The soil freezing characteristic curve (SFCC) represents a vital parameter in cold regions. Special instruments and highly trained personnel must measure the SFCC experimentally, a process that is both cumbersome and labor-intensive. Consequently, several estimation models have emerged to indirectly gauge the SFCC, yet the effectiveness of these models has rarely been examined. This study scrutinized three SFCC estimation models and enhanced them by employing a one-point measurement method. A total of 65 fine-grained soils and 25 coarse-grained soils were used to evaluate six SFCC estimation models. Findings reveal that the one-point measurement method markedly enhances the efficiency of the SFCC estimation models. In addition, when compared with three traditional SFCC estimation models, the extended SFCC estimation models were assessed, revealing that the extended Xin et al. SFCC estimation model ranks highest in performance among the SFCC estimation models considered. The research contributes a novel approach to developing SFCC estimation models in cold regions.

土壤冻结特征曲线（SFCC）是寒冷地区的一个重要参数。必须使用特殊仪器和训练有素的人员对 SFCC 进行实验测量，这一过程既麻烦又费力。因此，出现了一些间接衡量 SFCC 的估计模型，但这些模型的有效性很少得到检验。本研究仔细研究了三种 SFCC 估计模型，并通过采用单点测量方法对其进行了增强。总共使用 65 种细粒土和 25 种粗粒土来评估 6 个 SFCC 估计模型。研究结果表明，单点测量方法显着提高了 SFCC 估计模型的效率。此外，与三种传统的SFCC估计模型相比，对扩展的SFCC估计模型进行了评估，结果表明扩展的Xin等人。在考虑的 SFCC 估计模型中，SFCC 估计模型的性能排名最高。该研究为寒冷地区开发SFCC估计模型提供了一种新方法。