Aplicação do design generativo no dimensionamento de barreiras acústicas
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Data
2023-05-12
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Editor
Universidade Federal de Goiás
Resumo
Controlling road traffic noise close to inhabited areas can be achieved by
installing sound barriers. Nonetheless, the design process of such elements requires large
amount of time and energy from the designer on a trial and error cycle. Aiming to
automate part of this dynamic, especially in its early stages, this paper proposes a sizing
and performance evaluation model for noise barriers, created through visual
programming and generative design. Thus, from a hypothetical scenario – a terrain
adjacent to a highway – it was sought to find the points with the greatest possible
attenuation after the insertion of a thin and semi-infinite barrier in free field conditions.
Performed with an evolutionary solver, the analyses were organized by the barrier height
and type of sound source (point or line sources). Attenuation values were calculated
separately for each receiver height and frequency octave band (from 63 Hz to 8 kHz). For
simplification purposes, material characteristics and weather effects were not considered.
The results shown in the graphs correspond to expectations prior to the simulations – the
higher the receiver in relation to the barrier, the lower the sound reduction – proving the
efficiency of the method used. The best values could be easily selected and the
visualization of the solutions made in real time.
Descrição
Palavras-chave
Barreiras acústicas, Ruído de tráfego, Design generativo, Programação visual, Algoritmos evolutivos, Noise barriers, Traffic noise, Generative design, Visual programming, Evolutionary algorithms
Citação
CARDOSO, Lucas Martins; ROCHA, Dariane Gomes; REIS, Ricardo Prado Abreu. Aplicação do design generativo no dimensionamento de barreiras acústicas. REEC - Revista Eletrônica de Engenharia Civil, Goiânia, v. 19, n. 1, p. 01-23, 2023. DOI 10.5216/reec.V19i1.76008 Disponível em: https://revistas.ufg.br/reec/article/view/76008/39765. Acesso em: 07 ago. 2023.