Mechanism for the rapid motion of the Qianjiangping landslide during reactivation by the first impoundment of the Three Gorges Dam reservoir, China

The first impoundment of the Three Gorges Dam reservoir in China started from a water surface elevation of 95 m on June 1, 2003 and reached 135 m on June 15, 2003. Shortly after the water level reached 135 m, many slopes began to deform and some landslides occurred. The Qianjiangping landslide is the largest one; it occurred on the early morning of July 14, 2003 and caused great loss of lives and property. Field investigation revealed that, although failure occurred after the reservoir reached 135 m, the stability of the slope was already reduced by preexisting sheared bedding planes. To study the mechanism of the rapid motion of this reactivated landslide, two soil samples were taken from a yellow clay layer and a black silt layer in the sliding zone, respectively, and a series of ring shear tests were conducted on the samples. One series of ring shear tests simulates the creep deformation behavior, while the other series simulates different shear rates. Conclusions drawn from analysis of the ring shear tests indicate that the mechanism of the rapid motion of the reactivated landslide was caused by the rate effect of the black silt layer during the motion phase after the creep failure. The yellow clay layer did not play any important role in the rapid motion in the 2003 event.     

基于免疫粒子群优化算法的影像纹理分类

粒子群优化算法是基于群智能的随机全局优化方法,它源于对鸟群简化社会系统的模拟。为了提高标准粒子群优化算法的收敛性能,将生物免疫系统的记忆能力和多样性引入标准粒子群优化算法,提出一种免疫粒子群优化算法。在提取纹理样本Laws纹理能量模板特征、小波特征等纹理特征的基础上,提出针对分类问题的粒子表达方法和群体寻优策略,实现了基于免疫粒子群算法的纹理分类。实验结果表明,与标准粒子群优化算法相比,免疫粒子群优化算法在获取训练样本类别中心时具有较好的收敛性能,并且基于该算法的影像纹理分类具有较高的分类精度。     

吉林省中部农业土壤中滴滴涕的残留特征

通过分析研究吉林省中部农业土壤中DDT的含量和组成,探讨了该区土壤中DDT的残留特征及其可能的生态风险。研究结果表明,吉林省中部农业土壤中普遍残留DDT,质量分数为0.384~54.159μg/kg,残留水平较低,土壤DDT同系物中以p,p’-DDT和p,p-’DDE为主。DDT的组成表明,土壤中DDT主要是过去输入环境的DDT及其降解产物,但近期仍有少量的DDT输入。生态风险评估则显示该区土壤中的DDT具有一定的生态风险,应予以重视。     

西藏扎布耶盐田盐渍土的改性配比试验

根据盐渍土的物理化学性质,采用掺入不同比例石灰或水泥的方法进行改性处理。经击实、抗剪和抗压强度试验结果表明,盐渍土掺入石灰或水泥改性后,混合料的最大干密度、最优含水率、强度指标等均有增加,特别是内聚力迅速增长。研究表明,盐渍土混合料的性状得到了明显改善,改性后的混合料可用于修建盐田埝堤类相关工程。     

浅谈测井方法在工程勘察中的应用

地球物理测井应用于工程勘察中,能够较好地解决用钻探不易解决的问题,如有效判别薄夹层、寻找井旁隐伏断裂破碎带、判断孔内土层稳定性以及测定土层的速度等。其中自然伽马测井曲线在判定含砾粘土夹层及软弱薄粘土夹层等方面具有较高的准确性;声波、井径、自然γ、视电阻率等测井方法可有效查找井旁隐伏断裂破碎带,特别是声波测井曲线异常更为明显;另外跨孔实测剪切波速还可进行地基土加固效果检验。     

