Next Article in Journal
Inverse Kinematics Proposal to Automatize the 3D Scanning of Handball Shoes by Using a Robotic Arm with 3 Actuated Joints
Previous Article in Journal
Research on Algorithm for Authenticating the Authenticity of Calligraphy Works Based on Improved EfficientNet Network
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 14
Issue 1
10.3390/app14010296
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Miquéias A. S. Silva
1,
Susana M. Iglesias
1,*,
Paulo E. Ambrosio
1,
Iram B. R. Ortiz
1,
Dany S. Dominguez
1 and
Diego Frias
2
1
Pós-Graduação em Modelagem Computacional (PPGMC), Universidade Estadual de Santa Cruz (UESC), Ilhéus 100190, BA, Brazil
2
Departamento de Ciências Exatas e da Terra, Universidade do Estado da Bahia (UNEB), Silveira Martins, Salvador 41150-000, BA, Brazil
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(1), 296; https://doi.org/10.3390/app14010296
Submission received: 9 August 2023 / Revised: 22 September 2023 / Accepted: 26 September 2023 / Published: 28 December 2023
(This article belongs to the Section Materials Science and Engineering)
Browse Figures
Versions Notes

Abstract

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.
Keywords: digital image processing; fuzzy inference; reinforced lightweight concrete; X-ray micro-tomography digital image processing; fuzzy inference; reinforced lightweight concrete; X-ray micro-tomography

Share and Cite

MDPI and ACS Style

Silva, M.A.S.; Iglesias, S.M.; Ambrosio, P.E.; Ortiz, I.B.R.; Dominguez, D.S.; Frias, D. 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. Appl. Sci. 2024, 14, 296. https://doi.org/10.3390/app14010296

AMA Style

Silva MAS, Iglesias SM, Ambrosio PE, Ortiz IBR, Dominguez DS, Frias D. 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. Applied Sciences. 2024; 14(1):296. https://doi.org/10.3390/app14010296

Chicago/Turabian Style

Silva, Miquéias A. S., Susana M. Iglesias, Paulo E. Ambrosio, Iram B. R. Ortiz, Dany S. Dominguez, and Diego Frias. 2024. "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" Applied Sciences 14, no. 1: 296. https://doi.org/10.3390/app14010296

APA Style

Silva, M. A. S., Iglesias, S. M., Ambrosio, P. E., Ortiz, I. B. R., Dominguez, D. S., & Frias, D. (2024). 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. Applied Sciences, 14(1), 296. https://doi.org/10.3390/app14010296

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop