冻融作用下石灰稳定土动态回弹模量试验研究

为揭示石灰稳定土的动态回弹模量在冻融循环下的演变规律,文章研究对石灰掺量为0、3%、6%、9%的路基土试件分别在经历0次、3次、6次、9次、12次冻融循环后开展动三轴试验。结果表明,石灰的掺入有助于提高路基土的动态回弹模量。同时,冻融循环的累积衰减了动态回弹模量,且前6次冻融循环后动态回弹模量衰减较大,之后逐渐趋于稳定。此外,石灰稳定土的动态回弹模量受应力状态影响显著,具体表现为随偏应力的增大而减小,随围压的增大而升高。在此试验规律基础上,建立了能考虑石灰掺量、反复冻融循环作用及应力状态的路基土动态回弹模量预估模型,其回归精度较高。     

路基应用场景下尾矿土的力学特性试验研究

尾矿土的大量积存是目前亟待处理的重要问题,而将其作为填料用于路基修筑已在实际工程中被证明是合理有效的解决途径。为揭示路基应用场景下尾矿土的力学特性,文章开展了考虑湿度与围压变化的不固结不排水三轴剪切试验。结果表明：不同湿度与应力状态下尾矿土应力应变曲线均为硬化或稳定型,且Konder双曲线模型可准确描述尾矿土的变形行为;尾矿土的弹性模量与峰值破坏强度表现出明显的湿度敏感性与应力依赖性,具体表现为含水率的升高与围压的降低均导致弹性模量与峰值破坏强度发生衰减。此外,随着湿度水平逐渐增大,尾矿土的粘聚力与内摩擦角有所降低。研究所得可为路基工程中尾矿土的资源化利用提供理论依据与技术指导。     

某路基土料的干湿力学特性试验研究

路基病害的产生和发展与其土体含水率的关系最为密切,土体在不同的含水率状态会表现出不同的力学特性.通过对某道路路基土料的增湿变形特性试验分析,得出了该土料在不同压力下不同含水率下的胀缩特性,及其土料的膨胀和湿陷的界限压力值;并对土料的自然样和饱和样进行了三轴试验,分析整理了其应力应变非线性参数,进行了对比分析.     

连续刚构桥O#块空间应力分析

文章以某连续刚构桥的0#块为研究对象,建立Midas FEA有限元模型,对其成桥后正常使用极限状态下的0#块空间应力状况进行分析,同时对0#块各关键截面的应力集中效应进行研究.结果 表明:在刚构桥0#块的设计中,应重点考虑顶板纵向预应力对0#块应力分布的改善作用;0#块的横向应力和竖向应力不可忽视;0#块横隔板过人孔区...     

不同位置的加筋材料对桩-网复合地基桩土应力的影响分析

为了分析加筋垫层铺设位置对桩土应力的影响,文章通过加筋材料的铺设位置、厚度,分别对桩-网复合地基桩土应力进行了连续观测。结果表明：在垫层底面加筋材料加铺层数相等的条件下,加筋材料的铺设位置对复合地基的桩顶土压力和桩间土表面土压力影响有限;加筋材料铺设在垫层以下不同位置,桩-网复合地基中桩土应力比增加;填土高度为10～100 cm时,加筋垫层顶面桩间土内的土压力平均值随垫层厚度增加而提高。通过模型试验,为经济合理地设计桩-网结构提供科学依据。     

高速公路膨胀土路基处理探讨

文章对膨胀土的物理力学进行了分析,解释了路基病害形成的机理,提出了相应处理措施,最后引用小河-安康高速公路膨胀土处理的实例验证了处理措施的合理性.     

基于CAESARⅡ的集油管道跨越段应力分析

集油管道跨越段需要考虑的载荷与一般线路段相比较复杂,主要是增加了因风载、雪载、检修活载等引起的位移载荷,以及考虑地震的影响。在姬十联外输管道千井沟跨越段应用了CAESARⅡ软件,分别开展了水压试验应力、持续应力、热胀应力、固定推力支墩约束受力及位移分析及核算,并以ASME B31.3-2012、ASME B31.4-2010作为计算结果的判定依据,实践表明该软件计算结果合理。     