磷处理粉煤灰可作农业土壤磷源

粉煤灰(简称CCP或ash)具有改良土壤,增加植物产量的功能。此外,由于它特殊的多孔结构,也可用作吸纳并承载植物养分的载体。用NaH2PO4配制成含磷0.10mol/L浓度的溶液,对采自加拿大西安大略省Sarnia地区Lambton电厂的底灰(bottomash)进行振荡浸渍处理。结果表明,振荡浸渍66h后的粉煤灰中磷含量可达784×10-6。以磷处理粉煤灰、未进行磷处理的粉煤灰和石英砂按比例混合,作为基本生长介质进行玉米种植实验,其中实验配方设计为生长介质中含磷量分别为标准含磷浓度(50×10-6)的10%、25%、50%、75%和100%。生长26、34和46d后分别与不含磷的空白配方、施加含氮-磷-钾为0-20-0标准磷肥并控制磷含量为标准浓度(50×10-6)配方进行生物产量对比。生长实验结果表明,以磷处理粉煤灰供磷的生长介质,当含磷量为标准浓度的25%至100%时,其植物生长量就比添加标准浓度磷肥的配方好。种植46d后的生物生长量统计结果显示,含磷分别为标准浓度50%、75%和100%的实验介质中,玉米杆的鲜重较施标准磷肥介质中玉米分别增长39.46%、42.73%和46.13%;玉米杆干重依次增加29.71%、13.39%和28.87%;根鲜重平均增加16.62%;根干重平均增加14.03%。上述实验结果启示,粉煤灰可以很好地吸纳承载磷养分,并持续供给植物吸收生长,如果采用吸纳磷(或其他养分)的粉煤灰改良砂质土壤将有重要意义。     

A new concept of pedogenic nodule formation in landscapes of the Russian Far East

The typification of ferromanganese nodules (FMN) formed in subaqueous and subaerial settings and in residual materials, as well as FMN localization in various sediments and soils, is discussed. The genetic diversity of morphologically similar FMN and the transformation of FMN-bearing complexes with changes in landscapes have been established.     

New basis for the constitutive modelling of aggregated soils

Natural and compacted soils are usually characterized by aggregation of particles. The mechanical behaviour of these materials depends on soil structure. The oedometric compression tests performed on aggregated samples presented here showed that these materials exhibit a yield limit depending not only on stress history and stress state but also on soil structure. Evidence is provided using the neutron tomography technique. These results revealed that soil structure modification occurs together with plastic deformations. The experimental results are used to propose a new state parameter to quantify the soil structure. Based on pore-scale experimental observations, an evolution law for this parameter is proposed as a function of associated plastic strains. Considering both soil fabric and inter-particle bonding effects, a new yield limit depending on stress state, stress history and soil structure is introduced for the aggregated soils. Accordingly, a new constitutive framework consistent with strain hardening plasticity is proposed to consider soil structure effects in the modelling of aggregated soils.     

Changing grading of soil: effect on critical states

Examples of situations are presented where the grading of a soil changes during its lifetime either by crushing of particles leading to an increase of fine material or by slow transport of fine particles with seepage leading to a decrease of fine material. Such grading changes influence the basic constitutive properties of the soil, in particular properties such as critical states which are dependent on the available range of densities of packing. Discrete element modelling is used to show the dependence of critical state conditions on grading and the way in which the particle assembly seeks out new critical state conditions as the grading changes.     

Automatic classification of volcanic earthquakes by using Multi-Layered neural networks

The application of neural networks as classifiers of seismic events is described with the aim of developing an automatic system for the classification of explosion quakes at the Stromboli volcano. The architecture of the network that we trained to identify four different classes of shocks was a Multi-Layer Perceptron, using the Back Error Propagation algorithm. Five different approaches for representing the information embedded in the seismograms, both in the time and in the frequency domain, were considered, and the results compared. The direct use of the time series of the shocks was not satisfactory. The auto-correlation function worked well, but in some cases it was misleading. A better performance was obtained with a frequency domain representation. Finally, the use of the envelope function did not work well. Combining parameters such as the auto-correlation and envelope functions can improve one source of error, but it may introduce new ones. The performance obtained highlights the importance of the data attributes used for the training of the network. Topologies with eight neurons in a single hidden layer gave, on average, the best results among the considered neural network structures. The overall results provide a large number of events (89% with the best performance) correctly classified, indicating that this automatic technique is reliable, and encouraging further applications in the field of volcanic seismology.