Optimization & Costs / CO2 Emission Estimation for RFEM 6 / RSTAB 9

Product Description

  • Finite Elements in Structural Engineering Practice

Model Optimization Using Artificial Intelligence (AI)

Further Information

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On the one hand, the two-part add-on Optimization & Costs / CO2 Emission Estimation finds suitable parameters for parameterized models and blocks via the artificial intelligence (AI) technique of particle swarm optimization (PSO) for compliance with common optimization criteria. On the other hand, this add-on estimates the model costs or CO2 emissions by specifying unit costs or emissions per material definition for the structural model.


1

Features

  • Artificial intelligence technology (AI): Particle swarm optimization (PSO)
  • Structure optimization according to the minimum weight or deformation
  • Use of any number of optimization parameters
  • Specification of variable ranges
  • Optimization of cross-sections and materials
  • Parameter definition types
  • Optimization | Ascending or Optimization | Descending
  • Application of parametric models and blocks
  • Code-based JavaScript parameterization of blocks
  • Optimization taking into account the design results
  • Tabular display of the best model mutations
  • Real-time display of the model mutations in the optimization process
  • Model cost estimation by specifying unit prices
  • Determination of the global warming potential GWP when realizing the model by estimating the CO2 equivalent
  • Specification of the weight-, volume-, and area-based units (price and CO2e)
2

Input

The structural optimization in the programs RFEM and RSTAB is a completion of the parametric input and is a parallel process beside the actual model calculation with all its regular calculation and design definitions. The add-on assumes that the model or block is built with a parametric context and is controlled in its entirety by global control parameters of the type "optimization". These control parameters are given a lower and upper limit and a step size to delimit the optimization range. To find optimal values for the control parameters, specify an optimization criterion (e.g. minimum weight) with selection of an optimization method (e.g. particle swarm optimization).

The cost and CO2 emission estimation is managed in the material definitions. Both options can be activated individually in each material definition. The estimation is based on a unit for unit cost or unit emission for members, surfaces, and solids. The units can be specified by weight, volume, or area.

3

Calculation

Two methods are available for the optimization process to find optimal parameter values according to a weight or deformation criterion.

The most efficient method for finding good parameter values using little calculation time is the near-natural particle swarm optimization (PSO). This artificial intelligence (AI) technology bears a strong analogy to the behavior of flocks of animals in search of a resting place. In such swarms, there are many individuals (cf. optimization solution - e.g. weight) who like to stay in a group and follow the group movement. With the assumption that each individual swarm member has a need to rest at an optimal resting place (cf. best solution - e.g. lowest weight) and that this need increases as the resting place is approached, the swarm behavior is also influenced by the properties of the space (cf. result diagram). The PSO process in RFEM and RSTAB takes a similar approach. The calculation run starts with an optimization result from a random assignment of the parameters to be optimized and repeatedly determines new optimization results with varied parameter values, which are based on the experience of the previously performed model mutations, until the specified number of possible model mutations is reached.

Alternatively, a batch processing method is available in the program as well. This method attempts to check all possible model mutations by randomly specifying the values for the optimization parameters until a predetermined number of possible model mutations is reached.

After calculating a model mutation, both variants also check the respective activated design results of the add-ons and save the variant with the corresponding optimization result and value assignment of the optimization parameters if the utilization is < 1.

The estimated total costs and emission are determined from the respective sums of the individual materials. The sums of the materials are composed of the weight-based, volume-based, and area-based partial sums of the member, surface, and solid elements.

4

Result

Both optimization methods provide a list of model mutations from the stored data at the end of the process, indicating the controlling optimization result and the corresponding value assignment of the optimization parameters. This list is organized in descending order and shows the assumed best solution at the top, where, with the determined value assignment, the optimization result is closest to the optimization criterion and all add-on results have a utilization < 1. Furthermore, once the analysis is completed, the program will adjust the value assignment to that of the optimal solution for the optimization parameters in the global parameter list.

In the "Cost estimation" and "Estimation of CO2 emissions" tabs, the material dialog boxes display the individual estimated sums of the assigned members, surfaces, and solids per unit weight, volume, and area individually. In addition, these tabs show the total cost and emission of all assigned materials.

Manual of Optimization & Costs / CO2 Emission Estimation for RFEM 6 / RSTAB 9

Manuals

Customer Projects

Customer Projects Designed with Dlubal Products

Fascinating customer projects designed with the Dlubal structural analysis software.

Price

Price
1,480.00 USD

The price is valid for United States.

RFEM 6
Cross-Laminated Timber (CLT) Building

Design

The Multilayer Surfaces add-on allows you to define multilayer surface structures. The calculation can be carried out with or without the shear coupling.

Price of First License
1,120.00 USD