公路处治土路基长期性能研究

＂公路处治土路基长期性能研究＂项目通过对大量的工程调研、室内外试验、理论分析、数值模拟和现场监测等综合手段,系统研究了典型处治土路基在干湿循环、冻融循环和淋滤作用等环境因素与荷载作用下的工程特性及其影响、长期强度与变形规律,建立了考虑路基路面协调作用、基于动强度理论的处治土路基设计方法框架体系与指标,提出了提高处治土路基长期性能的工程技术措施,同时结合研究成果成功修筑了实体工程,编制了《公路处治土路基设计施工技术指南》。本篇介绍了该项目的主要创新性研究成果、项目的应用及推广情况,对于完善路基设计理论和方法、促进行业技术进步和学科的发展具有重要理论意义。     

关于管道钢的应力腐蚀开裂问题

管道应力腐蚀开裂是从潜伏在管道表面上的小裂纹发展起来的,引起应力腐蚀开裂必须同时具备三个条件,即拉应力、特定的腐蚀环境和敏感的管道材料。管道内表面接触Ｈ２Ｓ、ＣＯ２会形成腐蚀性介质,造成硫化物应力腐蚀开裂。埋地管线外壁在保护涂层和阴极保护失效的情况下,也会引起外壁应力腐蚀开裂。中性ｐＨ值应力腐蚀开裂４个因素条件即防腐层、土壤、温度和阴极保护电流的状况。建议要考虑管道裂纹的内检测工具的通过,加强对国     

复杂应力状态下再生混凝土多轴试验研究

文章以服役期满的道路废弃混凝土为粗骨料,设计了6个道路再生混凝土试件进行常规三轴试验研究,分析再生混凝土在复杂应力状态下的破坏形态,获取了试件受力应力-应变全过程曲线.试验结果表明:道路再生混凝土与天然混凝的应力-应变曲线相似,侧向压力对再生混凝土强度有较大的影响.该研究结论可为深入了解再生混凝土的力学性能及指导道路再生混凝土应用于工程实践提供理论依据.     

土基模量对沥青路面性能影响分析

从沥青路面结构层施工质量控制、半刚性基层反射裂缝扩展、路面疲劳寿命三个方面分析了土基模量对半刚性基层沥青路面性能的影响规律,提出了沥青路面土基模量的合理设计和施工控制措施。     

Real‐time identification of traffic conditions prone to injury and non‐injury crashes on freeways using genetic programming

This study applied the genetic programming (GP) model to identify traffic conditions prone to injury and property‐damage‐only (PDO) crashes in different traffic states on freeways. It was found that the traffic conditions prone to injury and PDO crashes can be classified into a high‐speed and a low‐speed traffic state. The random forest (RF) analyses were conducted to identify the contributing factors to injury and PDO crashes in these two traffic states. Four separate GP models were then developed to link the risks of injury and PDO crashes in two traffic states to the variables selected by the RF. An overall GP model was also developed for the combined dataset. It was found that the separate GP models that considered different traffic states and crash severity provided better predictive performance than the overall model, and the traffic flow variables that affected injury and PDO crashes were quite different across different traffic states. The proposed GP models were also compared with the traditional logistic regression models. The results suggested that the GP models outperformed the logistic regression models in terms of the prediction accuracy. More specifically, the GP models increased the prediction accuracy of injury crashes by 10.7% and 8.0% in the low‐speed and high‐speed traffic states. For PDO crashes, the GP models increased the prediction accuracy by 7.4% and 6.0% in the low‐speed and high‐speed traffic states. Copyright © 2016 John Wiley & Sons, Ltd.     

Development of railway station choice models to improve the representation of station catchments in rail demand models

ABSTRACT

This paper describes the development of railway station choice models suitable for defining probabilistic station catchments. These catchments can then be incorporated into the aggregate demand models typically used to forecast demand for new rail stations. Revealed preference passenger survey data obtained from the Welsh and Scottish Governments was used for model calibration. Techniques were developed to identify trip origins and destinations from incomplete address information and to automatically validate reported trips. A bespoke trip planner was used to derive mode-specific station access variables and train leg measures. The results from a number of multinomial logit and random parameter (mixed) logit models are presented and their predictive performance assessed. The models were found to have substantially superior predictive accuracy compared to the base model (which assumes the nearest station has a probability of one), indicating that their incorporation into passenger demand forecasting methods has the potential to significantly improve model predictive performance.     

