Structural analysis with FEA

Although the structures which were calculated and optimised during this first case study are highly hypothetical, an attempt was made to comply with

65 70 75 80 85 90 95 100 0 5 10 15 20 25 D a y li g h t F a c to r (% ) Area (m2₎ Initial Population. Population 10

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structural integrity by running the optimisation model with the inclusion of automated Finite Element Analysis (FEA)(Fig. 34).

Fig. 34 – Surface with a fold (left) and surface with no fold (right).

Although overall test runs have shown that using the proposed construct, FEA can be integrated at this phase in a relatively straightforward manner, during testing two principal types of problems occurred. Firstly, the NURBS surface, which is controlled by changing the coordinates of the control points, can easily fold through itself which does not allow for a direct FEA, and should thus be avoided in this test case. As matter of fact, most of the time some part of the surface is folded, and only if one applies very big restrictions on the range of variation of the coordinates of the control points it is possible to avoid that the surface does not show any folding.

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Extensive test runs were done, and eventually the surfaces were given a different colour to evaluate visually if folding occurred, yellow for non-folding surfaces and red for surfaces which did contain a fold (Fig. 35).

However, if the variation range of the coordinates was too restricted, only very predictable surfaces were generated, but on the other hand if the coordinates were less limited a considerable part of the generated surfaces could not be analysed in the FEA software and had to be rejected beforehand, without visual presentation to the designer. It was stated that in this first case study, form would prevail above structural integrity. The generated optimised roof structures are at the end only (rather precise) form suggestions. This should be usually enough exploration in the conceptual phase of the design process, and in any design process, if it was decided to continue with development. During and after the detailing process other simulation and analysis software can be applied, some with even better reliability than Ecotect, software such a ESP-r (2010) and EnergyPlus (2010), which can provide the architect with a more detailed analysis of the proposed object. However, as ESP-r and EnergyPlus are software programs which are not that simple to integrate in an automated optimisation cycle, they were not considered for this research work, but they are excellent tools for manual analysis and simulation and can be used to double check and validate the results from the optimisation construct.

4.1.7 Graphical User Interface

A typical GUI for an optimisation process, as was executed in case study 1, could be organised and function in the manner described and illustrated in this paragraph (Fig. 36). The basis parametric model which the designer will use for optimisation is presented, and from two (in this case) pop-up menus the designer can make a selection between different characteristics, which will influence the optimisation process. The material can be selected, in this demonstration the roof-like structure can be built in three kind of composite materials, and the total number of optimisation cycles which the optimisation algorithm will run before presenting results, can also be chosen, although the total number of optimisation cycles depends very much on the kind of

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Fig. 36 – Opening Screen GUI.

Computational time is also an important part of an optimisation process and depends also in part also on the computational power of the computer where the software construct is running. However, it was argued that any parametric model, before any optimisation, should be tested for fitness. If the optimised results do not show an important difference with the original model, this

could mean that very little can be expected from this digital design process.

This is an important first iterative cycle in the software construct. In a fully digital design process, form generation and form finding are paramount to the positive evolution and final outcome of the process. But promising parametric geometries do not always generate interesting results or distinctive

modification of form and appearance to qualify for optimisation as it is intended in a process of digital design. It is therefore important for the designer to understand when a digital design process has to be aborted and restarted with another better prepared basis model. Thus, with the intent to proof-run the process, in the proposed FFO the designer can choose from a pop-up menu how many generations will be created before presenting the optimised results.

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Fig. 37 – Selection of some of the parameters.

Once started running the selected quantity of generations, the optimisation algorithm will then produce an intermediate selection of six different Pareto- Optimum solutions evenly spread over the Pareto Frontier of best solutions.

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These solutions will be presented as images together with a small summary of some of the most important results of their specific performance simulation (Fig. 38). It will then depend on the designer which of the presented

“promising” or “good” solutions will be selected and reseeded as phenotypes in the optimisation algorithm (Fig. 39) for another run.

Fig. 39 – Selecting the preferred solutions for re-seeding.

Whenever one of the intermediate solutions seems like a promising result for development, more information can be provided and the designer can analyse in detail if this solution fits his expectations, after which he can either choose for further development in detail, or return to the optimisation iterations (Fig. 40).

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Fig. 40 – Analysing a possible candidate for further development.

This process can go on as long as no solutions are satisfying the aesthetical criteria of the designer or architect. Based on research on similar optimisation goals and with the use of the same MOEA, it is expected that, with a suitable initial geometry a fruitful process of form generation will produce valuable solutions after ten cycles at most (Gaspar-Cunha & Covas, 2004). Continuing the process of optimisation would only generate similar or imperceptibly different solutions unrelated to the goal of optimisation in design as intended in this study.

A graphical user interface is in no way indispensable in the process of

optimised generative form finding or building. The process of optimisation can almost completely be executed in the background or in batch. However, it will make the process more cumbersome and with a different perception of interaction.