Calibration method of asphalt pavement fatigue damage prediction model based on accelerated pavement test
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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。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.
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图 1 线性与非线性路面结构累积损伤计算方法对比
Figure 1. Comparison of linear and nonlinear cumulative damage calculation methods for pavement structures
图 2 基于足尺APT的沥青混合料层疲劳损伤预估模型标定方法
Figure 2. Calibration method of asphalt mixture layer fatigue damage prediction model based on full-scale APT
图 3 标定确立的沥青混合料层疲劳损伤预估模型应用方法
Figure 3. Application method of calibrated asphalt mixture layer fatigue damage prediction model
图 5 足尺路面加速加载试验设备、加载和FWD测试状况
Figure 5. Full-scale accelerated pavement test facilities, loading and FWD testing condition
图 6 疲劳损伤标定模型待定系数拟合结果
Figure 6. Fitting results of undetermined coefficients of fatigue damage calibration model
图 7 S1与S2在不同加载区间沥青混合料层疲劳损伤发展历史
Figure 7. Fatigue damage evolution histories of asphalt mixture layers for S1 and S2 in different loading intervals
图 8 疲劳损伤标定模型待定系数分析结果
Figure 8. Analysis results of undetermined coefficient for fatigue damage calibration model
图 9 不同加载区间累积疲劳损伤预测值与实测值
Figure 9. Predicted and measured values of cumulative fatigue damage in different loading intervals
图 10 累积疲劳损伤预测误差累积概率分布
Figure 10. Cumulative probability distribution of cumulative fatigue damage prediction error
表 1 HVS加速加载试验加载时间与加载区间划分
Table 1. Loading time and loading interval division of HVS accelerated pavement test
加载区间 加载时间 加载次数/104 轴载/kN S1-1/ S2-1 2018-02-26~2018-03-19 0~29 50 S1-2/S2-2 2019-08-06~2019-08-18 30~43 40 S1-3/S2-3 2019-08-18~2019-09-11 44~70 50 S1-4/S2-4 2019-09-11~2019-09-16 71~75 40 S1-5/S2-5 2019-09-17~2019-09-20 76~80 55 S1-6/S2-6 2019-09-21~2019-09-25 81~85 60 S1-7/S2-7 2019-09-26~2019-09-29 86~90 70 
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表 2 疲劳损伤模型标定与归一化应变能计算结果
Table 2. Calculation results of fatigue damage model calibration and normalized strain energy
加载区间 ai b R2 Ei/MPa εi/10-6 Ui S1-1 0.004 0 0.366 7 0.206 3 36 901 73.3 11.95 S1-2 0.003 4 0.366 7 0.791 8 23 410 98.6 13.71 S2-1 0.003 3 0.366 7 0.359 9 38 453 68.8 10.96 S2-2 0.002 4 0.366 7 0.233 9 23 600 79.5 8.99
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表 3 疲劳损伤标定模型拟合与归一化应变能计算结果
Table 3. Calculation results of fatigue damage calibration model regression and normalized strain energy
加载区间 ai b R2 εi/10-6 Ei/MPa Ui S1-3 0.004 4 0.366 7 0.782 1 103.2 21 925 14.05 S1-4 0.003 6 0.366 7 0.946 9 92.7 22 629 11.71 S1-5 0.004 2 0.366 7 0.800 8 107.6 19 339 13.48 S1-6 0.004 6 0.366 7 0.914 8 110.3 20 393 14.94 S1-7 0.004 9 0.366 7 0.904 7 120.9 18 301 16.12 S2-3 0.004 2 0.366 7 0.651 6 109.9 20 758 15.11 S2-4 0.003 0 0.366 7 0.859 4 88.7 21 990 10.42 S2-5 0.004 2 0.366 7 0.966 3 107.5 20 083 13.97 S2-6 0.004 7 0.366 7 0.855 2 102.6 24 249 15.38 S2-7 0.004 6 0.366 7 0.984 9 110.4 21 692 15.92
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