Minding the geotechnical data gap: appraisal of the variability of key soil parameters for slope stability modelling in Saint Lucia

Bulletin of Engineering Geology and the Environment - Identification of failure thresholds and critical uncertainties associated with slope stability often requires the specification of...     

Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R²) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches.     

Preparation of activated carbon with low ash content and high specific surface area from coal in the presence of KOH

An activated carbon with ash content less than ]0% and specific surface area more than 1600 m^2/g was prepared from cool and the effect of K-containing compounds in preparation of cool-based activated carbon was investigated in detail in this paper. KOH was used in co-carbonization with coal, changes in graphitic crystallites in chars derived from carbonization of coal with and without KOH were analyzed by X-ray diffraction (XRD) technique, activation rates of chars with different contents of K-containing compounds were deduced, and resulting activated carbons were characterized by nitrogen adsorption isotherms at 77 K and iodine numbers. The results showed that the addition of KOH to the coal before carbonization can realize the intensive removal of inorganic matters from chars under mild conditions, especially the efficient removal of dispersive quartz, an extremely difficult separated mineral component in other processes else. Apart from this, KOH demonstrates a favorable effect in control over cool carbonization with the goal to form nongraphitizable isotropic carbon precursor, which is a necessary prerequisite for the formation and development of micro pores. However, the K-containing compounds such as K2CO3 and K20 remaining in chars after carbonization catalyze the reaction between carbon and steam in activation, which leads to the formation of macro pores. In the end an innovative method, in which KOH is added to coal before carbonization and K-containing compounds are removed by acid washing after carbonization, was proposed for the synthesis of quality coal-based activated carbon.     

办好《铀矿冶》,为铀矿冶事业发展作出新贡献

回顾了《铀矿冶》不平凡的20年历程，结合核工业北京化工冶金研究院取得的科研成果，评价了《铀矿冶》在我国铀矿科技和生产中所起的重要作用。     

气固流化床密度的在线测控研究

在对气固流化床选煤系统被控对象特性分析研究的基础上，提出用逆系统控制方法解决具有非线性多变量特点的气固流化床密度控制问题，建立了相关数学模型，介绍了流化床密度逆控制系统和方法，并获得较好的控制精度。     

矿井气压分层测深与测量仪表的研究

矿井通风阻力参数测长过程，需利用测点相对标高值计算位压能，传统的做法是从建井、开拓、采掘等施工设计时的地质资料中查找，因修复及施工中改变方案等客观因素，造成无法查得准确和齐全的测点标高值。给测算带来诸式麻醉和遗憾。基于流体分层异重流学和大气热力学理论的研究成果与思维方式,分析研究了矿井大气压力系统的特征及矿井大气流动系统分层的准则。依据分层准则，提出了关于气压分层测深数学模型及其测量仪表的设计原则。     

松辽盆地可地浸砂岩型铀矿钻探施工技术

总结介绍了松辽盆地可地浸砂岩铀矿钻探的施工工艺，冲洗液配制及管理，可地浸砂岩铀矿钻探的目的层——松散砂体的取心技术和退心技术，钻探管材的使用及孔内事故的预防与处理。     

配焦粉炼焦的焦炭冷、热态性能研究

配加焦粉炼焦的焦粉细度、配量对焦炭质量，尤其焦炭的热态性能的影响，进行了试验分析，明确了焦粉细度和配量的变化对焦炭质量的影响趋势，更好的指导配焦粉炼焦的生产。     

火箭炮位置伺服系统的神经网络监督控制

火箭炮发射架位置伺服系统中存在燃气流冲击力矩、参数时变、外部扰动等非线性因素的作用,针对传统PID控制难以在此复杂工况下取得良好控制效果的缺点,文中设计了基于传统PID控制的RBF神经网络监督控制器,完成了神经网络离线样本的选取和训练算法的改进,利用神经网络自学习、自整定能力增强系统的自适应能力。仿真结果表明此复合控制策略可以有效提高系统的控制品质。