公路和铁路占地比较基础研究

介绍了比较基础的含义,分析了以运输能力作为公路和铁路用地比较基础的原因和不合理性。提出以运输需要和经济社会影响作为比较基础才具有现实的指导意义。     

基于耐撞性的多学科近似模型预测精度研究

以整车100%正面碰撞有限元模型为基础研究了三种近似模型的预测精度,分析并选取前部结构中对汽车碰撞安全性影响较大的12个部件厚度为变量,利用最优拉丁超立方试验设计方法生成80个样本数据并进行计算,应用多学科优化中常用的二次多项式响应面(Quadratic Polynomial Response Surface,QPRS)、Kriging以及径向基函数(Radial Basis Function,RBF)三种近似方法分别对选取部件的总质量、吸收总能量、B柱最大加速度和踏板侵入量建立近似模型。结果表明:RBF近似方法对部件总质量、吸收总能量、B柱最大加速度预测精度高于其他两种方法,Kriging近似方法对踏板侵入量预测模型具有较好的精度,QPRS近似方法适合于部件总质量的近似建模。     

极浅水单点系泊系统的设计研究

在分析研究单点系泊系统的主要形式、特点和适用范围的基础上,选取了软刚臂单点系泊系统作为的"海洋石油113"新单点系泊系统,针对其设计中遇到的难题,进行了深入研究,归纳总结了极浅水海域单点系泊系统的设计时所重点关注的设计基础、低频波浪力、触底和上浪等关键问题,为今后极浅水单点系泊系统设计提供一点借鉴经验。     

公路工程预算编制过程

为适应公路交通建设发展的需要,合理确定和有效控制工程造价,提高公路建设项目工程造价的编制质量,在尽快熟悉并掌握公路工程定额和公路工程预算编制办法的同时,深入现场收集相关资料,并运用于实际工作中,本着以实用、简练为主要原则,为专业技术人员在工作中能合理有效地编制公路工程预算,提供帮助。     

对科学发展观进《思想道德修养与法律基础》课的思考

把科学发展观纳入到《思想道德修养与法律基础》课教学体系之中,是深入学习、实践科学发展观的重要举措。本文指出充分认识科学发展观进课堂的重大意义是搞好教学的前提条件,分析了科学发展观与大学生思想道德修养和法律修养的内在联系,提出科学发展观进课堂的具体措施。     

美国路特固抗水强基材料的室内试验研究及探讨

美国路特固（ROADGOOD）是一种高科技抗水强基材料。它通过电化学原理永久地将路基土壤的亲水性变为疏水性。同时提高土壤微粒之间的吸附力,使路基土壤更加容易充分压实,且免于日后雨水的长期侵害与破坏,极大地降低日后的维护费用。     

地下洞室中的地应力测量

地应力是隧道等岩石工程稳定性设计与评价的重要基础资料,在重大工程设计与施工前,需进行地应力测量,为工程设计提供依据。岩爆常发生在强度高、质地坚硬、干燥且较完整的弹脆性岩体中。采用水压致裂法测定地应力,在此基础上用Russense法、陶振宇法和Wet能量指数法综合预测隧道发生岩爆的几率,为工程开挖提供参考。     

客车车身外流场分析

运用ANSYS中的CFD功能建立了基于计算流体力学的车身空气动力特性有限元计算模型,对客车车身外流场进行了数值模拟,并分析了涡流产生的原因及对客车带来的影响,为客车车身设计提供了参考。     

Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization

Improving the efficiency of ship optimization is crucial for modern ship design. Compared with traditional methods, multidisciplinary design optimization (MDO) is a more promising approach. For this reason, Collaborative Optimization (CO) is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.     

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.