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.

这项研究着重于提出基于分类器的新模型，该模型可应用于各种问题。Adaboost是一种集成学习机，它使用包含一系列基本模型的分类器。这项研究使用了增强型Adaboost模型，根据该地区较不常用的树算法模型对土壤类型进行了分类。确定不同岩土工程中的土壤类型非常重要。使用土壤分类，可以发现土壤性质，例如机械性质，抵抗静态和动态载荷的性能。关于主题的重要性，使用了440个实际项目的样本来设计这种新方法。数据集包括粘土含量，水分含量，比重，空隙率，塑料和液体极限参数，以确定土壤分类的类型。对这些样品进行了高精度测试，并获得了实际的分类类型。为了进行比较，根据这些条件设计并开发了两种增强树和神经网络模型。给出了针对不同土壤样品的分类结果。发达的adaboost模型表明它可以很好地对土壤进行分类。该模型显示，在全部数据（88个数据）中，只有11个样本未正确识别。因此，可以使用这种新技术来提高准确性并降低项目成本。发达的adaboost模型表明它可以很好地对土壤进行分类。该模型显示，在全部数据（88个数据）中，只有11个样本未正确识别。因此，可以使用这种新技术来提高准确性并降低项目成本。发达的adaboost模型表明它可以很好地对土壤进行分类。该模型显示，在全部数据（88个数据）中，只有11个样本未正确识别。因此，可以使用这种新技术来提高准确性并降低项目成本。