Cargando…

Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate

The article contains the results of selected tests of physical and mechanical properties of mortars differentiated in terms of the binder used: cement, epoxy, epoxy modified with PET waste glycolysate and polyester. Each type of mortar was modified by partial (0–20% vol.) substitution of sand with a...

Descripción completa

Detalles Bibliográficos
Autor principal: Dębska, Bernardeta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694445/
https://www.ncbi.nlm.nih.gov/pubmed/36431597
http://dx.doi.org/10.3390/ma15228111
_version_ 1784837801337946112
author Dębska, Bernardeta
author_facet Dębska, Bernardeta
author_sort Dębska, Bernardeta
collection PubMed
description The article contains the results of selected tests of physical and mechanical properties of mortars differentiated in terms of the binder used: cement, epoxy, epoxy modified with PET waste glycolysate and polyester. Each type of mortar was modified by partial (0–20% vol.) substitution of sand with an agglomerate made from waste polyethylene. The obtained results were used to build a database of mortar properties, which was then analyzed with the use of three different techniques of knowledge extraction from databases, i.e., cluster analysis, decision trees and discriminant analysis. The average results of the properties tested were compared, taking into account the type of mortar, indicating those with the most favorable parameters. The possibilities and correctness of mortar classification with the use of the indicated “data mining” methods were compared. The results obtained confirmed that it is possible to successfully apply these methods to the classification of construction mortars and then to propose mortars with such a composition that will guarantee that the composite will have the expected properties. Both the presented method of plastic waste management and the proposed statistical approach are in line with the assumptions of the currently important concept of sustainable development in construction.
format Online
Article
Text
id pubmed-9694445
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-96944452022-11-26 Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate Dębska, Bernardeta Materials (Basel) Article The article contains the results of selected tests of physical and mechanical properties of mortars differentiated in terms of the binder used: cement, epoxy, epoxy modified with PET waste glycolysate and polyester. Each type of mortar was modified by partial (0–20% vol.) substitution of sand with an agglomerate made from waste polyethylene. The obtained results were used to build a database of mortar properties, which was then analyzed with the use of three different techniques of knowledge extraction from databases, i.e., cluster analysis, decision trees and discriminant analysis. The average results of the properties tested were compared, taking into account the type of mortar, indicating those with the most favorable parameters. The possibilities and correctness of mortar classification with the use of the indicated “data mining” methods were compared. The results obtained confirmed that it is possible to successfully apply these methods to the classification of construction mortars and then to propose mortars with such a composition that will guarantee that the composite will have the expected properties. Both the presented method of plastic waste management and the proposed statistical approach are in line with the assumptions of the currently important concept of sustainable development in construction. MDPI 2022-11-16 /pmc/articles/PMC9694445/ /pubmed/36431597 http://dx.doi.org/10.3390/ma15228111 Text en © 2022 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dębska, Bernardeta
Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate
title Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate
title_full Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate
title_fullStr Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate
title_full_unstemmed Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate
title_short Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate
title_sort assessment of the applicability of selected data mining techniques for the classification of mortars containing recycled aggregate
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694445/
https://www.ncbi.nlm.nih.gov/pubmed/36431597
http://dx.doi.org/10.3390/ma15228111
work_keys_str_mv AT debskabernardeta assessmentoftheapplicabilityofselecteddataminingtechniquesfortheclassificationofmortarscontainingrecycledaggregate