Experimental Study on Microstructure of Unsaturated Expansive Soil Improved by MICP Method

The soil water characteristic curve and microstructure evolution of unsaturated expansive soil improved by microorganisms in Nanning, Guangxi were studied by means of filter paper method and scanning electron microscope imaging (SEM). Based on Fredlung & Xing model, the influence law of different cement content on the soil water characteristic curve of improved expansive soil is proved. According to the analysis of SEM test results, the influence mechanism of MICP method on the engineering characteristics of improved expansive soil is revealed. The results show that with the increase of cement content, the saturated water content and residual water content of the improved expansive soil gradually increased. At the same time, the water stability gradually increased while the air inlet value gradually decreased. The improved expansive soil changes from the superposition of flat particles and flake particles to the contact between spherical particles and flake particles, which indicates that the aggregate increases significantly. With the increase of the content of cement solution, the contact between particles tends to be smooth and the soil pores gradually tend to be evenly distributed. The particle size and microstructure of soil particles was changed and the connection between particles was enhanced in the improved expansive soil. Eventually the strength and water stability of expansive soil were improved. The conclusions above not only provide a theoretical basis for the in-depth study of engineering characteristics of unsaturated expansive soil improved by MICP method, but also offer theoretical evidence for perfecting engineering technology of expansive soil improved by MICP method.

Soil water characteristic curve of a compacted A-7-5 tropical red earth soil

Soil water characteristic curve (SWCC) is a very important property of unsaturated soil and by extension tropical red earth soils. This is because several other important soils’ properties can be related to it. The Filter paper method was employed in the determination of the A-7-5(5) tropical red earth WCC. The gravimetric water content was utilized in the computation of the SWCC. Four models, Fredlund and Xing (1994), FX; Van Genuchten (1980), VG; Brooks and Corey (1964), BC; and Kosugi (1996), K were used to estimate the SWCCs of the soil and the minimum SSEnorm (MSSE), Average Relative Error (ARE), and R2 values were used to determine the most suitable model for predicting the SWCC. Results show that all four models can be used to predict A-7-5(5) WCC as they all had R2 value greater than 89% although BC and K models perform best with coefficient of determination of over 97%. MSSE and ARE% were also significantly low for BC and K models.

Study on Soil Water and Suction Stress Characteristics for Unsaturated Clay Soil of Airport Engineering Based on Laboratory Tests

For the unsaturated soil in Feidong China, this study examined the suction stress characteristics based on the soil-water characteristic curve (SWCC), which was different from traditional research ideas. At the same time, the unsaturated consolidation device was adopted for SWCC tests, with consideration of the influence of yielding stress of soil, which was different from the traditional test approach of the soil-water characteristic curve. The results were estimated using the van Genuchten model, which was revealed that this is well-fit for the studied unsaturated soil, and the triaxial shear-strength tests were conducted with suction control. Then, the suction stress characteristic curve (SSCC) was analyzed, and SWCC-predicted data were compared with triaxial test-derived suction stress data. For the studied unsaturated soil, the deviatoric stress increased with the net inner stress p − u a at the same matric suction. At the same net inner pressure, the deviatoric stress increased with the matric suction, which verified the hardening activity of matric suction on the tested unsaturated soil strength. Besides, triaxial test-derived suction stress data greatly conformed to SWCC data-derived SSCC that was determined using identical parameters used in the SWCC model.

An Improved Prediction Method of Soil-Water Characteristic Curve by Geometrical Derivation and Empirical Equation

Much attention has been paid on the soil-water characteristic curve (SWCC) during decades because it plays great roles in unsaturated soil mechanics. However, it is time-consuming and costly to obtain a series of entire saturation-suction data by experiments. The curves acquired by directly fitting empirical equations to limited experimental data are greatly different from the actual SWCC, and the relevant soil parameters obtained by inaccurate curve are also incorrect. Thus, an improved prediction method for more accurate entire SWCC was established. This novel method was based on the analysis of shape characteristics of SWCC with three critical points S , C 1 , and C 2 under the hypothesis of geometrical symmetric relation. The theoretical computation was specifically deduced under conventional Gardner, VG, and FX models, respectively, and then inferred on different soil types of 45 collected SWCC datasets. This geometrical symmetric relation exhibited well in all these three conventional empirical equations, especially in Gardner equation. Finally, a series of filer paper tests on sand, silt, and clay were also carried out to acquire entire SWCC curve for the verification and evaluation of the proposed geometrical method. Results show that this improved prediction method effectively decreases deviation resulting from directly fitting empirical equations to limited data of wide types of soils. The averaged improvement was larger under VG equation than under Gardner and FX equation. It proved that the accuracy of predicting greatly depends on the shape characteristic point of maximum curve curvature (point C 2 ), other than the number of points. This research provides a novel computation method to improve prediction accuracy even under relative less experimental data.