Clustering of Fine-Grained Tropical Soils using Data Science Tools Applied to their Geotechnical Properties

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Mayssa Alves Da Silva Sousa
Mayssa Alves Da Silva Sousa
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Roberto Quental Coutinho
Roberto Quental Coutinho
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Laura Maria Goretti Da Motta
Laura Maria Goretti Da Motta
α Universidade Estadual do Maranhão

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Clustering of Fine-Grained Tropical Soils using Data Science Tools Applied to their Geotechnical Properties

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Abstract

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.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Mayssa Alves Da Silva Sousa. 2026. \u201cClustering of Fine-Grained Tropical Soils using Data Science Tools Applied to their Geotechnical Properties\u201d. Global Journal of Human-Social Science - B: Geography, Environmental Science & Disaster Management GJHSS-B Volume 22 (GJHSS Volume 22 Issue B3): .

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Detailed analysis of tropical soil clustering and data science applications in environmental studies.
Journal Specifications

Crossref Journal DOI 10.17406/GJHSS

Print ISSN 0975-587X

e-ISSN 2249-460X

Keywords
Classification
GJHSS-B Classification: DDC Code: 418.007 LCC Code: P53
Version of record

v1.2

Issue date

September 13, 2022

Language
en
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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.

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Clustering of Fine-Grained Tropical Soils using Data Science Tools Applied to their Geotechnical Properties

Mayssa Alves Da Silva Sousa
Mayssa Alves Da Silva Sousa Universidade Estadual do Maranhão
Roberto Quental Coutinho
Roberto Quental Coutinho
Laura Maria Goretti Da Motta
Laura Maria Goretti Da Motta

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