Global Journal of Human-Social Science, B: Geography, Environmental Science and Disaster Management, Volume 22 Issue 3

© 2022. Mayssa Alves Da Silva Sousa, Roberto Quental Coutinho & Laura Maria Goretti Da Motta. This research/review article is distributed under the terms of the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). You must give appropriate credit to authors and reference this article if parts of the article are reproduced in any manner. Applicable licensing terms are at https://creativecommons.org/licenses/by-nc-nd/4.0/. Clustering of Fine-Grained Tropical Soils using Data Science Tools Applied to their Geotechnical Properties By Mayssa Alves Da Silva Sousa, Roberto Quental Coutinho & Laura Maria Goretti Da Motta State University of Maranhão 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. Keywords: tropical soils. geotechnical properties. geotechnical behavior. cluster analysis. data science. GJHSS-B Classification: DDC Code: 418.007 LCC Code: P53 ClusteringofFineGrainedTropicalSoilsusingDataScienceToolsAppliedtotheirGeotechnicalProperties Global Journal of HUMAN-SOCIAL SCIENCE: B Geography, Geo-Sciences, Environmental Science & Disaster Management Volume 22 Issue 3 Version 1.0 Year 2022 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Online ISSN: 2249-460x & Print ISSN: 0975-587X Strictly as per the compliance and regulations of: