Parametric Design


Robert Vierlinger1, Arne Hofmann1
1 Researcher – University of Applied Arts, Vienna, Austria

Figure 1: Conflictive objectives of sun exposure and bearing behaviour

Today architectural design processes are more and more influenced by parametric methods. As these allow for a multiplicity of alternatives, the design process can be enriched by computational optimization. Extensive research has shown the efficiency of optimization in engineering and design disciplines.

Though, optimization is hereby rather a technical than a design task; it is limited to different autonomous specialist areas and does not enable a comprehensive approach. Advanced optimization methods facilitate the generation of complex systems, but these procedures are directed and do not provide turnoffs, multiple solutions or altering circumstances.

Figure 2: Pareto-dominated space of solution A Figure 3: Pareto-non-dominated front of a set

These however are things that are essential for architectural design processes, which mostly do not have clearly defined starting and end points. This practice subdivides the workflow into two independent and recurring tasks: the generation of a parametric model followed by optimization of its driving parameters.

The result is then assessed with respect to its actual qualities. The design either is kept, or modifications on the parametric model, its auxiliary conditions and parameters are made and the optimization process starts again from scratch.

Figure 4: Levels of interaction with / filter levels of the design assistant

The aim of the research project, this paper is referring to, is the development of a flexible generation and optimization framework for practical use in the sense of a continuously accompanying design explorer, in which parameterization is adaptable and objective functions are changeable at any time during the design process.

The user is supported in his/her understanding of correlations by identifying a multiplicity of optimal solutions utilizing state-of-the-art multi-objective search algorithms within the core of the framework. Considering the tool as an interactive design aid, an intuitive interface allowing for extensive manual guidance and verification of the search process is featured.

Zooming, filtering and weighting within the genotypic, phenotypic and objective space comply with an extensive support of man-machinedialogue and incorporation of non- or not-yet quantifiable measures. A reusable search history aids examination of design alternatives and the redefinition of constraints, maintaining the continuity of the search process and traceability of results in the sense of rational design verification.

Figure 5: Rendering of a design alternative Figure 6: Limited support areas for platform

Within this work it is not planned to focus on specific optimization targets, but to build an open framework to allow for all kinds of objective functions and in particular the mediation between conflicting targets. In a broader context of general design research, the process of design development from early to final solution is examined, where not even optimization itself but the entire search for an adequate optimization setup is targeted.

Even the research process is at its very beginning, in this paper we already propose a tool that integrates key features of a continuous design-assistant. User guided, adaptive multi-objective search algorithms, re-entrant history records, parallelization of computation, and a user interface that allows control in a manifold and intuitive way.

Figure 7: Parametric setup for example 2, search assistant connecting to all numeric in- and outputs

The techniques and tools used to design a building have changed significantly over the last two centuries. For a long time, manual drawings and empirical design rules have been the only way of designing buildings and artifacts. With the rise of modern science, analytical approaches of quantification, and, later, also computer aided drawing technologies were applied in planning.

Digital techniques allow the more flexible figuration of geometry and building information, as well as numerical methods enable quicker analysis of more complex systems. Finally, the last two decades show a shift towards integrated, parametrically defined information- and analysis models. Via parametric associative modeling techniques, a representation is generated by a set of rules that are relating to each other.

The term representation is used in an abstract way, meaning, e.g., a geometric model, BIM, solar data modeling, a structural model, etc. The data serving as the basis for these models can be altered easily, so a different alternative is simply produced by changing the input parameters. Ideally, all different models of a project are integrated into a single process of generation, so every change affects its related parts, resp. can be analyzed for compatibility.

Figure 11: Different alternatives of example 2 in its final setup of parameters and objectives.

Considering those two aspects, a fundamental exploitation of the concepts in design methods can be proposed. A design option can be set into relation to other alternatives in a goal-oriented way by comparing the individual trade-off states. To be able to make a justified decision, the designer needs to know about 1. the extreme trade-off configurations and 2. the relations that are causing the necessity for trade-offs.

Traditionally, the process to gain this knowledge is relying on a designers’ experience and, depending on the complexity and uncertainty of the task, additional try-and-error to unveil the relational nature of a design’s sub-problems. We propose an assistant tool for parametric design that helps in this process, utilizing contemporary design tools, computational power, and findings in artificial intelligence that are novel to our time, thus firstly enabling this approach.

’The first industrial revolution showed us how to do most of the world’s heavy work with the energy of machines instead of human muscle. The new industrial revolution is showing us how much of the work of human thinking can be done by and in cooperation with intelligent machines. Human minds with computers to aid them are our principal productive resource.

Figure 12: Densification of search around the user’s preferred solutions, marked blue

Understanding how that resource operates is the main road open to us for becoming a more productive society and a society able to deal with the many complex problems in the world today. The tools now being forged for aiding architectural design will provide a basis for building tools that can aid in formulating, assessing, and monitoring public energy or environmental policies, or in guiding corporate product and investment strategies.’ (Simon, 1986).

Having received a Nobel Price for fundamental research in decision making and rationality, Herbert Simon coined the term of artificial intelligence in 1955 with the development of the ’Logic Theorist’, after that attempting research on a ’General Problem Solver’. As one of the most generic contemporary approaches to computationally tackle problems, the usage of Genetic Algorithms is chosen.

Some precedents facilitating evolutionary optimization within Grasshopper do exist, however none of them consider the problems of multi-objective settings, incorporate user-interaction or try to overcome the computational limitations for professional application. A multitude of recent research projects in computer science, philosophy, and engineering indicates the potential still lying in the development of concepts, algorithms and platforms regarding multi-objective evolutionary optimization and search.

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