A unimodal soil-water characteristic curve model for gap-graded soil based on bimodal grain-size distribution and fractal theory
Year: 2024
Type: article
Abstract: Soil-water characteristic curve (SWCC) is an important hydraulic property that describes the relationship between matric suction and water content in unsaturated soils. However, existing SWCC models for gap-graded soil are highly empirical and fail to account for the influence of soil physical properties on SWCC. SWCC models for continuous-graded soil don’t consider the packing between particles, which results in predictions that are inconsistent with measured data. To solve the above limitation, this study proposed a new pore capillary model considering the packing of coarse and fine-grained soils and established a unimodal SWCC model called GU (Gap-graded soil and Unimodal SWCC) for gap-graded soil based on bimodal grain-size distribution (GSD) and fractal theory. There were only two physical fitting parameters in the GU model, pore fractal dimension and maximum adsorbed water content. The new proposed GU model was verified by published measured data and compared with SAP model, a representative SWCC theoretical model for continuous-graded soil. The verification and comparison prove that SWCCs predicted by the GU model have fewer error and a more reasonable variation trend than SWCCs predicted by the SAP model for different types of gap-graded soils. Thus, this study provides a novel approach to establish the relationship between soil properties and SWCC for gap-graded soil based on soil water retention mechanisms, which is important for subsequent studies of hydraulic properties.
Source: Transportation Geotechnics
Authors: Yuefeng Wu, Junsheng Chen, Weidong Pan, Lingfeng Guo, Yi Shan
Institutions: South China University of Technology, Guangzhou University
Language: English
Topic: Soil and Unsaturated Flow
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,900
OpenAlex ID: W4405220212