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Figure 11. The initial and final (optimal) target point. a)Zou and Chan(2005), used with permission,
b)Habibi
problem mathematically. When we need simul-
taneous solution of a number of equalities and
inequalities one way is to satisfy equalities and
check for inequalities. This is the common practice
in design offices. The design engineers satisfy
equilibrium equalities and check for acceptance
criteria inequalities for a structure. Another way
to solve the set of equalities and inequalities is
to get assisted by mathematics. The design opti-
mization is this second way. Design optimization
helps the designer to design a structure in such
a way that not only satisfies the equilibrium and
acceptance criteria but also reduces the cost. In
this methodology, the designer is allowed to put all
self-interested conditions among the constraints.
Albeit, the constraint limits require experience to
be determined realistically. It is the responsibility
of all Engineering faculties to consider the design
optimization as a highly vital subject and put it
among the obligatory courses. This will help more
understanding about this subject and provides the
ground for more developments in the field.
The OPBSD has a bright future. This method-
ology can be used as a powerful robust tool for
scientist engineers, to revise their suggestions on
acceptance criteria based on correlations that may
be discovered between several aspects of opti-
mally designed structures. For example, a design
engineer may have queries on the specification
of a good design so as to have proper energy dis-
sipation, limited plastic rotations in connections,
controlled relative drift, comfort of occupants, etc.
These questions may be answered by a massive
research on different types of optimally designed
structure including different objectives and con-
straints. This tool may be also used to discover
the controversy of different outcomes for different
design criteria.
Preparing the ground for application of OPBSD
can be followed in several directions:
A. Enhancing the knowledge on the material
nonlinearity models for better predicting
the response of a structure.
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