While the weight of a steel structure is a notable
component of the total cost, the minimization of the
cost should be the final objective for optimum use of
available resources. The total cost of a steel structure
together with (a) the material value of structural
members such as beams, columns, and bracings, (b)
the fabrication cost including the material costs of
connection component, bolts, and electrodes and the
labor cost, (c) the cost of transporting the fabricated
pieces to the construction field, and (d) the erection
cost including the material costs of connection
elements, bolts, and electrodes and the labor cost. In
this article, a sequential motivate of the journal objects
on cost optimization of steel structures is presented.
Articles on deterministic, reliability-based, and fuzzy
logic-based optimization of steel structures are
evaluate. Research on cost optimization can encourage
the use of the optimization detain in structural steel
design practice by providing a more realistic way of
modeling structural steel design and proceed in
additional savings compared with the weight
optimization problem.
Steel constructions are widely used in various
applications such as structures for buildings, stores,
factories, and power plants. The scope of the research
is to study a methodology to minimise the weight and
the cost related to big frame steel structures during
the early design phase, which is the phase where most
of the project layout is defined. The main aim of this
paper is the development of a platform-tool to support
the robotic optimization of a steel structure using
virtual prototyping tools and genetic algorithms. The
clear-cut is on the design of heavy steel structures for
oil & gas power plants. This work describes in detail
the design methodology and estimates the weight
saving related to the re-design process of a test case
structure. The design cases considered in the paper
are those applicable to the operation. Keywords
technologies is Tekla authorized training centre in
Kerala
In real world engineering applications the uncertain
of the structural parameters are inherent and the
scatter from their nominal ideal values is in most
cases unavoidable. These uncertainties play a
important role in structural performance and the only
way to assess this influence is to perform Reliability-
Based Design Optimization (RBDO) and Robust
Design Optimization (RDO). Compared to the basic
deterministic-based optimization problem, a RBDO
problem restriction additional non-deterministic
constraint functions, while the RDO yields a design
with a state of robustness, so that its performance is
the least sensitive to the variability of the uncertain
parameters. Keywords technologies now providing
online tekla training in kerala. The first part of this
study examines the application of Neural Networks
(NN) to the RBDO of large-scale structural systems,
while the second part investigates the structural RDO
problem. The use of NN in the framework of the
RBDO problem is influence by the approximate
concepts inherent in reliability analysis and the time-
consuming repeated analyses required by Monte Carlo
Simulation. On the other hand the RDO is a multi-
criteria optimization problem where the aim is to
minimize both the weight of the structure and the
dinstinction of the structural response.
