面向大型活动的交通应急预案快速生成与动态优化方法

现阶段大型活动突发事件下的交通组织应急预案多依赖人工经验,缺少针对性和定量化应急措施。针对预案的快速匹配问题,提出利用交通应急预案库中的历史案例提取突发事件特征属性,基于案例推理和朴素贝叶斯分类快速生成初始预案,并依据贝叶斯分类算法所得后验概率选取属性不完备情况下的最佳匹配预案。在此基础上,基于改进的规则推理建立交通应急预案库知识库和规则库,并采用正向推理修改预案内容。为构建评价指标体系,基于模糊层次分析法对预案应急能力水平和突发事件严重程度进行模糊评价。以北京2022年冬奥会为仿真案件,试验结果表明该方法可以快速生成最佳匹配预案并完成动态调整与完善。     

基于微生物诱导碳酸钙沉积（MICP）改善淤泥质土强度

采用微生物诱导碳酸钙沉积(MICP)对淤泥质土进行处理,用于提高淤泥质土的强度。以武汉东湖淤泥为研究对象,对MICP改性淤泥质土进行快剪试验与固结快剪试验。试验结果表明:MICP改性淤泥质土能增大淤泥质土的内摩擦角,对其黏聚力改变较小;MICP改性淤泥质土,胶结液浓度在1mol/L时对土体内摩擦角提高效果最好,快剪试验中MICP改性淤泥质土的内摩擦角从素土的8.54°提升至23.18°,提升了171.4%;固结快剪试验中MICP改性淤泥质土的摩擦角从15.96°提升至25.36°,提升了58.9%;MICP改性淤泥质土,生成碳酸盐沉积把土体小颗粒胶结成大颗粒,提高了土体大颗粒的质量分数。     

细水雾与排烟系统共同作用下地铁车站火灾烟气蔓延规律

为研究岛式地铁车站内列车发生火灾时,站台细水雾与排烟系统对烟气蔓延的控制效果,依托西安地铁4号线岛式地铁车站,采用FDS软件建立1：1的数值仿真模型,选择大涡模拟,研究站内列车火灾规模为5 MW时,站台细水雾与排烟系统共同作用下,火灾烟气蔓延速度、能见度与温度场的分布特征,分析了细水雾与排烟系统对烟气蔓延特性的影响规律;并通过缩尺模型试验,验证了数值模拟方法研究细水雾控制地铁火灾烟气蔓延的可靠性。研究结果表明：车门间隔开启时,烟气先向列车两侧蔓延,150 s时扩散至整个车厢并向站台层蔓延,当开启站台细水雾时,烟气温度明显下降,且随着细水雾粒径的减小与流量的增大,烟层降温效果增强;当水雾粒径为100 μm,流量为8 L·min^-1时,距离站台中线3 m处断面平均温度为36.19℃,较未开启细水雾时温升降幅可达62.91%;同时细水雾使得烟层蔓延速度减小,在开启细水雾系统后200 s内2^#楼梯口平均空气质量流速下降39.72%;当开启排烟系统时,可使列车内温度场纵向分布最大值向火源下游移动,加快站台层及列车内对流换热效率,使细水雾的气相冷却作用得到加强,二者同时作用时降温阻烟效果最佳。     

在役轨道交通矮塔斜拉桥检测与评估

以某在役轨道交通矮塔斜拉桥的检测与评估为例,通过对矮塔斜拉桥受力特点的分析,确定了定期检测中矮塔斜拉桥的检测重点.主梁、主塔和墩柱的恒载几何形态测量表明,该矮塔斜拉桥整体保持稳定的状态,主梁跨中有沉降的趋势;通过桥梁自振特性、斜拉索索力和动位移的测试,明确了该桥的动力特性和动力响应;通过外观检查,明确了主梁主塔混凝土结...     

巴塞罗那地铁设计与运营特点

分析巴塞罗那地铁系统线网、车站的特点,包括复合的网络结构、多样的线路形式,简洁朴素、人性化的车站,并介绍典型的线路与车站之间的基本形式——巴塞罗那解决方案.从运营方案、运营组织模式、列车类型和票务制度出发,归纳总结巴塞罗那地铁的运营特点,可为我国地铁建设规划与运营提供参考.     

等惯性两区段分散输水系统在40 m级单级巨型船闸中的应用*

输水系统是船闸实现其功能的关键设施,其设计直接影响船闸工程的安全与效率。等惯性四区段出水分散输水系统是目前国内外高水头大型船闸常用的输水系统形式,但其结构复杂、建设难度大,且由于设置第二分流口而带来自身空化等问题。为解决上述问题,结合我国内河船闸特点,通过国内外工程资料分析、理论与计算分析、物理模型试验等手段,提出并验证了“闸墙长廊道+闸室中心立体分流+闸底两区段四纵支廊道出水+侧支孔+明沟消能”的等惯性两区段输水系统形式在40 m级单级巨型船闸中的适用性,为高水头大型船闸建设提供参考和依据。     

郑万高铁大断面岩质隧道掌子面稳定性评价及控制措施

为建立施工阶段大断面隧道掌子面稳定性评价方法,结合郑万高铁大型机械化特点,对郑万高铁湖北段隧道掌子面地质信息进行统计分析,将掌子面稳定性分为A整体稳定、B局部掉块、C上半断面不稳定和D全断面不稳定4个级别,并建立以岩石坚硬程度、岩体完整程度和地下水状态3个指标的掌子面稳定性定性分级方法,得出围岩级别与掌子面稳定性的对应关系。研究结果表明： 1）岩石坚硬程度、岩体完整程度和地下水状态是围岩分级指标中使用率最高的3个指标; 2）Ⅰ、Ⅱ、Ⅲ级围岩的掌子面稳定性为A级。     

State estimation in roll dynamics for commercial vehicles

Accurate lateral load transfer estimation plays an important role in improving the performance of the active rollover prevention system equipped in commercial vehicles. This estimation depends on the accurate prediction of roll angles for both the sprung and the unsprung subsystems. This paper proposes a novel computational method for roll-angle estimation in commercial vehicles employing sensors which are already used in a vehicle dynamic control system without additional expensive measurement units. The estimation strategy integrates two blocks. The first block contains a sliding-mode observer which is responsible for calculating the lateral tyre forces, while in the second block, the Kalman filter estimates the roll angles of the sprung mass and those of axles in the truck. The validation is conducted through MATLAB/TruckSim co-simulation. Based on the comparison between the estimated results and the simulation results from TruckSim, it can be concluded that the proposed estimation method has a promising tracking performance with low computational cost and high convergence speed. This approach enables a low-cost solution for the rollover prevention in commercial vehicles.     

Suspension system state estimation using adaptive Kalman filtering based on road classification

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards Organisation road excitation levels.     

A signal-based fault detection and classification method for heavy haul wagons

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.