By: Carmo Gonçalves Machado Cardoso
Architecture, since its inception, has been through a constant process of diversifying its means of representation, which deeply impacts how designers view the discipline and manage the design process. The notion that, in Architecture, form should follow function, has been one of the core principles introduced in the 20th century. Rejecting ornaments that do not benefit the construction in any way is part of a broader pragmatic approach to building forms. Beauty and aesthetics were no longer the prime concern of architects, and notions such as thermal comfort and harnessing daylight became increasingly important.
With expanding computational power and the development of analysis software, complex mathematical formulas, based on physics principles, are embedded into the software and can be easily applied to models of buildings. This allows for the creation of optimization workflows that not only test the efficiency of a completed building, but can aid in defining the optimal design solution. Thus, this brings on a shift from form making to form finding. Using computational tools provide an ample range of solutions that can be easily changed and tested for efficiency, while a manual approach would take considerable added amounts of effort and time, even sacrificing accuracy. These tools favour the creation of multiple models, facilitating the process of seeking the best characteristics that amount to a better performing building in a particular environment.
This thesis intends to develop a combination of parametric design, analysis tools and optimization algorithms in order to find an optimal design solution, based on a specific architectural design as a conceptual basis. That is to say, an initial design will be shaped according to variables that impact building performance the most. Without jeopardizing the design intent, it is possible to reach a novel and contemporary version of the form follows function principle 1 : form follows performance.
Parametric Design is a design approach based on algorithmic thinking that exploits associative geometry to describe relationships between objects, creating a dependencies between them. Defined by a set of rules and encoded variables called parameters, a parametric model is able to instantiate a wide range of possible iterations of a design solution through the exploration of its parameters values. For example, a parametric model of a tree could be based on a subdivision process where each new branch has half of the size of the previous one, and each branch produces two new branches. The parametric model would accept as parameters the length of the initial branch and a value n, representing the number of subdivisions of the tree. By exploring different values for parameter n, a wide variety of results can be achieved: if n=0, the tree would only produce the trunk; if n=1, the trunk would have two branches; if n=2, there would be two new branches coming from each of the previous ones, and so on.
Parametric tools enable the creation of parametric architectural models, exploiting the flexibility of Parametric Design. It allows the fast exploration of shapes that can be changed and manipulated, according to the designer’s preferences.
Another important development brought by digital technology is the creation and spread of analysis tools. With the growing demand for sustainable building, it has become crucial for designers to take sustainability issues and building performance into account during the development of their designs. For clarification, a sustainable building is a building that is highly efficient in terms of resource use (such as energy, water, and materials). For that reason, analysis tools have become important tools for the design process: they facilitate performance analyses by executing the necessary calculations and allow architects to make more informed decisions regarding building performance. There are various types of tools for different types of analysis that can be used to evaluate a design, being Radiance for daylight analysis and EnergyPlus for thermal calculations just a few examples.
By combining analysis tools with the flexibility of parametric tools, better performing buildings can be achieved. Parametric tools can significantly reduce the amount of time that designers spend manually modifying their designs. Thus, more design alternatives can be tested and compared in order to find architectural solutions with better performances.
Nevertheless, both manual and parametric processes require the designer to identify design trends that result in improvements. Design trends can be understood as the typical range of values for certain specific parameters that will result in improvements in the design, according to certain criteria. Considering a simple example, while trying to improve natural lighting inside a building, bigger openings will result in more natural daylight. Therefore, the design trend to improve natural lighting is to increase the size of the openings. However, depending on the design and the considered criteria, identifying these design trends might not be a trivial process and might lead to combinations of parameters that are not obvious.
This process might be facilitated through the use of optimization processes. Optimization can be defined as the search for the best possible solution, called the optimal solution, from a set of different options, according to certain criteria. During an optimization process, the architect is aided by an algorithm of his/hers choice to find the optimal solution for a certain design, according to the given parameters and optimization goals (e.g. improving building performance). This algorithm produces different designs, compares them with the designs that were previously produced, selects and retains the designs that achieved best results, according to the optimization goals.
When performing an analysis, performance criteria may be established for that analysis. Identifying which design, or design trends, will be optimal to satisfy a certain performance becomes more difficult when working with more than one type of analysis. This is particularly the case when the criteria have an inverse relationship. For example, if the objectives are to reduce the heat gains but at the same time increase the natural lighting of a building, these two criteria would have an inverse relationship. This is because if more natural light enters a building, then there is an increase in heat gains due to increased solar radiation. Imagining that there is a parametric feature in the design that allows the size of the building’s openings to be changed, the optimal opening size would be a balance between the increased natural light and the heat gained.