Abstract
Abstract This paper describes the construction of an information flow that combines parametric modelling and genetic algorithms to optimise both the total weight of a structured steel roof and the shape of its surface to maximise the generation potential of photovoltaic energy. Through the systematisation of a design process using genetic algorithms integrated with generative design as an architectural design method, architects can work with problem parameterisation, going one step further than the mere formal proposition of solutions.As a result, we present a process in which there is a dialogical relationship between the architect and project in a continuous flow of information in the initial steps of the design process, guided by predefined optimisation objectives. Therefore, it is relevant to note that the designer accepts a reduced creative control over the final shape in favor of control over the core principles that constitute the optimisation algorithms. An approach that involved the designer in a process of post computational processing evaluations is also highlighted, giving him better support to discuss and analyze the design options.
Highlights
Traditional design processes can be described as conscious experimentation processes: formal solution hypotheses are formulated, tested and discarded or adjusted until a designer is satisfied with the result
We used Grasshopper, a node-based editor within the Rhinoceros modelling software that allows the creation of a series of geometric operations to produce a flexible geometry, based on input variables. These variables can be set as ranges rather than predefined values: the total horizontal span of the roof, for instance, should not be smaller than a certain value but could be larger, just as its minimum height at any given point. These two variables alone can result in countless shape possibilities, each one with different proportions, total weight, ideal shape and so on
We aim to present the process of developing a formal solution that would minimise the total weight of the structure and its cost, while maximising the energy generation potential of the structure
Summary
Traditional design processes can be described as conscious experimentation processes: formal solution hypotheses are formulated, tested (in drawings and models) and discarded or adjusted until a designer is satisfied with the result. The potential complexity of the roof structure increases exponentially To manage this complexity, these variables are controlled by a genetic algorithm that tests different values while aiming to optimise the model through minimising a single index: the division of the total steel cost of each design, expressed in Brazilian reais (R$) by the total solar energy generation potential, expressed in R$. These variables are controlled by a genetic algorithm that tests different values while aiming to optimise the model through minimising a single index: the division of the total steel cost of each design, expressed in Brazilian reais (R$) by the total solar energy generation potential, expressed in R$ This means that the algorithm will seek to reduce the design, but will be willing to make it bigger if the additional cost is more than offset by the extra savings generated by the solar panels. We present a process whereby a structure with a predefined shape can be optimised but, beyond that, the predefined metrics can yield shapes that had not been anticipated by the designer
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