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Suggested Citation:"Appendix H. Categorization of Traffic Loads State in the Faulting Model." National Academies of Sciences, Engineering, and Medicine. 2019. Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance. Washington, DC: The National Academies Press. doi: 10.17226/25583.
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Suggested Citation:"Appendix H. Categorization of Traffic Loads State in the Faulting Model." National Academies of Sciences, Engineering, and Medicine. 2019. Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance. Washington, DC: The National Academies Press. doi: 10.17226/25583.
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Suggested Citation:"Appendix H. Categorization of Traffic Loads State in the Faulting Model." National Academies of Sciences, Engineering, and Medicine. 2019. Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance. Washington, DC: The National Academies Press. doi: 10.17226/25583.
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Suggested Citation:"Appendix H. Categorization of Traffic Loads State in the Faulting Model." National Academies of Sciences, Engineering, and Medicine. 2019. Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance. Washington, DC: The National Academies Press. doi: 10.17226/25583.
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Suggested Citation:"Appendix H. Categorization of Traffic Loads State in the Faulting Model." National Academies of Sciences, Engineering, and Medicine. 2019. Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance. Washington, DC: The National Academies Press. doi: 10.17226/25583.
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Suggested Citation:"Appendix H. Categorization of Traffic Loads State in the Faulting Model." National Academies of Sciences, Engineering, and Medicine. 2019. Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance. Washington, DC: The National Academies Press. doi: 10.17226/25583.
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H-1 Appendix H. Categorization of Traffic Loads State in the Faulting Model The traffic input for the faulting model is the number of axles corresponding to different load levels. The weigh-in-motion (WIM) data can be used to determine the traffic input. The WIM tables present the number of axles by types of axles within vehicle classes at various load levels. The WIM data can be directly collected from the LTPP database. If the data are unavailable in the database, it can be estimated with annual average daily truck traffic (AADTT) and axle load distribution. The axle distributions shown in WIM tables are required to be categorized into the groups (single and tire) in the light of the determination of stress state. The details of the category of axle distribution and the approach to estimate the number of axles with AADTT are elaborated as follows. CATEGORY OF AXLE DISTRIBUTION The vehicle classes are classified by FHWA into 13 classes relying upon whether they convey travelers or products as presented in Table H.1. Load-related distresses are mostly caused by heavy load groups including the vehicle classes 4 to 13 rather than light axle load groups of vehicle classes 1 to 3. Therefore, the heavy axle groups are taken into consideration in this study for the following determination of the traffic input in the load-related faulting model. Table H.1. FHWA Vehicle Classification (30). Vehicle Class Schema Description 4 Buses 5 Two-axle, single-unit trucks 6 Three-axle single-unit trucks 7 Four- or more than four-axle single-unit trucks 8 Four- or less than four-axle single trailer trucks 9 Five-axle single trailer trucks 10 Six- or more than six-axle single trailer trucks 11 Five- or less than five-axle multi-trailer trucks 12 Six-axle multitrailer trucks 13 – Seven- or more than seven-axle multitrailer trucks Table H.2 presents the load intervals for each axle type including single, tandem, tridem, and quadrem. The various stress levels in the second faulting model can be obtained from the

H-2 axle load intervals for axle types so that the stress terms ( and ) are determined according to the different axle loads. The same axle loads for each axle type should be grouped together to provide continuous and non-repeating load levels. In other words, the axle load distributions for each axle type should be firstly concatenated together and then grouped into the unique load level. This procedure is schematically depicted in the Figure H.1. For example, the load level of 12,000 lb is contained in each load distribution table for four axle types (single, tandem, tridem, and quad). When concatenating the four load distribution tables, the load level of 12,000 lb is repeated four times so that the next step is to group the corresponding distribution (%) together to provide a non-repeating load level. Thus the continuous and non-repeating load levels were determined for determination of stress term ( and ). Table H.2. Load Intervals for Each Axle Type (30). Axle Type Axle Load Interval Single Axles 3,000 ~ 40,000 lb at 1,000 lb intervals Tandem Axles 6,000 ~ 80,000 lb at 2,000 lb intervals Tridem Axles 12,000 ~ 102,000 lb at 3,000 lb intervals Quadrem Axles

H-3 Figure H.1. Schematically Conversion Process to Continuous and Non-repeating Load Distribution. According to the number of tires, the load distributions are categorized into eight groups as shown in Figure H.2. The single axles of vehicle classes 4 to 7 have single tires and the others have dual tires. The rest of the axles including tandem, tridem, and quadrem of vehicle classes 4 to 5 have single tire and others have dual tires. Therefore, the number of tires (single and dual tire) form a matrix of vehicle classes and axle types into two major categories as shown in the gray and white areas of Figure H.2.

