ABSTRACT

Reported in this paper is an evaluation of structural and functional performance of heavy duty and light duty pavements by addressing the effects of several factors on the rate of pavement deterioration. The sample used herein is a network of freeways and arterials from Victoria/Australia covering four road classes, namely M, A, B and C. The first two road classes represent heavy duty pavement and the last two road classes represent light duty pavements. These road classes differ in qualities, functions, duties, geometric standards, and traffic volumes and loadings. This paper aims to assess the performance of the four road classes by comparing their pavement conditions and environmental factors affecting the rate of pavement deterioration. More than 2300 kilometers of road length was selected from 40 highway sections to reflect Victoria’s network conditions. The parameters considered for assessing road performance were traffic loading, pavement strength, and environmental factors. Hierarchical linear regression analysis was used to explore the significance of different predictor variables for all road classes. The study confirms that the rate of roughness and rutting progression per year for all road classes is very close, and the environmental factors affect roughness and rutting progressions for light duty pavements only.

摘要

本文报告了通过解决几个因素对路面劣化率的影响来评估重型和轻型路面的结构和功能性能。这里使用的示例是来自维多利亚/澳大利亚的高速公路和主干道网络，涵盖四个道路等级，即 M、A、B 和 C。前两个道路等级代表重型路面，后两个道路等级代表轻型路面。这些道路等级在质量、功能、职责、几何标准以及交通量和负载方面有所不同。本文旨在通过比较它们的路面条件和影响路面劣化率的环境因素来评估四种道路等级的性能。从 40 个高速公路路段中选择了超过 2300 公里的道路长度，以反映维多利亚州的网络状况。评估道路性能所考虑的参数是交通负荷、路面强度和环境因素。分层线性回归分析用于探索所有道路类别的不同预测变量的重要性。研究证实，所有道路等级每年的粗糙度和车辙进展率都非常接近，环境因素仅影响轻型路面的粗糙度和车辙进展。分层线性回归分析用于探索所有道路类别的不同预测变量的重要性。研究证实，所有道路等级每年的粗糙度和车辙进展率都非常接近，环境因素仅影响轻型路面的粗糙度和车辙进展。分层线性回归分析用于探索所有道路类别的不同预测变量的重要性。研究证实，所有道路等级每年的粗糙度和车辙进展率都非常接近，环境因素仅影响轻型路面的粗糙度和车辙进展。