Much attention has been paid on the soil-water characteristic curve (SWCC) during decades because it plays great roles in unsaturated soil mechanics. However, it is time-consuming and costly to obtain a series of entire saturation-suction data by experiments. The curves acquired by directly fitting empirical equations to limited experimental data are greatly different from the actual SWCC, and the relevant soil parameters obtained by inaccurate curve are also incorrect. Thus, an improved prediction method for more accurate entire SWCC was established. This novel method was based on the analysis of shape characteristics of SWCC with three critical points , , and under the hypothesis of geometrical symmetric relation. The theoretical computation was specifically deduced under conventional Gardner, VG, and FX models, respectively, and then inferred on different soil types of 45 collected SWCC datasets. This geometrical symmetric relation exhibited well in all these three conventional empirical equations, especially in Gardner equation. Finally, a series of filer paper tests on sand, silt, and clay were also carried out to acquire entire SWCC curve for the verification and evaluation of the proposed geometrical method. Results show that this improved prediction method effectively decreases deviation resulting from directly fitting empirical equations to limited data of wide types of soils. The averaged improvement was larger under VG equation than under Gardner and FX equation. It proved that the accuracy of predicting greatly depends on the shape characteristic point of maximum curve curvature (point ), other than the number of points. This research provides a novel computation method to improve prediction accuracy even under relative less experimental data.

中文翻译：

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一种改进的基于几何推导和经验方程的水土特征曲线预测方法

几十年来，土水特征曲线（SWCC）在非饱和土力学中发挥着重要作用，因此备受关注。然而，通过实验获得一系列完整的饱和吸力数据既费时又费钱。直接将经验方程拟合到有限的实验数据得到的曲线与实际的SWCC有很大差异，不准确的曲线得到的相关土体参数也是不正确的。因此，建立了一种更准确的整个 SWCC 的改进预测方法。这种新方法是基于对 SWCC 的三个关键点的形状特征的分析， ,和在几何对称关系的假设下。理论计算分别在常规 Gardner、VG 和 FX 模型下专门推导，然后对 45 个收集的 SWCC 数据集的不同土壤类型进行推断。这种几何对称关系在所有这三个常规经验方程中都表现得很好，尤其是在 Gardner 方程中。最后，还对沙子、粉土和粘土进行了一系列过滤纸测试，以获得完整的 SWCC 曲线，以验证和评估所提出的几何方法。结果表明，这种改进的预测方法有效地减少了直接将经验方程拟合到各种土壤的有限数据而导致的偏差。VG 方程下的平均改进大于 Gardner 和 FX 方程下的改进。)，点数除外。这项研究提供了一种新的计算方法，即使在相对较少的实验数据下也能提高预测精度。