Machine learning (ML) holds significant potential for predicting soil properties in geotechnical design but at the same time poses challenges, including those of how to easily examine the performance of an algorithm and how to select an optimal algorithm. This study first comprehensively reviewed the application of ML algorithms in modelling soil properties for geotechnical design. The algorithms were categorized into several groups based on their principles, and the main characteristics of these ML algorithms were summarized. After that six representative algorithms are further detailed and selected for the creation of a ML-based tool with which to easily build ML-based models. Interestingly, automatic determination of the optimal configurations of ML algorithms is developed, with an evaluation of model accuracy, application of the developed ML model to the new data and investigation of relationships between the input variables and soil properties. Furthermore, a novel ranking index is proposed for the model comparison and selection, which evaluates a ML-based model from five aspects. Soil maximum dry density is selected as an example to allow examination of the performance of different ML algorithms, the applicability of the tool and the model ranking index to determining an optimal model.

机器学习 (ML) 在预测岩土工程设计中的土壤特性方面具有巨大潜力，但同时也带来了挑战，包括如何轻松检查算法的性能以及如何选择最佳算法。本研究首先全面回顾了 ML 算法在岩土工程设计土壤特性建模中的应用。这些算法根据其原理分为几组，并总结了这些 ML 算法的主要特征。之后，进一步详述并选择了六种代表性算法，以创建基于 ML 的工具，以便轻松构建基于 ML 的模型。有趣的是，开发了 ML 算法最佳配置的自动确定，并评估了模型的准确性，将开发的 ML 模型应用于新数据并调查输入变量与土壤特性之间的关系。此外，提出了一种新的排序指标用于模型比较和选择，从五个方面评估基于 ML 的模型。选择土壤最大干密度作为示例，以检查不同 ML 算法的性能、工具的适用性和模型排名指数，以确定最佳模型。