the effects on other criteria. However,
problem of many combinations remain. It should be
questioned how to handle a lot of combinations. Also when
the architect starts with a predetermined improvement for
instance in terms of energy, if the system could be able to
allow for possibilities to reach at that determined target.
This is can be raised as a question of reverse computation
in such a system. However, it might lead to infinity.
Figure 4 – Components of the knowledge model
Figure 5 – Example of interconnectedness among performance aspects
The uppermost level indicates a target of improved overall
performance with respect to our own criteria. Conceptually
in such a redesign process fulfillment of four significant
requirements could be considered to be brought in better
condition: design, energy, comfort performances and costs.
The focus is on energy and comfort performances in
connection with the estimated overall costs. Improvement
of some design parameters could also be in question either
to come out as a result of energy and comfort performance
improvements or additional owner request. It might be
developed further.
In Figure 6 steps through the decision making is
represented. It starts with the identification of the problem
area(s) for which an action or combination of actions will
be investigated looking at the causes/types. This will send
the message to the relevant aspects of the other
performance criteria. In case another flaw is occurring
within the design and this is reducing the performance
Figure 6 – Making action steps visible
Discussion and Conclusions
Simulation results and building model outputs of the
existing situation will be stored in the system in order to be
compared later. With the data entered to the system it will
be possible to play with the variables, informing the user
about possible consequences.
Then, architect will choose a set of ‘reasonable’ actions
manually and create a new ‘re-design 1’. Then the system
will offer an automatic comparison in terms of how much
the situation is improved. This will be an iterative process
until the ‘goodness’ of the new design is verified.
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However the system can also be designed as automated in
which selection will be made automatically with respect to
the required prioritized effects.
In this way, the several iteration steps will be reduced.
The best possible solutions will be offered according to the
criteria from a bunch of solutions (genetic search process).
This requires more expert knowledge on genetic algorithm.
The decision-support tool to be developed will provide the
designer with alternatives to choose a right strategy for
energy-efficient transformations in dwellings from. This
will contribute to decreasing the demand for nonrenewable energy sources such as fossil fuels, reducing
greenhouse effect and saving money. In addition, from a
larger scale perspective, energy-efficient transformations
may provide less intervention to change the energy
infrastructure of housing districts.
It is aimed to enable the architects to understand the
influence of redesign interventions on energy and indoor
climate performance. The model is considered to give
meaning to the results by suggesting quantifiable
evaluation of performance criteria.
Integrating the knowledge model into real-time
environment is under consideration. What would be the
most convenient environment to achieve this is also a
question. Merely the knowledge model will remain too
abstract for designers to use and merely 3D representations
will not reflect upon the performance comparisons with
respect to the interventions applied.
References
Chaturvedi, D.K. 2008. Soft Computing : Techniques and
its applications in electrical engineering. Chapter 1:
Introduction to Soft Computing. Berlin : Springer.
Ciftcioglu, O., Bittermann, M. S., and Sariyildiz, I.S.
2007a. Fuzzy Neural Tree for Knowledge Driven Design.
ICICIC, Kumamoto, Japan.
Ciftcioglu, O., Bittermann, M.S., and Sariyildiz, I.S.
2007b. A Neural Fuzzy System for Soft Computing.
NAFIPS, IEEE, San Diego, USA.
Lawson, B. 1997. How designers think: The design process
mystified. Oxford : Architectural Press.
Newell, A. 1981. The Knowledge Level. AI Magazine.
Zadeh, L.A. 1994. Fuzzy Logic, Neural Networks, and Soft
Computing. Communications of the ACM, Vol. 37 No. 3.
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