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首页> 外文期刊>Journal of materials in civil engineering >Prediction Model for Field Rut Depth of Asphalt Pavement Based on Hamburg Wheel Tracking Test Properties
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Prediction Model for Field Rut Depth of Asphalt Pavement Based on Hamburg Wheel Tracking Test Properties

机译：基于汉堡轮跟踪试验特性的沥青路面现场车辙深度预测模型

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摘要

The Hamburg wheel tracking (HWT) test has been found to be a promising test to evaluate the field rutting performance of asphalt pavements and has been implemented as a material screening test during the mix design process by several state departments of transportation. However, the rutting performance of an asphalt pavement depends not only on the material properties, but also on many other factors such as pavement structure and traffic. To date, there are few performance models that have integrated the Hamburg rutting parameters for pavement rutting prediction. In addition, mechanistic-empirical-based prediction models have been found to have some difficulties in reasonably predicting field rut depth, especially when field variables and confounding factors have to be considered. Therefore, the objective of this paper is to evaluate the relationship between the HWT test results and the field rut depth, then develop a predictive model for field rut depth based on the HWT test results. Field projects consisting of 51 hot mix asphalt (HMA) and warm mix asphalt (WMA) pavements were included in the analysis. These projects were located in different climatic zones with varying traffic levels, pavement structures, and material properties. Through direct correlation, it was found that the field rut depth in general decreased with the increase of the rutting resistance index (RRI). However, HWT test results alone do not have a strong relationship with the field rut depth, and many other factors, such as climate and pavement structure, have to be considered. Further, statistical-based methods in conjunction with engineering interpretation were applied to identify critical influencing factors and develop a prediction model for field rut depth. The developed rutting predictive model indicated that (a) mixture property (rutting resistance index, a parameter developed based on the HWT test), pavement age (month), average annual daily truck traffic (AADTT), and pavement structure (total HMA thickness and overlay thickness) are critical influencing factors for field rut depth; (b) RRI, along with pavement age and traffic data, has the most significant effect on rut depth among the identified five key predictor variables; (c) no significant differences are observed between prediction results of HMA and WMA mixtures, and thus the prediction model can be applied for both; and (d) using the developed predictive model, the effect of the HWT RRI can be considered comprehensively with other factors including climate, traffic, and pavement structure to determine the suitability of a designed asphalt mixture for pavement construction.
机译：汉堡车轮跟踪（HWT）测试被认为是评估沥青路面现场车辙性能的有前途的测试，并已在多个州交通部门作为混合料设计过程中的材料筛选测试。但是，沥青路面的车辙性能不仅取决于材料性能，还取决于许多其他因素，例如路面结构和交通状况。迄今为止，很少有能将汉堡车辙参数用于路面车辙预测的性能模型。另外，已经发现基于机械经验的预测模型在合理地预测车辙深度方面存在一些困难，特别是在必须考虑田间变量和混杂因素的情况下。因此，本文的目的是评估HWT测试结果与车辙深度之间的关系，然后基于HWT测试结果建立车辙深度的预测模型。分析中包括由51个热拌沥青（HMA）和温拌沥青（WMA）路面组成的现场项目。这些项目位于不同的气候区，具有不同的交通水平，人行道结构和材料特性。通过直接相关，发现随着车辙阻力指数（RRI）的增加，车辙深度通常减小。但是，仅HWT测试结果与车辙深度没有密切关系，必须考虑许多其他因素，例如气候和人行道结构。此外，将基于统计的方法与工程解释结合起来，用于确定关键的影响因素，并开发出车辙深度的预测模型。建立的车辙预测模型表明：（a）混合料性能（车辙阻力指数，基于HWT试验开发的参数），路面使用年限（月），卡车的年平均日行车流量（AADTT）和路面结构（HMA总厚度和覆盖厚度）是影响车辙深度的关键因素； （b）在确定的五个关键预测变量中，RRI以及行人年龄和交通数据对车辙深度影响最大； （c）HMA和WMA混合物的预测结果之间没有观察到显着差异，因此可以将预测模型应用于两者； （d）使用开发的预测模型，可以将HWT RRI的影响与其他因素（包括气候，交通和人行道结构）进行综合考虑，以确定所设计的沥青混合料是否适合人行道施工。

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