Precompression stress, compression index, and swelling index are used for characterizing the compressive behavior of soils, and are essential soil properties for establishing decision support tools to reduce the risk of soil compaction. Because measurements are time-consuming, soil compressive properties are often derived through pedotransfer functions. This study aimed to develop a comprehensive database of soil compressive properties with additional information on basic soil properties, site characteristics, and methodological aspects sourced from peer-reviewed literature, and to develop random forest models for predicting precompression stress using various subsets of the database. Our analysis illustrates that soil compressive properties data primarily originate from a limited number of countries. There is a predominance of precompression stress data, while little data on compression index or recompression index are available. Most precompression stress data were derived from the topsoils of conventionally tilled arable fields, which is not compatible with knowledge that subsoil compaction is a serious problem. The data compilation unveiled considerable variations in soil compression test procedures and methods for calculating precompression stress across different studies, and a concentration of data at soil moisture conditions at or above field capacity. The random forest models exhibited unsatisfactory predictive performance although they performed better than previously developed models. Models showed slight improvement in predictive power when the underlying data were restricted to a specific precompression stress calculation method. Although our database offers broader coverage of precompression stress data than previous studies, the lack of standardization in methodological procedures complicates the development of predictive models based on combined datasets. Methodological standardization and/or functions to translate results between methodologies are needed to ensure consistency and enable data comparison, to develop robust models for precompression stress predictions. Moreover, data across a wider range of soil moisture conditions are needed to characterize soil mechanical properties as a function of soil moisture, similar to soil hydraulic functions, and to develop models to predict the parameters of such soil mechanical functions.

中文翻译：

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预测土壤预压应力的当前局限性和未来研究需求：现有数据的综合


预压应力、压缩指数和膨胀指数用于表征土壤的压缩行为，是建立决策支持工具以降低土壤压实风险的重要土壤属性。由于测量非常耗时，因此土壤压缩特性通常通过pedotransfer 函数得出。本研究旨在开发一个土壤压缩特性的综合数据库，其中包含来自同行评审文献的基本土壤特性、场地特征和方法方面的附加信息，并开发随机森林模型，用于使用数据库的各个子集预测预压应力。我们的分析表明，土壤压缩特性数据主要来自少数国家。压缩前应力数据占主导地位，而压缩指数或再压缩指数数据很少。大多数预压应力数据来自传统耕作耕地的表土，这与地下土压实是一个严重问题的认识不相符。数据汇编揭示了不同研究中土壤压缩测试程序和计算预压缩应力的方法的巨大差异，以及土壤湿度条件下等于或高于田间容量的数据集中度。尽管随机森林模型比以前开发的模型表现更好，但其预测性能并不令人满意。当基础数据仅限于特定的预压应力计算方法时，模型的预测能力略有提高。 尽管我们的数据库比以前的研究提供了更广泛的预压缩应力数据覆盖范围，但方法程序标准化的缺乏使基于组合数据集的预测模型的开发变得复杂。需要方法标准化和/或在方法之间转换结果的功能，以确保一致性并实现数据比较，从而开发用于预压缩应力预测的稳健模型。此外，需要更广泛的土壤湿度条件的数据来将土壤力学特性描述为土壤湿度的函数（类似于土壤水力函数），并开发模型来预测此类土壤力学函数的参数。