This paper presents a confidence ellipse-based method to evaluate the similarity of soil parametric data using the database from the site investigation reports. Then, the obtained similarity assessment results of parametric data are used to further estimate the site similarity via two proposed strategies, namely the mean and weighted mean approaches. The former referred to the average of parametric data similarity degrees, while the latter was the weighted average, and the weight was calculated using the coefficient of variation (COV) of each parameter. For illustration, the liquidity index (LI) dataset was firstly used to explore the performance of the presented method in the evaluation of parametric data similarity. Subsequently, the site similarity was assessed and the effects of numbers and weights of selected parameters for study were systematically studied. Lastly, the transformation models about the relationships between C_c and ω as well as between C_c and e₀ were constructed to illustrate the application of the similarity analysis in reduction of transformation uncertainty. Results show that the greatest site similarity degree is at about 0.76 in this study, and the maximum decrease of transformation uncertainty can reach up to 18% and 25.5% as union parametric data similarity degree increases. Moreover, the site similarity degree represents the whole similarity between two different sites, and the presented union parameter similarity degree maintains a good agreement with transformation uncertainty.

本文提出了一种基于置信椭圆的方法，利用现场调查报告中的数据库来评估土壤参数数据的相似性。然后，利用获得的参数数据的相似性评估结果，通过两种建议的策略，即均值和加权均值方法，进一步估计站点的相似性。前者是指参数数据相似度的平均值，后者是加权平均值，权重采用变异系数（COV）计算) 的每个参数。例如，首先使用流动性指数 (LI) 数据集来探索所提出方法在参数数据相似性评估中的性能。随后，对站点相似性进行了评估，并系统地研究了所选参数的数量和权重的影响。最后，关于C _c和ω之间以及C _c和e ₀之间关系的变换模型被构造来说明相似性分析在减少变换不确定性中的应用。结果表明，本研究的最大站点相似度约为0.76，随着联合参数数据相似度的增加，变换不确定性的最大降低幅度可达18%和25.5%。此外，站点相似度代表了两个不同站点之间的整体相似度，所呈现的联合参数相似度与转换不确定性保持良好的一致性。