Análise da fluência por compressão no isolamento ao ruído de impacto de placas cimentícias com resíduo de EVA

Santos, Fabianne Azevedo dos; Melo, Aluísio Braz; Pimentel, Roberto Leal; Nunes, Maria Fernanda de Oliveira

doi:10.1590/s1678-86212022000100582

Cited by 1 publication

(1 citation statement)

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“…EVA possesses favorable characteristics, such as good processability, thermal stability, impact resistance, fatigue resistance, resilience, toughness, and flexibility [4][5][6]. As a result, numerous studies have been conducted to explore its potential as an aggregate in the civil construction sector, aiming to produce lightweight concretes with good acoustic and thermal performance [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Application of Segmentation and Fuzzy Classification Techniques (TSK) in Analyzing the Composition of Lightweight Concretes Containing Ethylene Vinyl Acetate and Natural Fibers Using Micro-Computed Tomography Images

Silva,

Iglesias,

Ambrosio

et al. 2023

Applied Sciences

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The reuse of ethylene vinyl acetate (EVA) discarded from the sports and footwear industries as a partial substitute for gravel in concrete is a way of reducing anthropogenic environmental impacts by enabling the production of lighter structures with similar and superior resistance to those built with traditional concrete. Several studies have been published replacing gravel with EVA and natural fibers, resulting in lighter, more resistant, cheaper, and more ecological concrete. However, there is no methodology to characterize the composition and internal structure of these materials accurately and efficiently, which is vital for quality control in mass-produced pre-molded shapes. In this study, an automated system was developed to measure the percentage of each component in test cores using micro-computed tomography (Micro-CT). For this, (1) Micro-CT images were obtained for concrete test cores made with coarse aggregate consisting of gravel, EVA, and natural fibers in different proportions; (2) the images were segmented differentiating the gravel from the rest of the aggregate, while the remainder was further segmented with the cementitious matrix as background, and the pores, EVA fragments, and fibers as objects against this background; and (3) a Takagi–Sugeno–Kang-type fuzzy inference system was built to classify the objects in the foreground as pores, EVA, and fiber. The tool developed in this manner estimates the percentages of each concrete component and also provides an estimate of the porosity.

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