# An Introduction to Structural Optimization

## An Introduction to Structural Optimization

This book has grown out of lectures and courses given at Linköping University,
Sweden, over a period of 15 years. It gives an introductory treatment of problems
and methods of structural optimization. The three basic classes of geometrical optimization
problems of mechanical structures, i.e., size, shape and topology optimization,
are treated. The focus is on concrete numerical solution methods for discrete
and (finite element) discretized linear elastic structures. The style is explicit
and practical: mathematical proofs are provided when arguments can be kept elementary
but are otherwise only cited, while implementation details are frequently
provided. Moreover, since the text has an emphasis on geometrical design problems,
where the design is represented by continuously varying—frequently very many—
variables, so-called first order methods are central to the treatment. These methods
are based on sensitivity analysis, i.e., on establishing first order derivatives for objectives
and constraints. The classical first order methods that we emphasize are
CONLIN and MMA, which are based on explicit, convex and separable approximations.
It should be remarked that the classical and frequently used so-called optimality
criteria method is also of this kind. It may also be noted in this context that
zero order methods such as response surface methods, surrogate models, neural networks,
genetic algorithms, etc., essentially apply to different types of problems than
the ones treated here and should be presented elsewhere. The numerical solutions
that are presented are all obtained using in-house programs, some of which can be
These programs should also be used for solving some of the more extensive exercises
provided.
The text is written for students with a background in solid and structural mechanics
with a basic knowledge of the finite element method, although in our experience
such knowledge could be replaced by a certain mathematical maturity. Previous
exposure to basic optimization theory and convex programming is helpful but not
strictly necessary.
The first three chapters of the book represent an introductory and preparatory
part. In Chap. 1 we introduce the basic idea of mathematical design optimization
and indicate its place in the broader frame of product realization, as well as define
basic concepts and terminology. Chapter 2 is devoted to a series of small-scale problems
that, on the one hand, give familiarity with the type of problems encountered
in structural optimization and, on the other hand, are used as model problems in
upcoming chapters. Chapter 3 reviews basic concepts of convex analysis, and exemplifies
these by means of concepts from structural mechanics. Chapter 4 is, from an
algorithmic point of view, the core chapter of the book. It introduces the basic idea of
sequential explicit convex approximations, and CONLIN and MMA are presented.
In Chap. 5 the latter is applied to stiffness optimization of a truss.

A structure in mechanics is defined by J.E. Gordon  as “any assemblage of materials
which is intended to sustain loads.” Optimization means making things the
best. Thus, structural optimization is the subject of making an assemblage of materials
sustain loads in the best way. To fix ideas, think of a situation where a load is
to be transmitted from a region in space to a fixed support as in Fig. 1.1.We want to
find the structure that performs this task in the best possible way. However, to make
any sense out of that objective we need to specify the term “best.” The first such
specification that comes to mind may be to make the structure as light as possible,
i.e., to minimize weight. Another idea of “best” could be to make the structure as
stiff as possible, and yet another one could be to make it as insensitive to buckling or
instability as possible. Clearly such maximizations or minimizations cannot be performed
without any constraints. For instance, if there is no limitation on the amount
of material that can be used, the structure can be made stiff without limit and we
have an optimization problem without a well defined solution. Quantities that are
usually constrained in structural optimization problems are stresses, displacements
and/or the geometry. Note that most quantities that one can think of as constraints
could also be used as measures of “best,” i.e., as objective functions. Thus, one can
put down a number of measures on structural performance—weight, stiffness, critical
load, stress, displacement and geometry—and a structural optimization problem
is formulated by picking one of these as an objective function that should be maximized
or minimized and using some of the other measures as constraints. In Sect. 1.3
we will be specific about how such a formulation looks in mathematical terms. In
the next section, Sect. 1.2, we will temporarily move the perspective in the other
direction, and look at how structural optimization enters a broader picture.