原位低温拉曼光谱技术在人工合成CaCl2-H2O和MgCl2-H2O体系流体包裹体分析中的应用Ⅱ:低温下流体包裹体相变行为的研究

本文利用低温原位拉曼技术,对CaCl2-H2O体系和MgCl2-H20体系人工合成流体包裹体进行了研究.结果表明:对于盐浓度不同的溶液而言,可采用不同的冷冻方式有效采集低温拉曼光谱;通过系统采集不同温度下的拉曼光谱,可以直接准确地测定包裹体中流体的成分和低温相变过程.人工合成包裹体原位低温拉曼光谱的研究,为将该技术应用于天然包裹体分研究奠定了理论基础,可以预见,该技术必将在流体包裹体研究领域发挥其它方法不可替代的重要作用.     

高层建筑结构的抗震可靠度分析与优化设计

本文根据我们在文献２中给出的等效随机地震静力作用模型，紧密结合规范和利用我们在文献６中提出的结构体系可靠度分析的最弱失效模式法，提出了结构构件和体系“小震不坏”和“大震不倒”及结构体系在设计基准期内的抗震可靠度分析方法；重新校准了结构构件的目标可靠度指标；综合考虑结构造价和损失期望，提出了结构体系抗震目标可靠度的优化决策方法；分别给出了满足构件抗震目标可靠指标与同时满足构件和体系抗震目标可靠指标的     

考虑围岩软化特性和应力释放的圆形隧道黏弹塑性解

将围岩的塑性应变软化特性引入到考虑应力释放的圆形隧道黏弹塑性解中,并且在围岩的软化和残余强度阶段考虑围岩的塑性体积膨胀特性,提出了考虑塑性软化以及塑性体积膨胀和围岩应力释放的圆形隧道弹塑性解。当软化系数k = ∞、膨胀系数h = s时,该解转化为黏弹-脆塑性解;当k = 0、h = s时,则转化为黏弹-理想塑性解,进一步令h = s = 1,则转化为不考虑塑性体积膨胀的黏弹-理想塑性解。通过具体实例计算,分析了掌子面与研究断面间距x、围岩的软化系数k、膨胀系数h和s、支护结构等对围岩塑性区、破碎区半径和变形的影响。当开挖面与研究断面间距x在(0~4)D（D为隧道直径）范围内,随着时间增加塑性圈和破碎区迅速增大;超过4D,塑性区和破碎区半径增量逐渐变小,趋于稳定值;围岩中包含塑性区和破碎区时,二者半径的比值只取决于围岩的性质,与支护结构无关,但支护结构可以限制塑性区及破碎区的范围;考虑应变软化和塑性体积膨胀时,围岩径向位移和塑性区及破碎区半径均大于不考虑应变软化和塑性体积膨胀时的结果;软化系数k增大,围岩位移、塑性区和破碎区半径增加、塑性区半径和破碎区半径之间的比值变小。得到的结果对于隧道工程设计和施工具有一定的指导性和参考价值。     

Three-dimensional quantitative analysis on granular particle shape using convolutional neural network

To identify all desired shape parameters of granular particles with less computational cost, this study proposes a three-dimensional convolutional neural network (3D-CNN) based model. Datasets are made of 100 ballast and 100 Fujian sand particles, and the shape parameters (i.e., aspect ratio, roundness, sphericity, and convexity) obtained by conventional methods are used to label all particles. For the model training, by feeding the slice images of particles into the model, the contour of particles is automatically extracted, thereby the shape parameters can be learned by the model. Thereafter, the model is applied to predict shape parameters of new particles as model testing. All results indicate the model trained based on slice images cut from three orthogonal planes presents the highest prediction accuracy with an error of less than 10%. Meanwhile, the accuracy for concave and angular particles can be guaranteed. The rotation-equivariant of the model is confirmed, in which the predicted values of shape parameters are roughly independent of orientations of the particle when cutting slice images. Superior to conventional methods, all desirable shape parameters can be obtained by one unified 3D-CNN model and its prediction is independent of particle complexity and the number of triangular facets, thus saving computation cost.