A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling

Abstract

Abstract This research focuses on presenting new models based on classifiers that can be applied to various problems. Adaboost is a type of ensemble learning machine that uses classifiers that contain a range of base models. This study used enhanced Adaboost models to classify soil types base on tree algorithm models that are less commonly used in this area. Determining the type of soil in different geotechnical projects is very important. Using soil classification, soil properties such as mechanical properties, performance against static and dynamic loads can be found. Regarding the importance of the subject, 440 samples of the actual project were used to design this new methodology. The dataset included clay content, moisture content, specific gravity, void ratio, plastic, and liquid limit parameters to determine the type of soil classification. These samples were tested with high precision and the actual type of classification was obtained. For comparison, two enhanced tree and neural network model were designed and developed according to these conditions. The results of this classification were presented for different soil samples. The developed adaboost model showed that it could well classify the soil. This model showed that only 11 samples were not correctly identified among the total data (88 data). Therefore, this new technique can be used to increase the accuracy and reduce the cost of projects.