Performance Assessment Strategies

Using engineering performance evaluations to explore design alternatives
during the conceptual phase of architectural design helps to understand the
relationships between form and performance; and is crucial for developing
well-performing final designs. Computer aided conceptual design has the
potential to aid the design team in discovering and highlighting these
relationships; especially by means of procedural and parametric geometry to
support the generation of geometric design, and building performance
simulation tools to support performance assessments. However, current tools
and methods for computer aided conceptual design in architecture do not
explicitly reveal nor allow for backtracking the relationships between
performance and geometry of the design. They currently support post-
engineering, rather than the early design decisions and the design exploration
process.

Focusing on large roofs, this research aims at developing a computational
design approach to support designers in performance driven explorations. The
approach is meant to facilitate the multidisciplinary integration and the
learning process of the designer; and not to constrain the process in
precompiled procedures or in hard engineering formulations, nor to automatize
it by delegating the design creativity to computational procedures. PAS
(Performance Assessment Strategies) as a method is the main output of the
research. It consists of a framework including guidelines and an extensible
library of procedures for parametric modelling. It is structured on three
parts.

Pre-PAS provides guidelines for a design strategy-definition, toward the
parameterization process. Model-PAS provides guidelines, procedures and
scripts for building the parametric models. Explore-PAS supports the
solutions-assessment based on numeric evaluations and performance simulations,
until the identification of a suitable design solution. PAS has been developed
based on action research. Several case studies have focused on each step of
PAS and on their interrelationships.

The relations between the knowledge available in pre-PAS and the challenges of
the solution space exploration in explore-PAS have been highlighted. In order
to facilitate the explore-PAS phase in case of large solution spaces, the
support of genetic algorithms has been investigated and the exiting method
ParaGen has been further implemented. Final case studies have focused on the
potentials of ParaGen to identify well performing solutions; to extract
knowledge during explore-PAS; and to allow interventions of the designer as an
alternative to generations driven solely by coded criteria.

Both the use of PAS and its recommended future developments are addressed in
the thesis.