Cheng Hui, Zhang Lingkai, Shi Chong, Fan Pei Pei
College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, 311 East Nongda Rd, Urumqi, 830052, China.
Sci Rep. 2025 Jan 28;15(1):3490. doi: 10.1038/s41598-025-87250-x.
Water conveyance channels in cold and arid regions pass through several saline-alkali soil areas. Canal water leakage exacerbates the salt expansion traits of such soil, damaging canal slope lining structures. To investigate the mechanical properties of saline clay, this study conducted indoor tests, including direct shear, compression, and permeation tests, and scanning electron microscopy (SEM) analysis of soil samples from typical sites. This study aims to elucidate the impact of various factors on the mechanical properties of saline clay from a macro-micro perspective and reveal its physical mechanisms. A prediction model is formulated and validated. The findings indicate the following: (1) Cohesion in direct shear tests has a linear negative correlation with water content and a positive correlation with dry density and initially decreases with increasing salt content until 2%, after which it increases. The internal friction angle initially increases and then decreases with increasing water content, reaching a peak at the optimal water content, and then gradually increases with dry density while initially decreasing, followed by an increase in salt content, stabilizing thereafter. Water content, dry density, or salt content chiefly affect cohesion by influencing electrostatic attraction, van der Waals forces, particle cementation, and valence bonds at particle contact points. (2) Compression tests reveal a linear positive correlation between the compression coefficient and water content, a negative correlation with dry density, and a stepwise linear correlation with salt content, peaking at 2%. The compression index decreases with increasing water content and dry density, following a trend similar to that of the compression coefficient with increasing salt content. The rebound index shows a linear negative correlation with water content and dry density, transitioning from a negative to a positive correlation at 2% salt content. Scanning electron microscopy analysis revealed particle flattening and increased aggregation with increasing consolidation pressure, reducing compressibility. Large pores and three-dimensional porosity have the greatest influence on soil compressibility. (3) Permeability tests reveal an exponential negative correlation between the permeability coefficient and dry density. As the dry density increases, the particle arrangement becomes denser, decreasing the pore quantity, with micropores disproportionately impacting the permeability coefficient. An increase in salinity initially increases the permeability coefficient before it decreases. The boundary point of the 2% salt content divides the effect of salt ions from promoting free water flow to blocking seepage channels, with the proportion of micropores being the primary influencing factor. (4) Employing statistical theory and machine learning algorithms, dry density, water content, and salinity are used to predict mechanical index values. The improved particle swarm optimization-support vector regression (PSO-SVR) model has high accuracy and general applicability. These findings offer insights for the construction and upkeep of open channel projects in arid regions.
寒冷干旱地区的输水渠道穿越多个盐碱土区域。渠道漏水加剧了此类土壤的盐胀特性,破坏了渠道边坡衬砌结构。为研究盐渍黏土的力学性质,本研究进行了室内试验,包括直剪、压缩和渗透试验,并对典型场地的土样进行了扫描电子显微镜(SEM)分析。本研究旨在从宏观-微观角度阐明各种因素对盐渍黏土力学性质的影响,并揭示其物理机制。建立并验证了一个预测模型。研究结果表明:(1)直剪试验中的黏聚力与含水量呈线性负相关,与干密度呈正相关,且最初随含盐量增加而降低,直至2%,之后增加。内摩擦角最初随含水量增加而增大,然后减小,在最优含水量时达到峰值,然后随干密度逐渐增大,同时最初随含盐量降低,随后增加,此后趋于稳定。含水量、干密度或含盐量主要通过影响颗粒接触点处的静电引力、范德华力、颗粒胶结和价键来影响黏聚力。(2)压缩试验表明,压缩系数与含水量呈线性正相关,与干密度呈负相关,与含盐量呈阶梯状线性相关,在2%时达到峰值。压缩指数随含水量和干密度增加而降低,其趋势与压缩系数随含盐量增加的趋势相似。回弹指数与含水量和干密度呈线性负相关,在含盐量为2%时从负相关转变为正相关。扫描电子显微镜分析表明,随着固结压力增加,颗粒扁平化且团聚增加,压缩性降低。大孔隙和三维孔隙率对土壤压缩性影响最大。(3)渗透试验表明,渗透系数与干密度呈指数负相关。随着干密度增加,颗粒排列变得更密实,孔隙数量减少,其中微孔对渗透系数影响尤为显著。盐度增加最初会使渗透系数增加,之后降低。含盐量2%的分界点将盐离子从促进自由水流动转变为阻塞渗流通道的作用区分开来,其中微孔比例是主要影响因素。(4)运用统计理论和机器学习算法,利用干密度、含水量和盐度来预测力学指标值。改进的粒子群优化-支持向量回归(PSO-SVR)模型具有较高的精度和普遍适用性。这些研究结果为干旱地区明渠工程的建设和维护提供了参考。