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General optimisation - Scia Engineer Optimiser

to get closer to the desired solution.

is found.

4. Evaluation of the optimum solution

one optimum is found by the optimisation solver (these optimums represent local extremes of the goal function - it depends on the mathematical representation of the goal function if it has one or more local extremes). These individual optimums can vary just a little in the value of the goal

in the values of the parameters for individual optimums, which means that the structure takes different shapes, forms, etc. In such a situation, it is the user who must decide which of the variants will be used in the end.

can vary during the optimisation. The parameter indicates that a particular property becomes

if required, also the limits.

If suitable or needed, it is possible to specify also relations between individual parameters (e.g. the relation between the width and height of a cross-section).

of the optimisation method

It can be a price, weight, dimensions, position of a support, location of a load. Furthermore, it is necessary to select one of the available optimisation methods. The selection of the method may affect the time needed for the solution of the sought-after result.

3. Optimisation cycle

The optimisation solver (EOT) generates the sets of parameters used for the creation of a particular variant of the model.Scia Engineer receives these parameters, runs the prescribed calculation, code-check and, if

In the next step, EOT gets back the results and evaluates them to modify the parameters in order

Scia Engineer Optimiser is a cutting edge software tool for the overall optimisation of civil engineering structures. It represents a combination of a widespread structural analysis software (Scia Engineer) and a sophisticated optimisation engine (EOT). The two programs have been integrated together and offer a versatile and complete optimisation solution for

package for analysis, design and checks of civil engineering structures. The integration of Scia Engineer into the process of the overall optimisation is enabled by its above-standard

(numerical) values of individual properties of entities in Scia Engineer can be replaced by parameters. The parameters can be viewed and edited directly in Scia Engineer or via

design for a particular structural entity - e.g.

optimal reinforcement in a concrete cross-section on the base of calculated internal

determins relations between the parameters and selects the suitable optimisation method.

minimum possible number of steps.

The optimisation process can be clearly seen in the picture. Once all the required input data are entered, i.e. the model of the analysed structure

runs fully automatically and no interaction from the user is required. For real-life problems several optimum solutions can be found. In such

decision.

1. Creation of the model and its parameterization

The model of the analysed structure is created using standard Scia Engineer tools and functions. The geometry, boundary conditions, loads, etc.

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General optimisation - Scia Engineer Optimiser

5. Final check

calculation and checks that were not performed during the optimisation can be executed now.

EOT Optimisation methodsSeveral different methods have been

Gradient method: Sequential quadratic programming (SQP)

methods in case of continuous optimisation problems. They are felicitous for example when searching for the optimal positions of nodes, supports, or geometry of cross-sections etc. They cannot be used for optimisation tasks working with discrete values, as a

methods could be very fast, on the other hand convergence problems may occur in projects with a large number of parameters and in tasks

annealing (MSA), Differential evolution (DE) Simply said, stochastic methods search

for the result by means of “trial-and-error” and evaluation of these “trials”. This group contains methods that are also called genetic algorithms. Stochastic methods are the most stable, on the other hand, the necessary calculation time is much higher with respect to

Heuristic methods: Nelder-Mead (N-M) Heuristic methods share the properties of both

gradient and stochastic methods. Their speed is somewhere between stochastic and gradient methods as well as the stability.