Random forest to predict the elasticity modulus of concretes made with CDW
DOI:
https://doi.org/10.46421/enarc.v8i00.3719Keywords:
Recycled aggregate concrete, Optimization, Machine learning, Random ForestAbstract
The use of construction and demolition waste in concrete production has become a recurring alternative due to their benefits, such as cost reduction, reduction of CO2 emissions, and less environmental pollution. However, regarding the mechanical behavior of concrete with recycled aggregate, it is known that its mechanical capacity is lower than that of natural aggregate concrete, making it essential to predict the mechanical properties of the material to determine the mix in which the mechanical properties are maximized, and the environmental impacts are minimized. Thus, this work evaluates the use of random forests to model the modulus of elasticity of concrete with recycled aggregate and, from that, determine traces of low environmental impact. The results pointed out the feasibility of applying the machine learning technique in the modeling, obtaining a model with a coefficient of determination of 93.5%.
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