高等级沥青路面使用性能预测模型及预防护性养护措施研究

发布时间：2018-04-23 18:05

本文选题：高等级公路 + 沥青路面　；参考：《重庆交通大学》2015年硕士论文

【摘要】：随着我国经济的快速发展,高等级公路建设发展迅速,沥青路面得到了广泛的应用。近年来,沥青路面在各种自然因素及繁重的行车荷载的作用下,出现了沥青老化脱落、裂缝、松散等早期病害。这些早期损坏不仅影响路面行驶质量,而且还会缩短道路的使用寿命,增加养护费用,形成不良的社会影响,沥青路面养护问题日益突显。本论文以沥青路面为研究对象,从沥青路面的早期破坏出发,系统研究了沥青路面使用性能和预养护技术。论文主要研究内容体现在以下几个方面。本论文详细的分析了沥青路面出现的早期损坏现象及其产生原因,探讨了路面破损等级,阐述了高等级公路沥青路面路况数据采集方法、仪器设备及采集频率,综合分析了路面使用性能单项评价指标。以贵州省都新高速为依托工程,进行了路面早期病害调查,通过对数据的整理分析,发现车辙、裂缝、表面破损(麻面、坑槽)这三种破坏为主要病害类型。在对沥青路面使用性能各单项指标评价分析的基础上,结合路面主要病害类型,提出了适用于高等级公路沥青路面的使用性能评价指标体系。根据路面使用性能特点,从路面破损状况、路面平整度、路面抗滑性能、路面行驶质量、路面结构承载力等五方面综合评价路面使用性能。由于每项因素对路面使用性能的影响程度不一样,针对依托工程的病害特点,利用加权平均值法,改进了路面使用性能综合评价的方法,综合体现了各因素对路面使用性能的影响程度。车辙影响路面使用性能,具有容易观察、测量等特点,是公路养护评价经常采用的评价指标之一。目前我国一些道路养护数据缺乏,而灰度理论具有原始数据少、计算简单、预测精度高的特点,因此本文以灰色度为理论基础,建立了路面车辙近期的灰色预测模型,并对都新高速的车辙进行了预测,预测误差较小,精度较高。由于灰度理论预测近期数据精度高,远期数据精度低,结合RBF混沌神经网络逼近能力和分类能力,建立了混沌神经网络模型远期车辙预测模型,为预防护性养护措施的选择提供了数据支撑。对沥青路面预防性养护技术进行了分析,重点研究了薄层和超薄沥青混凝土添加纤维对路面使用性能的增加及作用机理。
[Abstract]:With the rapid development of China's economy and the rapid development of high-grade highway construction, asphalt pavement has been widely used. In recent years, asphalt pavement, under the action of various natural factors and heavy traffic load, appeared the early diseases such as asphalt aging and falling off, crack, loose and so on. These early damages not only affect the running quality of the pavement, but also shorten the service life of the road, increase the maintenance cost and form the bad social influence. The problem of asphalt pavement maintenance is becoming more and more serious. This paper takes asphalt pavement as the research object, starting from the early damage of asphalt pavement, systematically studies the performance and pre-maintenance technology of asphalt pavement. The main contents of this paper are reflected in the following aspects. In this paper, the early damage phenomenon of asphalt pavement and its causes are analyzed in detail, the pavement damage grade is discussed, and the data acquisition method, equipment and frequency of high grade highway asphalt pavement are described. The single evaluation index of pavement performance is analyzed synthetically. Based on the project of Duxin Expressway in Guizhou Province, the early disease investigation of pavement was carried out. Through the analysis of data, it was found that rutting, crack and surface damage (hemp surface, pothole) were the main types of diseases. Based on the evaluation and analysis of each single index of asphalt pavement performance and combined with the main types of pavement diseases, a performance evaluation index system suitable for high grade highway asphalt pavement is put forward. According to the characteristics of pavement performance, the pavement performance is comprehensively evaluated from five aspects: pavement breakage, pavement smoothness, pavement anti-skid performance, pavement driving quality, pavement structure bearing capacity and so on. Because each factor has different influence on pavement performance, according to the disease characteristics of relying engineering, the method of weighted average value is used to improve the comprehensive evaluation method of pavement performance. The influence of various factors on pavement performance is reflected comprehensively. Rutting affects the performance of pavement and has the characteristics of easy observation and measurement. It is one of the evaluation indexes often used in highway maintenance evaluation. At present, some road maintenance data are lacking in our country, but gray scale theory has the characteristics of little original data, simple calculation and high prediction precision. Therefore, based on the grey degree theory, this paper establishes the grey prediction model of pavement rutting in the near future. The rut of Duxin high speed is forecasted. The prediction error is small and the precision is high. Because of the high precision of short-term data and low precision of long-term data in gray level theory, combined with the ability of RBF chaotic neural network to approach and classify, the long-term rut prediction model of chaotic neural network model is established. It provides data support for the choice of protective maintenance measures. The preventive maintenance technology of asphalt pavement is analyzed, and the increase of pavement performance and its mechanism of adding fiber to thin and ultra-thin asphalt concrete are studied.
【学位授予单位】：重庆交通大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U416.217;U418.6

【参考文献】