基于粒径分布和薄膜水的土水特征曲线模型

doi:10.11988/ckyyb.20240089

raybet体育在线院报 ›› 2025, Vol. 42 ›› Issue (5): 200-207.DOI: 10.11988/ckyyb.20240089

基于粒径分布和薄膜水的土水特征曲线模型

朱世旺¹^,²(), 李双洋¹(), 姜琪¹^,², 赵建沅¹^,², 周尚琪¹^,², 刘慧颖¹^,²

¹ 中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程全国重点实验室,兰州 730000
² 中国科学院大学,北京 100049

收稿日期:2024-01-24 修回日期:2024-03-07 出版日期:2025-05-01 发布日期:2025-05-01
通信作者:
李双洋(1980-),男,甘肃静宁人,研究员,博士,主要从事冻土力学与寒区岩土工程研究。E-mail:lisy@lzb.ac.cn
作者简介:
朱世旺(1998-),男,安徽蚌埠人,硕士研究生,主要从事非饱和土水力特性相关研究。E-mail:zhushiwang@nieer.ac.cn
基金资助:
中国科学院“西部之光-西部交叉团队”重点实验室专项(xbzg-zdsys-202216); 中国科学院青年创新促进会优秀会员项目(Y201975); 甘肃省科技揭榜挂帅制项目-技术攻关类资助项目(21ZD8JA003)

Soil-Water Characteristic Curve Model Based on Particle Size Distribution and Pellicular Water

ZHU Shi-wang¹^,²(), LI Shuang-yang¹(), JIANG Qi¹^,², ZHAO Jian-yuan¹^,², ZHOU Shang-qi¹^,², LIU Hui-ying¹^,²

¹ State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
² University of Chinese Academy of Sciences, Beijing 100049, China

Received:2024-01-24 Revised:2024-03-07 Published:2025-05-01 Online:2025-05-01

摘要/Abstract

摘要：

利用粒径分布曲线预测土水特征曲线(SWCC)的传统模型在计算含水量时通常需要将粒径分布曲线分段处理,且往往忽略了土壤颗粒表面的薄膜水含量,导致预测精度受到很大限制。为了解决以上问题,基于颗粒点对点接触假设,使用威布尔函数描述粒径分布曲线,通过杨拉普拉斯方程确定毛细水含水量,并考虑薄膜水含量的影响,建立一个基于粒径分布和薄膜水的土水特征曲线模型。然后选取非饱和土壤数据库(UNSODA)中26个土壤样品进行验证,并与AP模型和MV模型进行对比分析。结果表明,相较于AP模型和MV模型,新模型能更准确地预测土壤的土水特征曲线,且由于考虑了薄膜水含量,使新模型在高基质吸力段的含水量预测误差显著减小。

关键词: 土水特征曲线, 粒径分布曲线, 毛细水, 薄膜水, 含水量预测

Abstract:

Traditional models for predicting soil-water characteristic curve (SWCC) based on particle size distribution curve require segmentation of the curve when calculating water content and often neglect the pellicular water (also known as film water) content on the surface of soil particles. This significantly limits the prediction accuracy. To address these issues, this study assumes point-to-point contact between particles, employs the Weibull function to characterize the particle size distribution curve, determines the capillary water content using the Young - Laplace equation, and accounts for the influence of film water content. On this basis, a new SWCC model considering particle size distribution and film water is developed. Twenty-six soil samples from the Unsaturated Soil Database (UNSODA) are selected for validation and comparison with the AP and MV models. The results indicate that the proposed model based on particle size distribution and film water can predict the SWCC more accurately and significantly reduces the prediction error of water content in the high-matrix-suction section.

Key words: soil-water characteristic curve, particle size distribution curve, capillary water, film water, water content prediction

中图分类号:

TU43土力学

朱世旺, 李双洋, 姜琪, 赵建沅, 周尚琪, 刘慧颖. 基于粒径分布和薄膜水的土水特征曲线模型[J]. raybet体育在线院报, 2025, 42(5): 200-207.

ZHU Shi-wang, LI Shuang-yang, JIANG Qi, ZHAO Jian-yuan, ZHOU Shang-qi, LIU Hui-ying. Soil-Water Characteristic Curve Model Based on Particle Size Distribution and Pellicular Water[J]. Journal of Changjiang River Scientific Research Institute, 2025, 42(5): 200-207.

图/表 12

图1 土颗粒点对点接触示意图

Fig.1 Schematic diagram of particle point-to-point contact

表1 土壤类型和编号

Table 1 Soil types and numbering

土壤类型	UNSODA中土壤编号
砂土	1460,1462,1464,1466,3340,4060
砂质壤土	1010,1015,4580
壤土	1211,1260,1261,2530,2 531,3302,4790
黏质壤土	1123,2743,3031,3033
黏土	1400,2340,2681,4120,4680,4681

图2 不同类型土壤3种模型预测与实测数据对比

Fig.2 Comparison of predicted and measured data for different soil types using three models

图3 实测含水量和预测含水量1∶1对照图

Fig.3 Comparison chart with the same scale between measured and predicted water content

表2 3种模型预测土水特征曲线均方根误差统计

Table 2 RMSE statistics for predicting SWCC with three models

土壤类型	均方根误差
土壤类型	AP模型	MV模型	新模型
砂土	0.068 6	0.033 9	0.012 8
砂质壤土	0.068 2	0.052 6	0.024 9
壤土	0.141 9	0.083 0	0.016 5
黏质壤土	0.048 9	0.048 0	0.026 2
黏土	0.082 8	0.048 8	0.010 2

图4 3种模型均方根误差比较

Fig.4 Comparison of RMSE among three models

图5 MV模型和Hamamoto模型与新模型对比

Fig.5 Comparison of MV model and Hamamoto model with the proposed model

表3 3种模型的均方根误差统计

Table 3 RMSE statistics for three models

土壤样品编号	RMSE
土壤样品编号	MV模型	Hamamoto模型	新模型
砂土3340	0.042 1	0.029 5	0.012 5
黏土4680	0.055 8	0.033 4	0.009 9

图6 不同a时的粒径分布曲线和土水特征曲线(b=2)

Fig.6 Particle size distribution curve and SWCC at different a(b=2)

图7 不同b时的粒径分布曲线和土水特征曲线(a=1×10-4)

Fig.7 Particle size distribution curve and SWCC at different b(a=1×10-4)

图8 使用威布尔函数和分段方法的新模型预测效果对比

Fig.8 Comparison of the predictive performance of the proposed model using Weibull function and segmentation method

图9 砂土吸附水和毛细水贡献

Fig.9 Contributions of adsorbed water and capillary water in sandy soil

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基于粒径分布和薄膜水的土水特征曲线模型

Soil-Water Characteristic Curve Model Based on Particle Size Distribution and Pellicular Water

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