路面性能变化规律与检测时机的研究
发布时间：2019-06-07 10:53
【摘要】：在荷载和外界环境因素的综合影响下,随着时间的延长以及轴载次数的逐渐累积,其路面状况以及使用性能就会产生明显破坏。因此定周期检测,并不能满足路面变化对周期检测的要求,过于频繁的路面初期检测会造成人力和资源的大量损失,然而若因此对后期路面进行检测的次数大为减少则会造成养护的不及时性,继而产生不必要的安全隐患,会降低道路的使用寿命。从而,确定最合理、最适宜的检测时机成为路面养护工作人员必须解决的问题。本文结合路面因素的复杂性和不定性的特点,通过对高速公路多条道路的路面使用性能进行相关参数的回归分析,分析路面数据均值与方差之间的关系及其普遍规律,提出安全、规范的均值-方差建模方法,达到对路面使用性能及方差的预估这一目的,进而确定以方差作为基础的可靠性模型。以可靠性理论为核心,以收集的道路路面路况相关数据的概率分布方式为基础的检测周期优化技术,通过对比可靠度值和可靠度限值,最后得到检测高速公路相关检测指标的最适宜时机,使道路检测和路面性能的递变规律相符合,提高路面性能检测准确度。
[Abstract]:Under the comprehensive influence of load and external environmental factors, with the prolongation of time and the gradual accumulation of axial load times, the pavement condition and performance will be destroyed obviously. Therefore, fixed period detection can not meet the requirements of pavement change for periodic detection, too frequent initial pavement detection will result in a large number of human and resource losses. However, if the number of pavement detection in the later stage is greatly reduced, it will lead to the untimely maintenance, and then there will be unnecessary safety risks, which will reduce the service life of the road. Therefore, it is necessary for pavement maintenance staff to determine the most reasonable and appropriate detection time. Based on the complexity and uncertainty of pavement factors, this paper analyzes the relationship between the mean value and variance of pavement data and its general law by regression analysis of the pavement performance of multiple highways. A safe and standard mean-variance modeling method is proposed to predict the pavement performance and variance, and then the reliability model based on variance is determined. Based on the reliability theory and the probability distribution mode of the relevant data of road and pavement conditions, the detection cycle optimization technique is based on the comparison of reliability value and reliability limit value. Finally, the most suitable time to detect the relevant detection indexes of expressway is obtained, so that the road detection is in accordance with the changing law of pavement performance, and the accuracy of pavement performance detection is improved.
【学位授予单位】：河北工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U418.6
本文编号：2494738
[Abstract]:Under the comprehensive influence of load and external environmental factors, with the prolongation of time and the gradual accumulation of axial load times, the pavement condition and performance will be destroyed obviously. Therefore, fixed period detection can not meet the requirements of pavement change for periodic detection, too frequent initial pavement detection will result in a large number of human and resource losses. However, if the number of pavement detection in the later stage is greatly reduced, it will lead to the untimely maintenance, and then there will be unnecessary safety risks, which will reduce the service life of the road. Therefore, it is necessary for pavement maintenance staff to determine the most reasonable and appropriate detection time. Based on the complexity and uncertainty of pavement factors, this paper analyzes the relationship between the mean value and variance of pavement data and its general law by regression analysis of the pavement performance of multiple highways. A safe and standard mean-variance modeling method is proposed to predict the pavement performance and variance, and then the reliability model based on variance is determined. Based on the reliability theory and the probability distribution mode of the relevant data of road and pavement conditions, the detection cycle optimization technique is based on the comparison of reliability value and reliability limit value. Finally, the most suitable time to detect the relevant detection indexes of expressway is obtained, so that the road detection is in accordance with the changing law of pavement performance, and the accuracy of pavement performance detection is improved.
【学位授予单位】：河北工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U418.6
【参考文献】
相关期刊论文 前1条
1 乔立群;马震;郭楣;;高速公路沥青路面路况检测[J];黑龙江交通科技;2008年11期
，本文编号：2494738
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