Complex Buildings and Cellular Automata—A Cellular Automaton Model for the Centquatre-Paris
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We explored the relational dynamic elements of complex buildings, a type of architecture designed to incubate uses, located in urban areas with high housing density. The uses of complex buildings concern different elements, including the network of agents using or managing them, the
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We explored the relational dynamic elements of complex buildings, a type of architecture designed to incubate uses, located in urban areas with high housing density. The uses of complex buildings concern different elements, including the network of agents using or managing them, the environment, and the activities and functions that take place occasionally, temporarily, or permanently.. Data was gathered through ethnographic research lasting 6 months, and a chronotopian approach was used to describe time and space. We analysed and discussed the interaction of the elements of complex buildings through a cellular automaton model, a computational method that simulates the growth of complex systems. It was used here to generate patterns that suggest configurations of uses that can optimize management and therefore increase economic and social capital. The cellular automaton representation was used to develop an abstraction of the Centquatre, a public cultural center in Paris. This center is a good example of a complex building, being based on a public–private partnership and having an architectural configuration designed to host a wide range of art, social, and productive activities. The building includes a large central space used as an urban public area open to different types of people. The relevance of the case study lies in its capacity to produce economic value by combining different uses, and also by welcoming different people to the public space. We found that the multistate cellular automata representation allows the Centquatre behavior to be modeled by means of combinatorial and statistical methods. The correlations between the automaton behavior and the number of users can be identified using machine learning techniques related to random forests. We argue that this approach makes it possible to improve the planning of complex buildings.