Dynamic analysis of risers using a novel multilayered pipe beam element model

In this paper a recently proposed formulation for the multilayered pipe beam element is extended to dynamic analysis of risers. Derivations of hydrostatic and hydrodynamic loadings due to internal and external fluid acting on each element layer are presented. Mass and damping matrices, associated to each element layer, are properly derived by adding their respective contributions to the expression of the virtual work due to external loading. The finite element implementation allows for the numerical representation of either bonded or unbonded multilayered risers, including small slip effects between layers. A number of numerical examples have been carried out and the results show the accuracy and efficiency of the new element formulation, even in large scale riser analysis. Moreover, we establish a few benchmarks using multilayered pipes and risers.     

考虑细颗粒含量成层的客专粉砂地基固结问题传递矩阵解

基于现有成层地基固结问题求解的局限性及适用性,借助Laplace变换及逆变换法,将体现成层粉砂地基固结特性的参数作为刚度矩阵元素,以矩阵的形式在成层地基层间传递,求解出考虑不同分级加荷及边界条件下成层客专粉砂地基的固结解析解。结合不同细颗粒含量对客专粉砂固结特性的影响规律,研究不同细颗粒含量对成层客专粉砂地基的应力、孔压及位移的发展规律。同时,运用ABAQUS对成层粉砂地基的固结问题进行数值模拟分析,通过和理论解析解对比分析,进一步验证了传递矩阵法求解不同细颗粒含量成层客专粉砂地基固结问题的可靠性和适用性。     

高铁沿线地下水位动态变化引发的成层弱透水层固结研究

采用Laplace变换和传递矩阵法推导高铁沿线地下水位动态变化引发的成层弱透水层固结变形的解析解,通过与ABAQUS数值计算结果对比验证解析解的正确性。利用解析解对某高速铁路沿线成层弱透水层的固结性状进行研究,计算地下水位动态变化下土层的固结变形量、变形速率和超静孔隙水压力随时间的变化,并分析土层压缩模量和渗透系数对土层固结变形的影响。研究结果表明:随着地下水位降低,土层中孔隙水压力逐渐降低,土层固结变形量和变形速率逐渐增加;当地下水位保持稳定后,土层固结变形速率逐渐降低,但土层仍持续较长时间的固结过程;土层压缩模量越大,土层系统总沉降量和沉降速率越小;土层渗透系数越大,土层总沉降量越大,水位稳定后土层系统完成固结的时间越短。     

Estimating historical hourly traffic volumes via machine learning and vehicle probe data: A Maryland case study

This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors.