Orientador: Benamy Turkienicz
http://www.lume.ufrgs.br/handle/10183/11433
Can a computer appreciate a work of art? Can a computer create a new work of art? What does it mean for an object to be a work of art? How are objects understood as works of art? Dozens of ways of understanding art have been proposed. Is there one true way to understand works of art? If not, what do the different ways of understanding art have in common? How might they be implemented in a computer? Does this “computer” or “algorithmic” approach have any contributions to make to the field of art and aesthetics?
This work aims at describing the elements that characterize Oscar Niemeyer’s singular architectural language. It argues that the identification of these elements passes for the scrutiny of non-visible aspects of his work. The identification was possible taking into consideration from the analysis of buildings characterized for curved profile and the construction of a model that associates the compositional elements utilized by Niemeyer to a Shape Grammar. The utilization of the model made it possible to reveal the generative principles - set of rules, vocabulary and geometric relations - that characterize Niemeyer’s style and architectural language. It also helped showing how Niemeyer’s language associates, in an original way, operations of transformation such as rotation, reflection, and translation to a vocabulary of curves. The association has its parameters on a drawn line which acts as a regulator based on the golden section. As its conclusion, the work suggests possibilities of development of this grammar for all the forms utilized by Niemeyer and the aplication of generative principles in the teaching of architecture.
The generation of shapes that conform to particular styles, using shape computation tools based on the mathematics of shape grammars [Stiny 1980], has been demonstrated in a number of domains [Prats et al 2006]. Researchers at the University of Leeds have built the world’s first and only 3D shape grammar implementation for curvilinear shapes [Chau 2004]. The basic elements of a shape grammar are shown in Figure 1. The box at the top of the figure shows an initial shape (that seeds the computation) and the two shape rules that are applied during the computation. The shapes at the bottom of the figure show a fragment of the network of shapes that can be computed from the initial shape through the application of the shape rules. The application of a shape rule involves two key steps. Firstly, the shape on the left-hand side of a rule must be identified in the shape from which a new shape is to be computed; this is referred to as “sub-shape detection”. Secondly, the rule is applied by replacing the sub-shape from the left-hand side of the rule with the shape on the right-hand side of the rule. Once a sub-shape has been detected, the Leeds system can automatically apply a rule. However, the sub-shapes have to be identified manually because the automatic detection of sub-shapes is an open research question within the shape grammar community.