Random Forest aplicada à predição do módulo de elasticidade de concretos com RCD
DOI:
https://doi.org/10.46421/enarc.v8i00.3719Palavras-chave:
Concreto com agregado reciclado, Otimização, Aprendizado de máquina, Random ForestResumo
O emprego de resíduos de construção e demolição na produção de concretos, tornou-se uma alternativa recorrente devido aos benefícios que apresentam, a exemplo da redução do custo, redução das emissões de CO2 e menor poluição ao meio ambiente. Entretanto, no que se refere ao comportamento mecânico do concreto com agregado reciclado, sabe-se que sua capacidade mecânica é inferior ao de concretos com agregado natural, tornando imprescindível prever as propriedades mecânicas do material, a fim de determinar o proporcionamento em que se maximizam as propriedades mecânicas e minimizam-se os impactos ambientais. Neste trabalho avalia-se o uso de florestas aleatórias para mapear o módulo de elasticidade do concreto com agregado reciclado, que a partir deste, poderá auxiliar na formulação de concretos com baixo impacto ambiental. Os resultados encontrados demonstram a viabilidade da aplicação da técnica de aprendizado de máquina utilizada na modelagem, obtendo um modelo com coeficiente de determinação de 93,5%.
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