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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The two-part add-on Optimization & Costs/CO2 Emission Estimation helps you to plan sustainably. On one hand, it finds suitable parameters for parameterized models and blocks via the artificial intelligence technology (AI) of particle swarm optimization (PSO) in order to comply with the usual optimization criteria. On the other hand, this add-on estimates the model costs or CO2 emissions by specifying the unit costs or emissions per material definition for the structural model.



  • 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)


Did you know? The structural optimization in the programs RFEM and RSTAB is a completion of the parametric input. It is a parallel process beside the actual model calculation with all its regular calculation and design definitions. The add-on assumes that your model or block is built with a parametric context and is controlled in its entirety by global control parameters of the "optimization" type. Therefore, these control parameters have a lower and upper limit and a step size to delimit the optimization range. If you want to find optimal values for the control parameters, you have to specify an optimization criterion (for example, minimum weight) with the selection of an optimization method (for example, particle swarm optimization).

You can already find the cost and CO2 emission estimation in the material definitions. You can activate both options individually in each material definition. The estimation is based on a unit for unit cost or unit emission for members, surfaces, and solids. In this case, you can select whether to specify the units by weight, volume, or area.



There are two methods that you can use for the optimization process, with which you can find optimal parameter values according to a weight or deformation criterion.

The most efficient method with the littlest calculation time is the near-natural particle swarm optimization (PSO). Have you heard or read about it? This artificial intelligence (AI) technology has a strong analogy to the behavior of flocks of animals, looking for a resting place. In such swarms, you can find many individuals (cf. optimization solution - for example, weight) who like to stay in a group and follow the group movement. Let's assume that each individual swarm member has a need to rest at an optimal resting place (cf. best solution - for example, lowest weight). This need increases as the resting place is approached. Thus, the swarm behavior is also influenced by the properties of the space (cf. result diagram).

Why the excursion into biology? Quite simply – the PSO process in RFEM or RSTAB proceeds in a similar way. The calculation run starts with an optimization result from a random assignment of the parameters to be optimized. It repeatedly determines new optimization results with varied parameter values, which are based on the experience of the previously performed model mutations. The process continues until the specified number of possible model mutations is reached.

As an alternative to this method, the program also offers you a batch processing method. 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. Furthermore, they save the variant with the corresponding optimization result and value assignment of the optimization parameters if the utilization is < 1.

You can determine the estimated total costs and emission 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.



Both optimization methods have one thing in common. At the end of the process, they provide you with a list of model mutations from the stored data. Here zou can find the details of the controlling optimization result and the associated value assignment of the optimization parameters. This list is organized in descending order. You can find the assumed best solution shown in the first line. For this, the optimization result with its determined value assignment is closest to the optimization criterion. 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 material dialog boxes, you can find the additional tabs "Cost Estimation" and "Estimation of CO2 Emissions". They show you the individual estimated sums of the assigned members, surfaces, and solids per unit weight, volume, and area. Furthermore, these tabs show the total cost and emission of all assigned materials. This gives you a good overview of your project.



  • Parameter optimization of Smart Blocks based on a JS code
  • AI-based optimization process that uses the FEA result to determine the subsequent mutation
  • Optimization of parameterized structural models for the smallest model deflection, the lowest price, the lowest weight, or the maximum/minimum of a user-defined parameter
  • Consideration of the defined design checks during the optimization
  • Simple specification of unit prices and CO2 emissions using the material and cross-section properties of the model
  • Tabular result display of the cost and CO2 emission estimation

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



1,650.00 EUR

The prices apply to the use of the software in all countries.

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