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Statistical evaluation of testing conditions on the saturated hydraulic conductivity of Brazilian lateritic soils using artificial intelligence approaches

The saturated hydraulic conductivity, k sat , is a crucial variable to describe the hydromechanical behavior of soils. The value of k sat of lateritic soils that are typically found in tropical regions is highly affected by the soil’s structure, void ratio, and fine particle aggregation. As a result...

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Published in:Scientific reports 2022-11, Vol.12 (1), p.20381-20381, Article 20381
Main Authors: dos Ramos Souza, Weber Anselmo, dos Santos Pereira, Sávio Aparecido, Mendes, Thiago Augusto, Costa, Rafaella Fonseca, de Farias Neves Gitirana Junior, Gilson, Rebolledo, Juan Félix Rodríguez
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Language:English
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Summary:The saturated hydraulic conductivity, k sat , is a crucial variable to describe the hydromechanical behavior of soils. The value of k sat of lateritic soils that are typically found in tropical regions is highly affected by the soil’s structure, void ratio, and fine particle aggregation. As a result, the determination of k sat in the field or in the laboratory is complex and involves greater variability, depending on the type of test and on the spatial location of sampling. This paper presents a study of k sat values of lateritic soils, analyzing them using Statistic, Multilayer Perceptron Artificial Neural Networks (ANN) and Decision Trees (CHAID). This study aims to support decision-making regarding the type of test and depth chosen for sampling in laterite soils and understanding the factors influencing the permeability of such soils. An extensive literature review on the k sat values of lateritic soils was performed, providing data for the establishment of a database comprise of 722 registries. According to agronomic and geotechnical soil classifications, the Brazilian lateritic soils presents a “moderate” hydraulic conductivity. A significant variation of permeability values along the depth was identified, particularly for depths between 0.1 and 0.2 m. Regarding the importance of testing variables, the ANN indicated a high dependency on the type of test. The decision tree divided field test and laboratory test automatically, inferring the relevance of the type of test to the determination of k sat .
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-24779-1