Can tensile stress develop in fractured multilayers under compressive strain conditions?

To contribute to the understanding of how opening-mode fractures (joints) form and open or close at depth in layered rocks, we present a 2D numerical study aiming to determine whether tensile stress can develop in pre-fractured elastic multilayers submitted to biaxial compressive strain conditions.First, we investigate the role of the elastic and geometrical properties of the layers on the development of tensile stress in models with five bonded layers and containing one open fracture in the central layer. Our results indicate that, in absence of elastic contrast (in Young's modulus) between the layers, no tensile stress develops in the models. However, when the fractured layer is stiffer than the two adjacent layers directly above and below, a lobe of horizontal tensile stress develops centered on the pre-existing fracture. The creation of this tensile stress is contingent upon the partial closing of the fracture. The levels of tensile stress and the thickness of the lobe of tensile stress increase logarithmically with an increase in the elastic contrast and are systematically larger for a larger Soft/Stiff ratio (ratio of the total thickness of the soft layers with the total thickness of the stiff layers).Second, we investigate the role of fracture interaction in the development of tensile stress in models containing a pair of open fractures. We observe that the levels of tensile stress in the region between the fractures are systematically higher than those observed in identical models containing a single fracture. This increase in tensile stress is very large for small elastic contrasts between the layers but diminishes when the elastic contrast increases. Furthermore, the spacing between the pre-existing fractures plays an important role in the stress distribution in the region between them. When the fracture spacing is equal to or lower than 1.15 times the height of the fractured layer for the experimental conditions chosen, the lobes of tensile stress centered on the fractures coalesce. This results in the formation of vast areas of tensile stress in models under remote compressive loading conditions. Such tensile areas are likely to allow the initiation and propagation of subsequent opening-mode fractures.The results obtained provide new insights into the formation of joints in layered rocks in compressive environments, with important consequences on fluid flow.     

Utilization of red mud as a stabilization material for the preparation of clay liners

Red mud is a waste material generated by the Bayer Process widely used to produce alumina from bauxite throughout the world. Approximately, 35% to 40% per ton of bauxite treated using the Bayer Process ends up as red mud waste. Because of storing issues, the waste negatively affects the environment. To solve this problem, it is essential to investigate different uses for red mud waste. The potential use of red mud for the preparation of stabilization material is presented in this study. This study examines the effects of red mud on the unconfined compressive strength, hydraulic conductivity, and swelling percentage of compacted clay liners as a hydraulic barrier. The test results show that compacted clay samples containing red mud and cement–red mud additives have a high compressive strength and decreased the hydraulic conductivity and swelling percentage as compared to natural clay samples. Consequently, it is concluded that red mud and cement–red mud materials can be successfully used for the stabilization of clay liners in geotechnical applications.     

广西资源红辉沸石性能开发与应用试验的研究

阐述了广西资源红辉沸石矿的地质特征及该产地红辉沸石的物理性质、化学成分及其差热-热重分析特征。根据该产地红辉沸石的性能,试图开发其在高强建筑材料中的应用。在粘土中加入不同比例该产地红辉沸石后,经高温烧结所形成的固体材料的各种物理性质,如密度、表观密度、孔隙度、吸水率等有所降低,而力学性质如抗压强度、抗折强度等则增大。经过反复试验对比,获得了红辉沸石的添加量对烧成材料性质的影响规律。     

纳米二氧化硅在固井水泥浆中的应用研究进展

近年来,纳米二氧化硅由于其本身超高的比表面积及与水泥的火山灰反应,在石油天然气工业中引起了广泛的关注。本文系统梳理了纳米二氧化硅在固井水泥浆中的研究现状,着重介绍了纳米二氧化硅对固井水泥浆的失水量和稠化时间、对水泥石的力学性能和微观结构的影响,总结了NS在高温条件下的性能表现,探讨了纳米二氧化硅在固井水泥浆的应用中存在的问题、面临的挑战以及未来的发展方向,以期对纳米二氧化硅在固井领域的 应用和发展有所帮助。     

疏水材料处理吉林西部乾安地区盐渍土的力学强度试验

为了给吉林西部乾安地区盐渍土边坡水渠工程施工提供理论依据及施工参考,利用新型泥土超疏水乳液（CN）对该区盐渍土进行了处理,研究了不同处理条件下CN对盐渍土土体力学强度的影响,将经过不同处理的素土与CN土无侧限抗压强度进行对比,结果表明：1）在不同含水率条件下,CN土的无侧限抗压强度均有较大提升,且在14.86%含水率时取得最大提升率98.90%;随着含水率的增加,素土与CN土应力-应变曲线均存在由硬化型向软化型的转变,其阈值在最优含水率附近。2）随着压实度的增加,CN土无侧限抗压强度逐渐增强,在90%压实度条件下取得最大提升率130.77%;且CN处理后,原本呈塑性硬化型的素土转变成脆性软化型。3）盐分的增加对于CN土无侧限抗压强度起到了抑制作用,在0.3%含盐量条件下取得最大无侧限抗压强度1.33 MPa;过量的盐分结晶会破坏CN形成的纳米结构疏水膜,使得在高含盐量条件下,CN土的抗压强度低于素土。4）扫描电镜实验对CN土微观机理探讨,CN处理后使得土颗粒团聚呈大颗粒,颗粒表面光滑,孔隙发育减少。结构单元体和孔隙单元体的分析证明了处理后土体连结更为紧密,从而提升抗压强度。     

Non-parametric machine learning methods for interpolation of spatially varying non-stationary and non-Gaussian geotechnical properties

Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available.     

基于压缩感知的气象雷达回波压缩采样与重建

由于Nyquist采样定理的限制,高分辨率气象雷达面临采样率过高、数据存储量过大等问题。压缩感知理论可以实现气象雷达信号的压缩采样,解决采样率过高等问题。基于压缩感知理论,分析了气象雷达回波信号的稀疏性,建立了气象雷达回波信号的压缩采样和重建的过程,并结合气象雷达实测的回波数据进行仿真。仿真结果表明,0.3倍采样率下的重建回波与原始回波存在较大误差,0.5倍采样率时误差明显降低,0.7倍采样率时则可高概率重建出原始回波。因此,将压缩感知理论应用于高分辨率气象雷达的信号处理中,可以实现在较低采样率下高概率重建原始回波信号。     

Rock Strength Evaluation during Progressive Failure Process Based on Fractural Characterization

A series of laboratory tests was performed to study the decrease of the rock strength during its progressive failure process. Hoek–Brown criterion was adopted as the means to estimate rock strength. The parameters m and s of the criterion for different stages of progressive failure process were derived from the results of triaxial tests. To better evaluate the strength changes over a very wide range of length scales, fractals were introduced to describe the fractures of rock for each stage. Based on the test results, liner fitting expressions were used to approximate the relationship between the fractal dimension D of surface crack and parameters m and s to reflect the strength change of the rock with different fracture state for both model and full scales.     

Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns

Learning from data is a very attractive alternative to “manually” learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed.