非一致地震激励下大跨度斜拉桥空间响应分析

基于二维相干非一致激励功率谱模型，采用随机过程理论关于幅值、相位与功率谱的关系式，根据场地地震危险性评价和设计地震动参数合成人工地震波，考虑波动传播效应和多点激励效应，分析了某座跨长江的大跨度斜拉桥在地震激励下的空间非线性响应特性，计算了桥梁结构关键部位在地震力作用下的响应特性，并与一致激励下对应响应量进行比较。认为非一致激励下大跨斜拉桥关键部位的某些响应粱大幅增加。反应了进行非一致响应分析的必要性。     

8XC196KC用于ABS轮速测量方法的改进研究

对ABS中速度测量的过程进行仔细的分析,提出了一种能够将精度和实时性相结合的速度测量方案,该方案能够较为有效地平衡精度和实时性之间的矛盾,并通过实际程序的执行验证了这一结论。这一测量方法不仅可用于ABS的速度测量,也可推广用于其他的速度或周期测量方案中。     

Evaluation of detection sensitivity and count performance of advanced vehicle detection technologies at a signalized intersection

This article evaluates the stop-bar detection and count performance of three advanced vehicle detection sensors under various environmental conditions at a signalized intersection. Continuing advancements to vehicle detection technologies and improvements to their detection capabilities to overcome issues from impacting conditions necessitates testing the performance of new and upgraded sensor products to evaluate their performance and identify the most suitable products for various climates and weather conditions. The three evaluated sensors were Autoscope Encore video, Iteris Vantage Edge 2 video, and Wavetronix SmartSensor Matrix microwave sensors. The three sensors performed with high detection sensitivity during ideal environmental conditions with up to 99.9% detection accuracy levels and are suitable for traffic monitoring centers that rely on remote access to the monitored sites and the collected data. However, they were affected by some extreme adverse weather conditions, mainly daytime and nighttime snow, daytime fog, dawn lighting, and strong winds (for high mounted devices). The selection of a sensor product will depend on the type of application and the priority given to the type of traffic data being collected. Overall, the Iteris video sensor performed with the highest detection sensitivity levels, with the Wavetronix Matrix microwave sensor performing similarly under most conditions (14 of 19 evaluated conditions). Autoscope video provided the highest count accuracies and also provides a much broader data collection capability. The results of this study will help transportation agencies in selecting suitable vehicle detection sensor technologies for future installations within their jurisdiction and improved data collection.     

一种低桩宽承台卸荷板式海堤在限制环境下的应用

海滨城市建设海堤经常受相邻桥隧的影响,建设条件受到各种限制,传统的大型土石方海堤已无法满足要求。因此,提出一种低桩宽承台卸荷板式海堤,该结构能适应打桩设备小、施工作业面狭窄、地质条件差且不能换填、地面荷载大及防腐要求高等不利条件。采用MIDAS/GTS NX软件对结构进行三维数值模拟计算,并现场监测收集变形数据,结果表明,该结构具有桩基弯矩小、水平位移小、整体稳定性好等优点。     

波浪中过度加速度的非线性时域预报

国际海事组织(IMO)船舶设计建造分委会(SDC)第4次会议把过度加速度稳性直接评估的制定提上议程。然而,如何准确预报和评估波浪中的过度加速度依然是一个亟待解决的问题。文章针对此挑战,采用三维时域混合源法进行了波浪中过度加速度的直接预报和评估。首先,采用三维时域混合源法,建立了波浪中船舶大幅横摇运动和船体任意位置处横向加速度的非线性时域预报方法;其次,以4000TEU集装箱船为研究对象,开展规则波和不规则波中的大幅运动模型试验,对计算方法进行验证;然后,对比分析了3DOF(垂荡—纵摇—横摇)和4DOF(横荡—垂荡—纵摇—横摇)耦合数学模型的计算精度;最后,分析了船体横向加速度的影响因素。研究表明,考虑横荡影响的4DOF数学模型计算精度较高;数值计算结果和模型试验结果吻合良好,证明文中建立的非线性时域方法可有效预报波浪中的过度加速度,可用于IMO过度加速度衡准的制定,也可为船舶设计提供评估手段。此外,文中还研究了IMO薄弱性衡准草案中中国和德国联合提出的加速度简化计算方法的适用性,证明该简化计算方法具有一定的保守性,符合衡准的要求,可用于过度加速度薄弱性衡准计算。     

地下掘进之地盘——机械互制与可挖指标研探

地下机械式之掘削效率乃为进度、成本及品控之关键要因,一般工程思考皆多考虑工法与机型对应各类不同地层之"可挖性"作探讨,即针对地质材料选择适应之切削面盘,鲜能再对地盘——机械开挖互制行为作一"适确性"研析,而本研究先由微观之量纲分析及力学尺度探讨并建立指标,运用至巨观推力、扭矩之施工良窳、可挖性及正常施作或异况破坏之分析。机械式掘削接触地质与产生之互制行为相异性极大,故由地质破坏特征作为切削地盘材料之分类,藉此提供该广义式之探讨方针;其次在施工判准所建立之可挖指标上,使用量纲分析方法进行具代表力学行为之因子解析,作为建置指标的依据;再者,应用现场调查以资搜机械式开挖所量测的推进力系数值,进而佐证其适确性,最终完整研析机械式掘削开挖之行为与可挖性指标之应用。     

基于ArcSence校园三维系统的建立过程

以辽宁省交通高等专科学校三维校园系统为例．介绍了利用ArcGIS的3DAnalyst、3DMAX校园三维系统的建立过程,该系统采用基于GIS的开发方法,除具有常规的漫游、查询功能外,主要突出了系统的空间属性,加强了其空间分析功能同时也能将动画导出成AVI文件,被第三方软件调用。     

船舶与海洋工程中的焊接变形预测方法研究概述

文章介绍了焊接变形预测理论的发展及现状,并将其归纳为基于实验和理论相结合的经验计算阶段、基于计算机数值模拟的模型分析阶段、基于新兴数据挖掘方法的研究应用阶段三个标志性阶段,详细论述了每个阶段预测技术在实际中应用的优劣.着重以BP、RBF人工神经网络为例阐述了神经网络等数据挖掘方法在焊接变形预测中的应用前景,并指出了目前存在的不足及未来亟待研究的方向.     

基于尺寸精度的铝合金减震器塔结构设计及优化

铝合金作为一种优质轻量化材料在轻量化车身上的应用越来越广泛,为了确保铝合金零部件的使用功能和性能,设计人员在设计前期就需要对零部件的精度进行设计分析和优化。文章基于一种压铸铝合金减震器塔的零部件,首先对该铝合金零部件精度进行分析摸底并识别出影响该铝合金零部件精度的两个关键要素,再分别针对两个关键要素进行优化设计,重点一是优化加强筋结构,以增加减震器塔本体的刚度和抗变形能力,另外是增加热处理工艺中夹具支撑块结构,通过外力支撑来增加抗变形能力。结果表明:合理的加强筋布置能有效提升减震器塔本体的刚度、强度和尺寸精度;热处理时需设计专用支撑夹具,且支撑夹具要保证所有重要安装面的支撑,避免铝合金减震器塔局部出现悬臂,通过外力支撑来防止变形。     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.