The objective of this research was to investigate the relationship between soil moisture characteristic curve (SMCC) and confined compression curve (CCC), and the ability to estimate the SMCC from the CCC. Five provinces of Iran have been chosen as sampling sites and soil samples (150) were collected from these areas, and some properties of the soil samples were determined. The Gardner model has been fitted to the measured SMCC and CCC. The Gardner model's stress-void ratio coefficients, as well as the CCC properties, were used to estimate the soil moisture at five levels (five classes of variables) through the Gardner model using artificial neural networks (ANNs). A more accurate estimation of the water content was obtained by combining the basic soil properties, and three key properties of soil compression. In addition, the integral root mean square error (IRMSE) in the training and testing steps was reduced from 0.107 and 0.111 to 0.095 and 0.096, respectively. In conclusion, the use of CCC data to estimate the SMCC at all levels of the input variables indicated very favorable results with an increase in the accuracy of the water content estimation between 4% and 16% in both training and testing steps.

本研究的目的是调查土壤水分特征曲线 (SMCC) 和受限压缩曲线 (CCC) 之间的关系，以及从 CCC 估计 SMCC 的能力。选择伊朗的五个省份作为采样点，从这些地区采集了土壤样品（150 个），并确定了土壤样品的一些特性。Gardner 模型已拟合到测量的 SMCC 和 CCC。Gardner 模型的应力-空隙比系数以及 CCC 特性用于通过使用人工神经网络 (ANN) 的 Gardner 模型估计五个水平（五类变量）的土壤水分。通过结合基本土壤特性和土壤压缩的三个关键特性，可以获得更准确的含水量估计值。此外，训练和测试步骤中的积分均方根误差 (IRMSE) 分别从 0.107 和 0.111 减少到 0.095 和 0.096。总之，使用 CCC 数据来估计所有输入变量水平的 SMCC 表明非常有利的结果，在训练和测试步骤中，含水量估计的准确性提高了 4% 到 16%。