Loess is widely deposited in arid and semi-arid areas and is characterized by low dry density, developed pore space, and loose structure, which is not commensurate with high structural strength and shear strength in the dry state. Many natural phenomena and experimental results show that intact loess is very sensitive to the change of water content, and a slight increase in water content can cause a rapid reduction in strength. Abundant information is available in the literature for the collapsibility of loess. However, research on the evolution of loess compressibility during wetting, which is very helpful in understanding loess collapsible deformation caused by long-term irrigation, remains minimal. To this end, in this article, the evolution of the compressibility of intact loess at different wetting stages is investigated by oedometer tests, and microstructure and pore size distribution (PSD) were characterized on intact loess specimens at different water contents before and after oedometer tests by scanning electron microscope (SEM) and mercury intrusion porosimetry (MIP) methods. The results show that the compression index (C_c) and secondary compression index (C_α) of intact loess depend on water content and vertical stress and change abruptly when vertical stress exceeds yield stress. C_α/C_c values of intact loess are not constant, which increases with vertical stress to a peak and then gradually decreases to 0.025. Wetting and loading can cause damage to the microstructure of intact loess; specifically, loading leads to the collapse of the overhead structure and the transformation from a bimodal PSD into a unimodal PSD, and wetting intensifies the collapse of the microstructure to form a compacted interlocking structure and promotes the transformation of medium pores into small pores.

The present work determined the influence of superhydrophobic nano-SiO₂ on the hydraulic conductivity and pore size distribution of expansive soil, and analysed the mechanism of modification between superhydrophobic nano-SiO₂ and expansive soil from a microscopic view. Superhydrophobic nano-SiO₂ was added to expansive soil as a modifier. Our samples were of two types, i.e., unmodified (without nano-SiO₂) and modified (with 0.2%, 0.4%, 0.6%, 0.8%, and 1.0% nano-SiO₂ by weight of the parent soil). The hydraulic conductivity decreased with increasing nano-SiO₂ content. Fourier transform mid-infrared test revealed that some silanols in soil and nano-SiO₂ were dehydrated and condensed to form siloxanes. We inferred that nano-SiO₂ can attach onto the surface of soil particles to form a hydrophobic membrane, which reduced the soil expansion and the change in pore size distribution. And microscopic tests showed that the pore volume and hydrophilicity of the soil samples decreased with increasing SiO₂ content. According to the Young–Laplace equation, the minimum permeable pore radius was calculated in the hydraulic-conductivity test. With increasing nano-SiO₂ content, the volume of permeable pore decreased. It had an excellent linear relationship with the hydraulic conductivity and permeable pore volume of samples containing different nano-SiO₂ contents. Therefore, superhydrophobic nano-SiO₂ could effectively reduce hydraulic conductivity by changing the pore size distribution of expansive soil.

To describe and measure the initiation and development of desiccation cracks in lateritic soil and to discuss the relationship between crack ratio and resistivity, three parallel specimens are prepared and dried under different temperatures and humidity. The geoelectrical resistivity technique combined with image processing method is applied. Water content, surface crack ratio, and electrical resistivity are monitored during the drying path. Results indicate that the calculated surface crack ratios can be identified on the basis of five distinct stages. The occurrence of the first crack in three specimens indicates that the first cracking time occurs earlier with increasing temperature and decreasing relative humidity. The time that crack ratios stabilize indicates that environmental parameters significantly influence crack evolution. The temporal characteristics of resistivity can also be identified on the basis of five distinct stages. The resistivity and crack ratio can be effectively described by a mathematical equation, which is considered as the basis for the use of the geoelectrical technique for the assessment of the temporal variability of soil desiccation cracking.

Field tests were carried out on the compacted lateritic soil subgrade of Laibin‐Mashan expressway in Guangxi Province to obtain the vertical vibration acceleration and dynamic stress amplitude of each test point under different axle loads and different driving speeds. The distribution law of the dynamic response and its frequency domain characteristics obtained by wavelet analysis emerged. The vibration of the subgrade is clearly aggravated by the increase of speed and load. Specifically, the acceleration of vehicle speed from 20 km/h to 40 km/h has a prominent effect on the vibration of subgrade, and the influence of speed on the vibration of subgrade decreases with subgrade depth. The acceleration has the greatest impact on the vibration energy in the third and fourth frequency bands.

Purpose: The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.Design/methodology/approach: Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.Findings: The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.Originality/value: By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.