Bus journey times in medium size urban areas

The predictive accuracy of the models based on the fundamental relation between journey time and passenger demand can be improved through data disaggregation or route segmentation. Primary reason for this is the improvement in the estimates of stopping delays and delays due to passenger boarding and/or alighting (dwell time). Both Poisson and Negative Binomial model estimates of stoppings for passenger boarding and alighting are shown to improve with disaggregation. These improvements, however, contribute little to the overall predictability of the fundamental models which are useful for gaining insight into the significance and variability of the stopping delays and dwell time, or testing sensitivity to changes in the long term. Site or route specific models of journey times which have better predictive capability exist, and may be used for short-run planning. However, the interchangeability and performance over time of the latter, have to be evaluated before making definitive conclusions.     

Nonlinear gating control for urban road traffic network using the network fundamental diagram

This work proposes a nonlinear model predictive controller for the urban gating problem. The system model is formalized based on a research on existing models of the network fundamental diagram and the perimeter control systems. For the existing models, modifications are suggested: additional state variables are allocated to describe the queue dynamics at the network gates. Using the extended model, a nonlinear model predictive controller is designed offering a ‘non‐greedy’ policy compared with previous, ‘greedy’ gating control designs. The greedy and non‐greedy nonlinear model predictive control (NMPC) controllers are compared with a greedy linear feedback proportional‐integral‐derivative (PID) controller in different traffic situations. The proposed non‐greedy NMPC controller outperforms the other two approaches in terms of travel distance performance and queue lengths. The performance results justify the consideration of queue lengths in dynamic modeling, and the use of NMPC approach for controller design. Copyright © 2014 John Wiley & Sons, Ltd.     

A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting

The k-nearest neighbor (KNN) model is an effective statistical model applied in short-term traffic forecasting that can provide reliable data to guide travelers. This study proposes an improved KNN model to enhance forecasting accuracy based on spatiotemporal correlation and to achieve multistep forecasting. The physical distances among road segments are replaced with equivalent distances, which are defined by the static and dynamic data collected from real road networks. The traffic state of a road segment is described by a spatiotemporal state matrix instead of only a time series as in the original KNN model. The nearest neighbors are selected according to the Gaussian weighted Euclidean distance, which adjusts the influences of time and space factors on spatiotemporal state matrices. The forecasting accuracies of the improved KNN and of four other models are compared, and experimental results indicate that the improved KNN model is more appropriate for short-term traffic multistep forecasting than the other models are. This study also discusses the application of the improved KNN model in a time-varying traffic state.     

灰色理论在土石混填路基工后沉降预测中的应用

运用灰色理论建立了土石混填路基的非等时距预测模型,同时,为了预测土石混填路基工后沉降,建立了土石混填路基工后沉降的GM(2,1)模型。通过实例计算表明,GM(2,1)模型预测精度较GM(1,1)灰色模型高,在土石混填路基工后沉降预测中具有较大的实用价值。     

Further evidence on the temporal stability of disaggregate travel demand models

Current evidence on the transferability of disaggregate travel demand models is inconclusive. Adding to this body of research, the present analysis focuses upon the temporal characteristics of work trip behavior in the San Francisco Bay Area. Using before and after data sets associated with the BART Impact Travel Study, multinomial logit models of work trip modal choice are estimated. The results indicate that the general form and the coefficient estimates of a pre BART model are transferable in time. Moreover, when updated to reflect BART's presence, the model's predictive success and its implied elasticity measures are generally accurate, relative to those implied by reestimating the entire model on post BART data. Finally, as economic theory would predict, elasticity measures of the service related variables were found to increase over time.     

风积沙动态模量与含水率及压实度关系研究

选取新疆地区阜康以北风积沙,通过室内试验对含水率、静态模量及动态模量等指标进行测定,对数据进行处理,分析含水率及压实度对动态模量的影响,以及通过回归分析得到动态模量与含水率和压实度的关系,并得出动态模量取值范围,为此地区风积沙作为路基填料提供一定的参考依据。     

Modelling demand in restricted parking zones

Multiple linear regression (MLR) and geographically weighted regression (GWR) models are used for estimating parking demand in areas with paid short stay parking systems. These models have been applied to the city of Santander (Cantabria, Spain) to check their goodness of fit and their predictive ability. The results show the main advantages and disadvantages of using GWR models. The technique proved to be useful in this case study because it offered a better fit and made better predictions in a scenario showing a certain degree of spatial heterogeneity unexplained by any of the variables introduced into the global model. However, the GWR model also presented situations of local correlation although this was considered moderate given the results provided by the variance inflation factors and the local condition indexes.