The application of machine learning (ML) methods has proven to be promising in dealing with a wide range of geotechnical engineering problems in recent years. ML methods have already been used for the prediction of soil water retention curves (SWRC) and estimation of air-entry values (AEV). However, the reported works in the literature are generally based on limited data and conventional, less accurate approaches for AEV estimation. In this paper, a large database, known as UNsaturated SOil hydraulic DAtabase (UNSODA), is studied and the conventional and true AEVs of 790 soil samples are estimated based on determination methods reported in the literature. A ML approach is then employed for the development of a predictive model for the estimation of true AEV from water content-based SWRCs of a wide range of soil types taking into account the impact of bulk density and grain size distribution parameters. The obtained results reveal an enhanced accuracy in AEV determination, featuring R² values of 0.964, 0.901 and 0.851 for training, validation, and testing data, respectively, which confirm the marked performance of the developed ML model. Based on the results of a sensitivity analysis, the particle sizes of 50 and 250 µm are found to have the highest impact on the AEV estimation.

近年来，机器学习（ML）方法的应用已被证明在处理广泛的岩土工程问题方面很有前景。ML 方法已被用于预测土壤保水曲线 (SWRC) 和估算空气进入值 (AEV)。然而，文献中报道的工作通常基于有限的数据和传统的、不太准确的 AEV 估计方法。本文研究了一个名为不饱和土壤水力数据库 (UNSODA) 的大型数据库，并根据文献中报道的测定方法估计了 790 个土壤样品的常规和真实 AEV。然后采用 ML 方法开发预测模型，用于从各种土壤类型的基于含水量的 SWRC 中估计真实 AEV，同时考虑容重和粒度分布参数的影响。获得的结果表明 AEV 确定的准确性更高，具有 R^{训练、验证和测试数据的2 个}值分别为 0.964、0.901 和 0.851，这证实了开发的 ML 模型的显着性能。根据敏感性分析的结果，发现 50 和 250 µm 的粒径对 AEV 估计的影响最大。