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基于路面加速加载的沥青路面疲劳损伤预估模型标定方法
引用本文:韩振强, 胡力群, 沙爱民. 基于路面加速加载的沥青路面疲劳损伤预估模型标定方法[J]. 交通运输工程学报, 2023, 23(4): 258-270. doi: 10.19818/j.cnki.1671-1637.2023.04.019
作者姓名:韩振强  胡力群  沙爱民
作者单位:1.长安大学 特殊地区公路工程教育部重点实验室,陕西 西安 710064;;2.长安大学 公路学院,陕西 西安 710064
基金项目:国家自然科学基金项目(52108393);;国家重点研发计划(2021YFB2601300);;中央高校基本科研业务费专项资金项目(300102211307)~~;
摘    要:为提高工程应用中沥青路面疲劳损伤预估模型的可靠性,提出了一种结合小型试件试验与足尺路面加速加载试验的模型标定方法;基于路面疲劳损伤发展特征分析,提出采用非线性增量递归法的沥青路面累积疲劳损伤分析方法,适用于疲劳损伤预估模型由小尺寸试验向足尺试验条件的转移与外推;基于小尺寸试件试验疲劳寿命预估模型构建了足尺沥青混合料层疲劳损伤预估模型,利用疲劳寿命预估模型转移方程实现足尺路面加速加载条件下的疲劳损伤预估;为确定模型转移方程,提出了基于路面加速加载试验的疲劳损伤标定方程,推导了疲劳损伤预估模型待定系数标定方法;利用重型荷载模拟器实施了级配碎石组合式基层沥青路面足尺试验路段加速加载试验,结合路面钻芯试样动态模量与四点弯曲疲劳试验,标定和验证了沥青混合料层的疲劳损伤预估模型。研究结果表明:非线性增量递归法可考虑材料非线性、性能衰减和加载历史对结构层疲劳损伤累积的影响,符合实际路面疲劳损伤发展规律;利用标定确立的疲劳损伤预估模型可以预测试验路不同加载区间沥青混合料层的累积疲劳损伤,50%和90%的预测值相对实测结果的误差分别小于3.1%和20.0%,表明该预估模型具有一定可靠性;提出的模型标定方法,可为基于路面加速加载试验的沥青路面疲劳损伤预估模型建立提供参考,为路面设计和养护维修决策提供更加可靠的性能预估模型。

关 键 词:路面工程   沥青路面   路面加速加载试验   疲劳分析方法   预估模型标定   转移方程
收稿时间:2023-02-21

Calibration method of asphalt pavement fatigue damage prediction model based on accelerated pavement test
HAN Zhen-qiang, HU Li-qun, SHA Ai-min. Calibration method of asphalt pavement fatigue damage prediction model based on accelerated pavement test[J]. Journal of Traffic and Transportation Engineering, 2023, 23(4): 258-270. doi: 10.19818/j.cnki.1671-1637.2023.04.019
Authors:HAN Zhen-qiang  HU Li-qun  SHA Ai-min
Affiliation:1. Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an 710064, Shaanxi, China;;2. School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China
Abstract:To improve the reliability of fatigue damage prediction model for asphalt pavements in engineering applications, a calibration method of the model was proposed based on the small-scale-specimen test and full-scale accelerated pavement test (APT). The evolution characteristics of asphalt pavement fatigue damage were analyzed to establish a cumulative fatigue damage analysis method by the nonlinear incremental recursive (NIR) method, which was suitable for the shift and extrapolation of the fatigue damage prediction model from small-scale test to full-scale test. The fatigue damage prediction model for the full-scale asphalt mixture layer was established based on the fatigue life prediction model obtained by small-scale specimen test, and the transfer equation of the fatigue life prediction model was used as the tool to predict the fatigue damage under the full-scale APT condition. In order to determine the model transfer equation, the fatigue damage calibration equation based on the APT was proposed, and the calibration methods of the undetermined coefficients were derived. The APT was carried out by the heavy vehicle simulator on the full-scale asphalt pavement test sections with graded-crushed stone composite base. The fatigue damage prediction model of asphalt mixture layer was calibrated and validated by integrating the dynamic modulus test and the four-points-bending-fatigue test results of the core samples drilled from the APT sections. Research results show that the NIR method can consider the impacts of material nonlinearity, performance decay, and loading history on the fatigue damage accumulation of asphalt pavement structure, conforming to the actual evolution law of the pavement fatigue damage. The calibrated fatigue damage prediction model can be used to predict the cumulative fatigue damages of the asphalt mixture layers of test sections in different loading intervals, and 50% and 90% of the predicted results have an error of less than 3.1% and 20.0% compared to the measured results, respectively, indicating that the calibrated prediction model has certain reliability. Therefore, the proposed model calibration method can be taken as a reference for the establishment of fatigue damage prediction model of asphalt pavement based on the APT and provide more reliable performance prediction models for the decision-making on pavement design and maintenance.
Keywords:pavement engineering  asphalt pavement  accelerated pavement test  fatigue analysis method  prediction model calibration  transfer equation
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