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For architects as well as developers and urban planners working on the floor plans or estimating the shape and dimensions of large buildings is always a challenge. This task requires some knowledge and what is more important – even with understanding of the process it is a very time-consuming task. One should take into consideration the arrangement of all rooms as well as adjacencies and connections of main spaces. Presented project can be considered as an exploration of various ways of generating floor plans for public buildings, which was followed by creating a new algorithm for solving that task. Public buildings were chosen as a main target of conducted research because of their complex and non-standardized structure. The aim was to try different previously described approaches, invent completely new techniques and methods, choose the best of them and incorporate them into our own generator. The creation of working generator prototype was an important target of the project and should not be considered as its final stage, since this trial revealed some positive and negative sides of chosen approach and therefore serves as an intermediate stage. Floor plans analysis was conducted in order to understand the structural patterns of communication spaces in built public houses. One of positive outcomes of this investigation is that public buildings do not share a lot of features in common, therefore this field of exploration is rather unobstructed and provides a lot of possibilities for experiments.

This work is based on the concept that the structure of a city can be defined by six basic urban patterns. To enable more complex urban planning as a long-term objective I have developed a simulation method for generating these basic patterns and for combining them to form various structures. The generative process starts with the two-dimensional organisation of streets followed by the parceling of the remaining areas. An agent-based diffusion-contact model is the basis of these first two steps. Then, with the help of cellular automata, the sites for building on are defined and a three-dimensional building structure is derived. I illustrate the proposed method by showing how it can be applied to generate possible structures for an urban area in the city of Munich.

We present and compare two evolutionary algorithm based methods for rectangular architectural layout generation: dense packing and subdivision algorithms. We analyze the characteristics of the two methods on the basis of three floor plan scenarios. Our analyses include the speed with which solutions are generated, the reliability with which optimal solutions can be found, and the number of different solutions that can be found overall. In a following step, we discuss the methods with respect to their different user interaction capabilities. In addition, we show that each method has the capability to generate more complex L-shaped layouts. Finally, we conclude that neither of the methods is superior but that each of them is suitable for use in distinct application scenarios because of its different properties

This work presents a concept of interactive machine learning in a human design process. An urban design problem is viewed as a multiple-criteria optimization problem. The outlined feature of an urban design problem is the dependence of a design goal on a context of the problem. We model the design goal as a randomized fitness measure that depends on the context. In terms of multiple-criteria decision analysis (MCDA), the defined measure corresponds to a subjective expected utility of a user. In the first stage of the proposed approach we let the algorithm explore a design space using clustering techniques. The second stage is an interactive design loop; the user makes a proposal, then the program optimizes it, gets the user’s feedback and returns back the control over the application interface.