H-4 Figure H.2. Categories of Traffic Loads (30). ESTIMATION OF ANNUAL NUMBER OF AXLE WITH AADTT The AADTT is the annual average 24-hour volume of traffic passing through a specified section of highway and can be obtained from the LTPP database. The truck traffic in the AADTT includes the heavy traffic of vehicle classes 4 to 13. The AADTT was adopted to be converted to the number of axle loads if the WIM data are unavailable in some LTPP sections. In order to convert the AADTT to annual number of axle loads for each vehicle class and axle type, two truck-traffic adjustment factors are needed including normalized vehicle class distribution and number of axles per truck. Normalized Vehicle Class Distribution The vehicle class distribution represents the percent of the AADTT for each vehicle for the base year and is normalized for truck vehicle classes 4 to 13. The summation of the normalized distribution factors of truck classes must be 100. Table H.3 presents the default value of distribution factors obtained from the literature (30), and these distribution factors are assumed to be constant every year. The annual number of truck for each vehicle class with a base year is determined by: 365 (H.1) where k is a specific vehicle class (class 4 to 13); is annual number of trucks for a vehicle class, k; is normalized vehicle class distribution percentage for a truck class, k.

H-5 Table H.3. Normalized Vehicle Class Distribution Factor. Vehicle Class Distribution Factor (%) 4 1.8 5 24.6 6 7.6 7 0.5 8 5.0 9 31.3 10 9.8 11 0.8 12 3.3 13 15.3 Number of Axle Type per Vehicle The number of axle types is the average number of individual axles within each vehicle class and axle type including single, tandem, tridem, and quadrem. The default values of the average number of axles for each truck class is predicted depending upon the LTPP traffic data, as shown in Table H.4. The number of axle loads for each axle type with each truck class can be determined by: (H.2) where is a specific axle type (single, tandem, tridem, or quad); is the annual number of axle loads for an axle type in a vehicle class; is the average number of axles by axle type for each truck class. In this way, the number of axles for each axle type and truck class are determined by the AADTT and default axle load spectra, so the total number of axles can be obtained.

H-6 Table H.4. Average Number of Axles for Each Vehicle Class. Vehicle Class Single Axle Tandem Axle Tridem Axle Quadrem Axle 4 1.62 0.39 0.00 0.00 5 2.00 0.00 0.00 0.00 6 1.02 0.99 0.00 0.00 7 1.00 0.26 0.83 0.00 8 2.38 0.67 0.00 0.00 9 1.13 1.93 0.00 0.00 10 1.19 1.09 0.89 0.00 11 4.29 0.26 0.06 0.00 12 3.52 1.14 0.06 0.00 13 2.15 2.13 0.35 0.00 In summary, the WIM data are used to determine traffic inputs of number of axles by rearranging them into two main categories of single and dual tires by various axle load levels for all axle types. When the WIM data are unavailable in some LTPP sections, the AADTT is converted into number of axle loads within each vehicle class and axle type at various axle load levels. In order to perform this conversion, the axle load distributions are required, which indicate the percentage of the number of axles within axle load levels by axle types of vehicle classes. The WIM data at the same section in recent years or in adjacent sections are used to determine the axle load distribution. The total number of axles determined by the AADTT and default axle load spectra and the axle load distribution determined by available WIM tables are used for conversion from AADTT to WIM data.

Next: Appendix I. Subgrade Subroutine for Flexible and Rigid Pavements »
Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance Get This Book
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The performance of flexible and rigid pavements is known to be closely related to properties of the base, subbase, and/or subgrade. However, some recent research studies indicate that the performance predicted by this methodology shows a low sensitivity to the properties of underlying layers and does not always reflect the extent of the anticipated effect, so the procedures contained in the American Association of State Highway and Transportation Officials’ (AASHTO’s) design guidance need to be evaluated.

NCHRP Web-Only Document 264: Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance proposes and develops enhancements to AASHTO's Pavement ME Design procedures for both flexible and rigid pavements, which will better reflect the influence of subgrade and unbound layers (properties and thicknesses) on the pavement performance.

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