Case Study on the Effect of Recycled Asphalt Layer Parameters on the Bearing Capacity of the Pavement

A. Zariņš
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引用次数: 3

Abstract

Numerous ways to use recycled asphalt (RA) in the road base course will provide both environmental and economic benefits, allowing to recycle and utilise this initially waste material in road or pavement reconstruction projects. However, the properties and parameters of RA necessary for the application of reclaimed asphalt pavement (RAP) in a new pavement structure in most cases are not detectable in the design stage, which complicates design and construction process. The purpose of this paper is to study possibilities for evaluating the performance and parameters of RA, as well as to review the possibilities, methods and applications for RA testing. Data for this case study were obtained from recently completed road structures in the form of FWD measurements, together with lab explored parameters of drilled pavement cores. Based on that data, the relationships between the main pavement structural parameters, such as modulus on the surface of the pavement, compressive strength of RA core segment, thickness of bound layers and back calculated modulus were examined. On the way to exploring different analytical approaches, two approximation models were developed and compared, using the obtained data: by directly approximating the obtained data and after processing them with artificial neural network (ANN).
再生沥青层参数对路面承载力影响的实例研究
在道路基层中使用再生沥青(RA)的多种方法将提供环境和经济效益,允许在道路或路面重建项目中回收和利用这些最初的废料。然而,在新路面结构中应用再生沥青路面所需的RA性能和参数在设计阶段大多无法检测到,这使得设计和施工过程变得复杂。本文的目的是研究RA的性能和参数评价的可能性,以及RA测试的可能性、方法和应用。本案例研究的数据以FWD测量的形式从最近完成的道路结构中获得,以及实验室探索的钻孔路面岩心参数。在此基础上,研究了路面表面模量、RA芯段抗压强度、粘结层厚度等主要路面结构参数与反算模量之间的关系。在探索不同的分析方法的过程中,利用获得的数据建立了两种近似模型,并进行了比较:直接近似和人工神经网络(ANN)处理后的近似模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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