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<journal-meta>
<journal-id journal-id-type="publisher">global-journal-of-human-social-science-b-geography-environmental-science-disaster-management</journal-id>
<journal-title-group>
<journal-title>Global Journal of Human-Social Science - B: Geography, Environmental Science &amp; Disaster Management</journal-title>
</journal-title-group>
<issn publication-format="print">0975-587X</issn>
<issn publication-format="electronic">2249-460X</issn>
<publisher><publisher-name>Global Journals Publishing Group Incorporated</publisher-name></publisher>
<self-uri xlink:href="https://globaljournals.org/journal-seo-export/jats/61464.xml" />
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<article-meta>
<article-id pub-id-type="publisher-id">61464</article-id>
<title-group>
<article-title>Clustering of Fine-Grained Tropical Soils using Data Science Tools Applied to their Geotechnical Properties</article-title>
<subtitle>Data Science for Tropical Soil Cluster Analysis</subtitle>
</title-group>
<contrib-group>
<contrib contrib-type="author"><name><surname>Sousa</surname><given-names>Mayssa Alves Da Silva</given-names></name><xref ref-type="aff" rid="aff1" />
</contrib>
<contrib contrib-type="author"><name><surname>Coutinho</surname><given-names>Roberto Quental</given-names></name></contrib>
<contrib contrib-type="author"><name><surname>Motta</surname><given-names>Laura Maria Goretti Da</given-names></name></contrib>
</contrib-group>
<aff id="aff1">BRAZIL, State University of MaranhÃ£o,</aff>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2022-09-13">
<day>13</day>
<month>09</month>
<year>2022</year>
</pub-date>
<volume>22</volume>
<issue>B3</issue>
<fpage>9</fpage>
<lpage>25</lpage>
<abstract><p>The characterization of fine-grained tropical soils for use in pavements has evolved since the 1980s, however, even today these soils are still discarded or underused in infrastructure works because they do not fully meet the requirements established by traditional classification methodologies or even by the CBR. Tropical soils present peculiarities of geotechnical behavior regarding elastic and plastic deformability, as many authors have already observed. This article contributes to this distinction by analyzing the grouping of thirteen fine-grained soils from northeastern Brazil through the application of data science tools to the results of geotechnical tests. More than fifty geotechnical parameters obtained in the laboratory were considered. By means of simple and multiple linear regressions, they were analyzed in a hierarchical cluster, using Ward’s linkage method and Euclidean distance. The results showed that the mechanical behavior of soil compaction and the granulometry, especially the quantities of silt and fine sand, were decisive for the initial division of soils into clusters.</p></abstract>
<kwd-group kwd-group-type="author-generated">
<kwd>tropical soils. geotechnical properties. geotechnical behavior. cluster analysis. data science.</kwd>
</kwd-group>
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<title>Full Text</title>
<p>The characterization of fine-grained tropical soils for use in pavements has evolved since the 1980s, however, even today these soils are still discarded or underused in infrastructure works because they do not fully meet the requirements established by traditional classification methodologies or even by the CBR. Tropical soils present peculiarities of geotechnical behavior regarding elastic and plastic deformability, as many authors have already observed. This article contributes to this distinction by analyzing the grouping of thirteen fine-grained soils from northeastern Brazil through the application of data science tools to the results of geotechnical tests. More than fifty geotechnical parameters obtained in the laboratory were considered. By means of simple and multiple linear regressions, they were analyzed in a hierarchical cluster, using Ward&#039;s linkage method and Euclidean distance. The results showed that the mechanical behavior of soil compaction and the granulometry, especially the quantities of silt and fine sand, were decisive for the initial division of soils into clusters.</p>
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