汽车的力学参数与稳态转向特性

本文以汽车力学参数试验台测出的数据为基础，为通过建立轮胎侧偏角，车厢侧倾角，回正力矩与侧向加速度之间的关系，运用最小二乘法对测试数据进行线性曲线拟合，找出了这些数据与汽车记转向特性之间的关系，并按国家标准ＧＢ／Ｔ３０４７－９１中的有关规定对被测试车辆的稳态转向特性进行了评价 。     

深圳市“5.24”暴雨洪水灾后的反思

1998年5月24日，深圳市暴雨成灾，经济损失很大。本文总结和分析了暴雨洪水的特性及其成灾的原因，提出防洪减灾的措施和建议。     

大跨度钢管混凝土拱桥地震反应分析

利用有限元法分析大跨度钢管混凝土拱桥的地震反应，考虑了剪切变形的影响，用子空间迭代法分析了其振动规律和动力特性，应用反应谱法和时程分析法分析了该拱桥的地震反应，得出了一些有价值的结论。     

公路桥梁高承台桩基沉降分析研究

桩基的沉降计算是桩基工程设计不可忽视的关键问题之一。为探讨高承台桩基沉降特性，基于荷载传递法提出一种高承台桩基沉降计算方法，并采用多组不同参数对某高承台群桩的沉降特性进行了比较计算和深入分析，得到了多个影响因素下群桩沉降的变化规律。     

斜拉索对钢管混凝土桁式组合拱桥力学性能的影响

位于湖南省永连公路上的天子山大桥是目前世界上第一座钢管混凝土桁式组合拱桥,其设计上的一个创新是用斜拉索代替了原来的刚性斜杆.制作了天子山大桥1∶20的铝合金模型,用模态分析的方法对钢管混凝土桁式组合拱桥的动力特性进行了研究,探讨了斜拉索对钢管混凝土桁式组合拱桥力学性能的影响.     

预应力混凝土先简支后连续梁静、动力试验研究

简要介绍预应力混凝土先简支后连续梁1∶5模型设计、试验方法。根据弹性试验、开裂试验和破坏试验结果,研究分析该类结构的工艺特点、正常使用荷载作用下结构受力变形特征、结构特别是湿接缝截面的抗裂性、开裂区域裂缝发展分布规律以及结构极限承载力。在对模型梁体系转换前、后和先简支后连续梁破坏前、后动力测试与理论分析的基础上,校核模型梁的刚度、制作精度,最后初步探讨了先简支后连续梁的动力性能。     

振荡压实对桥梁结构物的影响研究

振荡压实是利用在材料厂中产生的快速交变剪应力使材料的颗粒重新排列而变得更加密实。通过振荡压实与振动压实对桥梁结构的振动的对比试验，分析了这2种作业方式对桥梁结构物的影响．论证了振荡压路机在桥梁压实中的优越性。     

西南红层软岩地区铁路滑坡形成机理分析及治理措施

红层软岩地区铁路工程建设中滑坡问题突出。本文结合西南地区一铁路红层滑坡治理工程,利用该地区红层滑坡的现场调查结果,从红层软岩特性、地层岩性、地质地貌、水文地质条件、深孔位移监测数据等多方面对该地区铁路滑坡形成机理进行分析,并论述红层滑坡与一般滑坡的不同点,分析红层软岩岩体力学特性、地层主要成分以及红层软岩中的粉砂质泥岩在不同风化程度下的力学特性。根据分析结果提出治理措施,并对治理措施进行评价。总结得出地层岩性、地质水文、人为因素是导致红层滑坡病害发生的主要因素。     

Changes in fuel economy: An analysis of the Spanish car market

This paper estimates the role that technological change and car characteristics have played in the rate of fuel consumption of vehicles over time. Using data from the Spanish car market from 1988 to 2013, we estimate a reduced form equation that relates fuel consumption with a set of car characteristics. The results for the sales-weighted sample of vehicles show that energy efficiency would have improved by 30% and 42% for petrol and diesel cars respectively had car characteristics been held constant at 1988 values. However, the shift to bigger and more fuel-consuming cars reduced the gains from technological progress. Additionally, using the results of the fuel equation we show that, besides a natural growth rate of 1.1%, technological progress is affected by both the international price of oil and the adoption of mandatory emission standards. Moreover, according to our estimations, a 1% growth in GDP would modify car characteristics in such a way that fuel consumption would increase by around 0.23% for petrol cars and 0.35% for diesel cars.     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.