| Summary: | Automated systems for large-span roof truss optimization provide engineers with
the flexibility to consider multiple alternatives during conceptual design. This
investigation extends previous work on multi-objective roof truss optimization to include
the design preferences of a human user. The incorporation of user preferences into the
optimization process required creation of a mechanism to identify and model preferences
as well as discovery of an appropriate location within the algorithm for preference
application.
The first stage of this investigation developed a characteristic feature vector to
describe the physical appearance of an individual truss. The feature vector translates
visual elements of a truss into quantifiable properties transparent to the computer
algorithm. The nine elements in the feature vector were selected from an assortment of
geometrical and behavioral factors and describe truss simplicity, general shape, and
chord shape.
Using individual feature vectors, a truss population may be divided into groups
of similar design. Partitioning the population simplifies the feedback process by allowing users to identify groups that best suit their design preferences. Several
unsupervised clustering mechanisms were evaluated for their ability to generate truss
classifications that matched human judgment and minimized intra-group deviation. A
one-dimensional Kohonen self-organizing map was selected.
The characteristic feature vectors of truss designs within user-selected groups
provided a basis for determining whether or not a user would like a new design. After
analyzing user inputs, prediction algorithm trials sought to reproduce these inputs and
apply them to the prediction of acceptable designs. This investigation developed a
hybrid method combining rough set reduct techniques and a back-propagation neural
network.
This hybrid prediction mechanism was embedded into the operations of an
Implicit Redundant Representation Genetic Algorithm. Locations within the ranking
and selection processes of this algorithm formed the basis of a study to investigate the
effect of user preference on truss optimization.
Final results for this investigation prove that incorporating a user's aesthetic
design preferences into the optimization project generates more design alternatives for
the user to examine; that these alternatives are more in line with a user's conceptual
perception of the project; and that these alternatives remain structurally optimal.
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