The liquid limit is a key property of fine soils closely related to the stress-strain behaviour and other relevant characteristics of soils such as the expansive potential. There are two standardized methods for its determination, the Casagrande cup and the fall cone test, among which there are many correlations that offer heterogeneous results. In the present study, a compilation of 113 data from fine soil samples with low-medium plasticity has been carried out. Then, a comparative study of different machine learning algorithms was carried out to relate the liquid limit obtained from both methods, having in consideration other parameters such as the plastic limit and the percentages of passing through the 0.40 and 0.075 mm sieves. The result of this study has shown that extremely randomized trees algorithm provides the best performance. Consequently, the algorithm has been tuned to enhance the precision, obtaining a coefficient of determination (R²) value of 0.99. The results demonstrate the potential of machine learning techniques for relating liquid limit obtained by Casagrande's method and fall cone test in fine-grained soils with low-medium plasticity, mainly for values of the liquid limit higher than 30 for which classical linear regression approaches provide lower performance metrics.

液限是细土的一个关键特性，与土的应力应变行为和其他相关特性（如膨胀势）密切相关。有两种标准方法可用于测定，即 Casagrande 杯和落锥试验，其中有许多相关性可提供不同的结果。在本研究中，对 113 个中低可塑性细土样品的数据进行了汇编。然后，对不同的机器学习算法进行了比较研究，以关联从两种方法获得的液限，同时考虑了其他参数，例如塑限和通过 0.40 和 0.075 毫米筛子的百分比。这项研究的结果表明，极度随机化的树算法提供了最好的性能。² ) 0.99 的值。结果证明了机器学习技术在将 Casagrande 方法获得的液限与具有中低塑性的细粒土中的落锥试验相关联的潜力，主要是对于高于 30 的液限值，经典线性回归方法提供较低性能指标。