> Summary

Image by Jamal van Kastel

This thesis' main contribution is a visual analytics tool that allows for the visualization of data and design geometries in a highly dynamic, game-like 3D data environment, allowing for the exploration of both quantified and non-quantified performances of a collection of design options. 

This thesis' Computational Design System aids in the design of a nearly Zero-Energy sports hall for a sports facility in Overhoeks, Amsterdam. 

The Computational Design System (CDS) consists of two different parts of a workflow, each addressing specific steps. The first half of the workflow is used to generate a dataset of design options. The latter half is used to evaluate this dataset.  

The Iterative Design System is used as follows: 

1. Design alternatives are generated using parametric models developed in Rhino & Grasshopper. Multiple Grasshopper scripts are used to generate design options that encompass multiple design concepts.   LIMITATIONS:  generating random geometry does not necessarily result in a single optimal solution therefore optimizations are completed 

1. Simulations are run on each model using Grasshopper plug-ins the results are used to further optimize energy, thermal & lighting performance of the different geometry 

1. Evolutionary solver Octopus is used to automatically generate design options, iteratively converging towards optimized design solutions. 

Then the Data Analytics system is used as follows: 

1. The data is processed and clustered to aid the designer in decision making. Machine learning is used for some of the techniques. The data in processed in multiple different ways to provide different levels of information: 

   1. Self Organizing Maps are used to reduce the dimensionality of the data, creating a two-dimensional representation of the high-dimensional design space. The Self-Organizing Map creates ‘soft clusters’ of similar design options and augments the dataset with approximations for non-simulated areas of the design space.  LIMITATIONS: not easy-to-read overview of the cells’ performances 

   2. A decision tree is used to cluster the data based on the user-defined criteria  

   3. Hierarchical clustering is used to cluster the data based on the user-defined criteria 

   4. Annual Performance Data is visualized using a method best described as a combination of a stacked bar graph and an analytic hierarchal process, these are methods used to differentiate between non-empirical values of options 

   5. Pictograms are used to visualize the designs with best or worst performances, this is to aid the designer in quickly identifying these characteristics 

   6. A correlation matrix can be used to further indicate interrelationships between design aspects  

2. The above noted different methods of clustering and visualizing the data are combined in the Unreal Engine to create a highly interactive environment for visual analytics. The visual analytics tool positions all buildings in a ‘data landscape’, a visual representation of the various data analytics techniques. Features of the landscape give insight in the buildings’ performances. By integrating multiple data analytics methods and facilitating access through game-like means of interactivity, users can intuitively explore the design space to make informed design decisions.