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

Abstract

The saturated hydraulic conductivity, ksat, is a crucial variable to describe the hydromechanical behavior of soils. The value of ksat 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 ksat 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 ksat 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 ksat 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 ksat.