You can be sure that costs are an important factor in the structural planning of any project. It is also essential to adhere to the provisions on emissions estimation. The two-part add-on Optimization & Costs/CO2 Emission Estimation makes it easier for you to find your way through the jungle of standards and options. It uses the artificial intelligence technology (AI) of the particle swarm optimization (PSO) to find the right parameters for parameterized models and blocks that guarantee the compliance with the usual optimization criteria. This add-on also estimates the model costs or CO2 emissions by specifying unit costs or emissions per material definition for the structural model. With this add-on, you are on the safe side.
- 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 parametrization 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 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.