Vegetation coverage is an important indicator for evaluating regional environmental quality. Based on MODIS NDVI and DEM data collected for the upper reaches of the Ganjiang River Basin, China, this study used trend analysis, coefficient of variation, Hurst index, and linear regression to analyze the temporal and spatial evolution of vegetation coverage and its relationship with terrain factors in the basin during the years 2000–2020. The vegetation coverage in the study area showed a fluctuating increasing trend at a rate of 5%/10y, and an increasing trend with increasing elevation. The maximum vegetation coverage was identified in the elevation zone of 750–1,000 m, with an average of 83.54%. Vegetation coverage also showed an increasing trend with increasing slope. The maximum vegetation coverage was up to 82.22% in the slope zone of ≥25°. There were no significant differences among the distributions of vegetation coverage in different aspects because the terrain in the study area is not rugged enough to form barriers against sunlight. The vegetation coverage was relatively stable in the study area, with an average coefficient of variation of 14.8%. Hurst analysis showed that the anti-sustainability effect of vegetation change was stronger than that of sustainability, and weak anti-sustainability was dominant. The effects of human activities mainly concentrated in the areas of low elevation and small slopes less than 2°where cities and towns are located. The findings can provide a scientific basis for the management of regional ecosystems in the future.
Natural disasters such as debris flow caused by earthquakes seriously threaten the local infrastructure and economy, as well as the lives of people in the area. As the material source of debris flow, it has significance to accurately and effectively study the underground structure of the landslide to prevent debris flow disasters. A landslide has a complex structural system, and its underground characteristics play an important role in its stability. The early identification of fracture surfaces and unstable bodies, and assessment of potential hazards are essential for prevention and protection. The research object of this paper is a landslide that occurred in Yige Village, Xianshui Town, Daofu County, which is on the Xianshui River Earthquake Zone, an area subject to frequent earthquakes. In western Sichuan, the frequent occurrence of landslides has caused considerable economic losses. Developing methods for efficient and accurate risk assessment is a top priority. The Daofu landslide is a typical example of a landslide directly threatening the road below and forming a debris flow channel. The lithology is composed of Jurassic sedimentary rocks, such as marl and clay, covered by limestone. In this study, we combined traditional methods (drilling and field investigation) with two geophysical techniques, multichannel analysis of surface waves (MASW) and electrical resistivity tomography (ERT) to effectively determine the electrical characteristics, velocity characteristics and spatial structure of the landslide. It is found that the buried depth of the sliding surface of the landslide is about 16–20 m. The sliding body above the sliding surface forms a low velocity and low resistivity Quaternary cover. The rock mass below the sliding surface is Triassic Zhuwo Formation sandstone and slate with high velocity and high resistivity. According to comprehensive analysis, the landslide lacks sufficient stability under rainstorm. Our study shows that the use of MASW and ERT can quickly and effectively characterize the subsurface of landslides to assess landslide risk and prevent debris flow hazards.
Soil-rock mixtures are geological materials with complex physical and mechanical properties. Therefore, the stability prediction of soil-rock mixture slopes using machine learning methods is an important topic in the field of geological engineering. This study uses the soil-rock mixture slopes investigated in detail as the dataset. An intelligent optimization algorithm-weighted mean of vectors algorithm (INFO) is coupled with a machine learning algorithm. One of the new ensemble learning models, which named IN-Voting, is coupled with INFO and voting model. Twelve single machine learning models and sixteen novel IN-Voting ensemble learning models are built to predict the stability of soil-rock mixture slopes. Then, the prediction accuracies of the above models are compared and evaluated using three evaluation metrics: coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). Finally, an IN-Voting ensemble learning model based on five weak learners is used as the final model for predicting the stability of soil-rock mixture slopes. This model is also used to analyze the importance of the input parameters. The results show that: 1) Among 12 single machine learning models for the stability prediction of soil-rock mixture slopes, MLP (Multilayer Perceptron) has the highest prediction accuracy. 2) The IN-Voting model has higher prediction accuracy than single machine learning models, with an accuracy of up to 0.9846) The structural factors affecting the stability of soil-rock mixture slopes in decreasing order are the rock content, bedrock inclination, slope height, and slope angle.
In order to study the stability of the high and steep slope of an open-pit mine under deep bench blasting vibration, a mine in Inner Mongolia is taken as the engineering background, and the mechanical parameters of rock samples were determined based on uniaxial and triaxial instruments. The stability of the high and steep slope of the open-pit mine under static and dynamic loads was analyzed by using field vibration monitoring and numerical simulation methods. The results show that the vibration range of the vibration wave is -1.25–1.25 cm/s, and the vibration wave shows a gradual attenuation trend. The Sadovsky regression equation was used to analyze and fit the monitoring data and the corresponding regression equations in each direction were obtained. Under static action, the safety factor of the high and steep slope is 1.20, and the displacement of the sliding zone passing through the slope is small, so the slope stability is good. Under the action of dynamic blasting load, the overall displacement of the slope is small, and the change of displacement decreases with the decrease of the vibration wave.
In this study, the engineering properties of remolded diatomite and the effects of cement on the compression characteristic, strength properties and microstructures of cement-stabilized diatomite were investigated. Samples were prepared and stabilized with different cement content ratios, ranging from 0% to 15% by dry mass. Results show that compared with undisturbed diatomite, the compressibility of the remolded diatomite increases while the strength characteristics decrease. With the increase of cement content, the compressibility of cement-stabilized diatomite is significantly reduced and the strength characteristics are improved. Adding cement to diatomite changes the structure of pure diatomite and forms more tiny pores between cement and diatomite, while curing reduces the porosity ratio of samples and enhance the strength of cement-stabilized diatomite, especially for diatomite with higher cement content. The physical-chemical reactions including hydrolysis and hydration between cement and diatomite increase the content of sodium aluminosilicate, calcium aluminosilicate and other minerals in the soil.
To study the mechanical effect of clay under acidic and basic conditions, typical clay minerals, montmorillonite and illite, were taken as the main research objects in this study. The variation law and mechanism of the cohesive force and internal friction angle were studied by immersing the remoulded soil in HNO3 solution with pH = 3 and NaOH (alkaline waste liquid) with pH = 13.5, respectively. It was found that, under acidic conditions, a corrosion reaction between clay minerals and nitric acid occurred. Except for the medium-term, the cohesion generally shows a decreasing trend, and the internal friction angle has little change. Under alkaline conditions, the cohesion of montmorillonite-quartz sand remoulded soil decreased briefly in the early immersion stage of and increased in the middle and late stages. The internal friction angle increases steadily with the extension of immersion time. The cohesion of illite-quartz sand remoulded soil also decreased first and then increased, while the internal friction angle changed little. X-ray diffraction analysis shows that montmorillonite and illite will corrode under acidic conditions, and no new material will be generated, resulting in a decrease in soil cohesion. Under alkaline conditions, montmorillonite was seriously depleted, resulting in the formation of zeolite minerals (zeolite X, garronite) and new cement hydrated calcium silicate CSH (xonotlite). Strong alkali reacts with illite to generate sodium metaaluminate (NaAlO2) and liquid cement Na2SiO3 (sodium silicate). The formation of new cements is the main reason for the increase in cohesion under acid-base conditions, and chemical corrosion and ion exchange cause a decrease in cohesion.
Frontiers in Earth Science
Geological Hazards in Deep Underground Engineering: Mechanism, Monitoring, Warning, and Control