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NCHRP Report 669: Models for Predicting Reflection Cracking of Hot-Mix Asphalt Overlays (2010)
National Cooperative Highway Research Program (NCHRP)

Citation Manager

Zhou, Fujie, Lytton, Robert L, Hu, Sheng, Luo, Rong, Tsai, Fang-Ling, Lee, Sang Ick, Transportation Research Board. "Categorizing Traffic Load." NCHRP Report 669: Models for Predicting Reflection Cracking of Hot-Mix Asphalt Overlays. Washington, DC: The National Academies Press, 2010.

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Front Matter (R1-R11)
Organization of the Report (1-1)
Material Properties (2-2)
Calibration to Field Data (3-3)
Use in Design (4-4)
Available Reflection Cracking Models (5-5)
Selection of a Reflection Cracking Model (6-6)
Process of Constructing a Calibrated Reflection Cracking Model (7-7)
Collection of Pavement Structure Data (8-9)
Traffic Data Collection (10-10)
Axle Load Distribution Factor (11-12)
Categorizing Traffic Load (13-13)
Finite Element Method for Calculating SIF (14-16)
Method of Predicting SIF (17-18)
Modeling of Cumulative Axle Load Distribution (19-19)
Probability Density on Tire Patch Length (20-25)
Reflection Cracking Amount and Severity Model (26-26)
Calibration of Field Reflection Cracking Model (27-27)
System Identification Process (28-28)
Parameter Adjustment and Adaption Algorithm (29-29)
Calibrating Reflection Cracking Model of Test Sections (30-32)
Heat Transfer in Pavement (33-33)
The Bottom Boundary Condition (34-34)
Stiffness, Tensile Strength, Compliance, and Fracture Properties of Mixtures (35-35)
Artificial Neural Network Algorithms for Witczak's Complex Modulus Models (36-37)
Models of Paris and Erdogan's Law Fracture Coefficients A and n (38-38)
Computational Method for Crack Growth Due to Traffic (39-40)
Computational Method for Viscoelastic Thermal Stresses (41-41)
Computation-to-Field Calibration Coefficients (42-43)
Validation of the Calibration Coefficients (44-47)
Mechanistic Prediction of Crack Growth (48-48)
Calibration of Calculated Overlay Life to the Observed Distress (49-49)
Predictions of Overlay Reflection Cracking (50-54)
Calibration of the Computational Model to Field Data (55-55)
Suggested Research (56-57)
References (58-59)
Appendices (60-60)
Abbreviations used without definitions in TRB publications (61-61)

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13 25 Vehicle class 4 Axle Load Distribution (%) 20 Vehicle class 5 Vehicle class 6 15 Vehicle class 7 10 5 0 0 10000 20000 30000 40000 50000 Axle Load (lb) Figure 7. Annual normalized single axle load distribution for vehicle class 4 to 7 (LTPP Section 180901 in 2004). Weight Tables in which WIM data are typically reported for axles, and each axle has single or dual tires. Table 7 lists the vehicle classes 4, 5, 6, and 7 for LTPP test section 180901 in number of axles for each axle type and vehicle class. 2004. Figure 7 shows the annual normalized single axle load All axles of vehicle classes 4 and 5 and single axles of class distribution calculated using the data in Table 6. 6 and 7 vehicles have single tires while the others have dual tires. Thus the matrix of vehicle class and axle types can be categorized according to the number of tires. When the steer- Categorizing Traffic Load ing and non-steering axles are put together in the single axle In this study, the traffic loads were categorized based on the type, the matrix can be characterized into eight categories vehicle class, axle type, and number of tires to facilitate the based on the vehicle class, the axle type, and the number of analysis of load effects for reflection cracking. Every truck in tires. The total number of axle loads for each category was each vehicle class has single, tandem, tridem, or/and quadrem used to determine the axle load distribution factor for the Table 7. Number of axles for each vehicle class. Number of Axles Vehicle Class Single* Tandem Tridem Quadrem 4 1 1 5 2 (1) 6 1 1 7 1 1 8 3 (2) 9 1 2 10 1 1 1 11 5 (4) 12 4 (3) 1 13 3 (2) 2 The number of nonsteering single axles are shown in parentheses. Shaded areas are vehicle classes that use single tires; unshaded areas are vehicle classes that use dual tires.