冀东铁精矿的物化性能测试

对冀东铁精矿进行了系统的物化性能测试。结果表明，铁精矿粒度较粗、颗粒表面光滑、比表面积小、SiO2含量较高。结合矿相结构分析了冀东铁精矿成球性能比较差的原因。采用3％的优质膨润土作铁精矿球团的黏结剂，制得球团的性能能够满足生产要求。     

碳酸盐岩内断裂带结构及其与油气成藏

以野外观察描述为手段,系统研究了碳酸盐岩断裂变形机制的影响因素及断裂带结构演化过程,剖析了碳酸盐岩地层中断裂带结构与流体运移的关系。研究表明,影响碳酸盐岩内断裂变形机制的因素包括岩性、孔隙度、变形深度、温度、胶结作用、先存裂缝等,控制断裂带结构形成的因素包括滑动位移和破裂模式等。低孔隙度碳酸盐岩以裂缝发育为主,高孔隙度碳酸盐岩变形早期产生变形带,带内裂缝联接逐渐发育成断层带。随着埋藏深度的增加,断裂带结构不同：埋藏深度小于3 km,断层核主要发育无内聚力的断层角砾岩和断层泥;埋藏深度大于3 km,断层核普遍发育有内聚力的断层角砾岩和碎裂岩,破碎带发育多种成因的裂缝。随着位移的增加,破裂模式从早期的破裂作用变为后期的碎裂作用,最终形成碎裂流。断裂带演化是一个四维过程,断层核和破碎带发育情况直接影响断层对油气的运移和封闭的作用。断裂变形机制、断裂带内部结构以及与流体运移关系的研究,都可为封闭性提供重要的理论依据。     

冀中南平原土壤碘地球化学特征研究

对冀中南平原土壤碘的地球化学分布特征及其控制因素的研究表明,冀中南平原滨海盐土和沼泽土中碘含量最高,白洋淀湖泊沼泽相和沧州东部的滨海平原深层土壤碘含量较高,而太行山中段滹沱河冲积扇平原、漳河冲积扇平原深层土壤碘含量较低,显示沉积环境是控制土壤碘区域分布的重要因素。研究认为土壤碘的分布特征受自然因素和人为因素的影响,以自然因素为主,表层土壤碘受到较大的人为因素影响。     

平推式滑坡启动判据的修正

对平推式滑坡启动判据的传统研究往往忽略滑块后缘的静水压力沿滑面法向的分力及承压水作用的具体范围,因而导致计算结果不准确。通过对平推式滑坡进行全面的受力分析,同时引入了承压水作用范围的概念,最终推导出修正后的平推式滑坡启动判据公式。将所得的修正判据应用于模型试验的结果分析,发现修正方法计算结果比传统方法大6.0%～15.5%,更加接近于试验观测值,验证了修正判据的准确性。同时对平推式滑坡启动临界水头的模型试验结果和修正计算值之间的差异进行了机理分析：当边坡模型滑动面倾角小于等于8°时,渗流的时滞性和渗透层的不均匀性导致启动临界水头实测值大于修正计算结果;当倾角大于8°时,模型稳定性随试验时间增加而逐步降低,导致启动临界水头实测值小于修正计算结果。     

强地面运动数值模拟中三维物理模型的建立方法——以昆明盆地为例

基于活动断层的地震危害性综合评价可为城市规划和工程建设提供科学依据, 强地面运动数值模拟则是进行地震危害性预测和评价的重要方法, 而建立以第四系为主体的符合真实地层结构的三维物理模型是保证数值模拟结果可靠性的必要条件之一. 本文以昆明盆地为例, 综合利用地震地质、钻孔、地形地貌、DEM、地震勘探、波速测试等资料,以剪切波速为主要分层指标,参考地层层序, 结合ArcGIS等多种软件和相关的编程技术,采用将相邻介质分界面（层网）在深度方向上逐层叠加的方法建立三维物理模型;详细阐述了建立三维物理模型的主要技术思路和实现过程, 同时建立三维物理模型的数据库,为强地面运动数值模拟提供模型数据和参数. 文中还针对不同类型的探测、实验数据,总结了相应的技术处理方法和需要注意的问题.      

基于经验遗传-单纯形算法和结构模态参数识别结构物理参数的方法

对于复杂的大自由度系统的反演分析,遗传算法每步计算中包含大量的正演分析,成为限制遗传算法应用的运行速度的瓶颈。减少反演分析中的正演计算次数,是扩大遗传算法适用范围的有效途径。经验遗传-单纯形算法正是解决这一问题的一种有效方法。本文将这一方法应用于不完全模态参数已知条件下的结构物理参数识别研究。结果表明:本文建议的方法有精度和搜索效率高、对初值选取依赖性不强、可以反映"残缺"的高阶模态信息等优点。     

Application of experimental design techniques to structural simulation meta-model building using neural network

Neural networks are being used to construct meta-models in numerical simulation of structures. In addition to network structures and training algorithms, training samples also greatly affect the accuracy of neural network models. In this paper, some existing main sampling techniques are evaluated, including techniques based on experimental design theory,random selection, and rotating sampling. First, advantages and disadvantages of each technique are reviewed. Then, seven techniques are used to generate samples for training radial neural networks models for two benchmarks: an antenna model and an aircraft model. Results show that the uniform design, in which the number of samples and mean square error network models are considered, is the best sampling technique for neural network based meta-model building.     

A macro‐element model for non‐linear soil–shallow foundation–structure interaction under seismic loads: theoretical development and experimental validation on large scale tests

In this paper, different formulations of a macro‐element model for non‐linear dynamic soil‐structure interaction analyses of structures lying on shallow foundations are first reviewed, and secondly, a novel formulation is introduced, which combines some of the characteristics of previous approaches with several additional features. This macro‐element allows one to model soil‐footing geometric (uplift) and material (soil plasticity) non‐linearities that are coupled through a stiffness degradation model. Footing uplift is introduced by a simple non‐linear elastic model based on the concept of effective foundation width, whereas soil plasticity is treated by means of a bounding surface approach in which a vertical load mapping rule is implemented. This mapping is particularly suited for the seismic loading case for which the proposed model has been conceived. The new macro‐element is subsequently validated using cyclic and dynamic large‐scale laboratory tests of shallow foundations on dense sand, namely: the TRISEE cyclic tests, the Public Works Research Institute and CAMUS IV shaking table tests. Based on this comprehensive validation process against a set of independent experimental results, a unique set of macro‐element parameters for shallow foundations on dense sand is proposed, which can be used to perform predictive analyses by means of the present model. Copyright © 2011 John Wiley & Sons, Ltd.