基于轨道结构高差特征值的CRTSⅡ型板离缝定量检测方法

高速铁路无砟轨道的离缝病害对行车安全至关重要,对此提出一种基于轨道板-支承层相对高差特征值的离缝定量检测方法。通过集成线结构光等传感器构建离缝检测原型系统设备,用于采集轨道轮廓线;将轨道轮廓线按轨道结构分成三段并基于RANSAC算法分别进行拟合,拟合直线相交两点间的距离以此作为轨道结构高差特征值;以轨枕位置为里程定位基准,对比同一区段内轨道结构高差特征值的多次检测结果,通过变化量精确计算离缝病害的长度和高度值。在此基础上进行线路试验,结果表明：检测系统多次重复测量结果的标准偏差系数稳定在2%以内;在95%的置信度下,重复测量偏差小于0.25 mm;将检测结果与铁路部门当期的病害库进行对比,离缝高度和长度检测结果的平均绝对误差分别为0.24 mm和0.34 m,均方对数误差值分别为0.011和0.017。     

土石混合料-土工织物界面剪切特性研究

土石混合料加筋结构常见于高填方边坡工程中,其稳定性受筋土界面相互作用的影响。为了研究土石混合料-土工织物界面的相互作用机理,利用了室内大型直剪仪进行一系列直剪试验,分析了5种含石量(0%、25%、50%、75%、100%)、3种压实度(88%、92%、96%)以及3种竖向应力(100、200、300 kPa)对界面剪切特性的影响,并建立了界面剪胀系数的经验公式。结果表明：界面在低含石量(0%～25%)下表现出剪切软化趋势,在高含石量(50%～100%)下表现出剪切硬化趋势,且剪切应力-剪切位移曲线出现较为明显的波动现象;界面最大竖向位移随含石量和压实度的增大呈现出增大趋势,随竖向应力的增大呈现出减小趋势。界面摩擦角在低含石量下基本保持稳定,而后随含石量的增大表现出先增大后减小的趋势;界面似黏聚力的增长速率随压实度的增大而减小;结合含石量、竖向应力等因素,建立了剪胀系数经验公式,该公式能够较为准确地描述界面的体积变化情况。     

帕尔菲格携2类上装亮相Bauma China2010

在Bauma China2010上,意大利帕尔菲格集团展出了2个系列的专用汽车上装产品一随车起重机和拉臂钩,包括分别装备PS8000直臂式随车起重机和PK27002折臂式随车起重机的2辆随车起重运输车,1台PK50002·EH折臂式随车起重机,以及1台拉臂钩系统。     

浅谈旧水泥混凝土路面板处理方案

简要介绍了旧水泥混凝土路面板处理的几种方案。     

浅谈沥青路面裂缝的密封胶灌缝处理

列举了沥青路面的各种裂缝形式,并分析了各种裂缝的产生原因。结合京沈路(K17+000~K31+000)大修工程的现场施工情况,进而有针对性的分析了密封胶灌缝处理的优点。     

水泥混凝土路面胀缝设计方法探讨

对水泥混凝土路面胀缝破坏的原因进行了分析,针对胀缝设计、施工过程中存在的问题,提出了改进的胀缝设计方法,以及不设胀缝或少设胀缝的理论分析。     

一种高效的圆形窗口线段裁剪算法

在圆形窗口线段裁剪过程中,当两端点都在窗口之外时情况较为复杂.从较远端点向圆形窗口引切线,通过判断点相对于两切线以及垂直于切线的两圆半径所在直线的位置关系,确定被裁剪线段是否与圆形窗口相交,只对有交点的情况才进行求交运算.实验结果表明,本算法明显提高裁剪效率.     

再生集料刚性基层横向切缝间距研究

针对现有刚性基层缩缝间距计算公式存在的问题,结合温度场的计算理论,采用有限元计算方法对养护期末刚性基层的温度场和温度应力进行分析,并与再生集料水泥混凝土基层的容许拉应力对比,提出了合适的切缝间距。     

槽形箱梁铁路桥空间分析

由于槽形箱梁铁路桥的空间受力情况较为复杂,为此建立新长沙站大桥空间实体模型,分析各种荷载工况下箱梁各个腹板的剪力分配情况,用以指导箱梁的抗剪设计;同时通过计算分析箱梁的空间应力分布情况,提出横向分缝的构造处理措施,以避免槽形梁梁顶以上腹板应力过大。     

Intelligent direct analysis of physical and mechanical parameters of tunnel surrounding rock based on adaptive immunity algorithm and BP neural network

Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and, mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relationships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively unproved the recommended values in the original prospecting data. This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.