scholarly journals Analysis and Determination of Axle Load Spectra and Traffic Input for Pavement Design

2021 ◽  
Vol 9 (8) ◽  
pp. 2536-2542
Author(s):  
Fayaz Rashid
Keyword(s):  
Pavement Design ◽  
Distribution Factor ◽  
Vehicle Class ◽  
Analysis And Design ◽  
Load Spectra ◽  
Traffic Characteristics ◽  
Gross Vehicle Weight ◽  
Monthly Distribution ◽  
Class Average ◽  
Hourly Distribution

Abstract: This study examines the vehicle class distribution, hourly distribution factors, weekly distribution factors, monthly distribution factors, axle load spectra for each vehicle class, and each axle of each vehicle class for the WIM station installed on the N-55 highway to aid analysis and design of new Mechanistic-Empirical Pavement Design Guide. The maximum, minimum and permissible load limit for the different vehicle class, average gross vehicle weight (GWV) and permissible load limits also being incorporated. The directional distribution for north bound and south bound traffic were observed to be almost 50% for both directions, except for 5 axle trucks which was 74% for north bound and 26% for south bound. The truck class most prevalent on the highway were identified to be 3-axle tandem truck (47.50%) and also it was observed that 94.1% of this vehicle class carried load above permissible limits. Keywords: Traffic characteristics, Load distribution factor, Axle Load Spectra.

Correlation-Based Clustering of Traffic Data for the Mechanistic–Empirical Pavement Design Guide

2013 ◽  
Vol 2339 (1) ◽  
pp. 104-111 ◽  
Author(s):  
Derong Mai ◽  
Rod E. Turochy ◽  
David H. Timm
Keyword(s):  
Pavement Design ◽  
Combination Method ◽  
Traffic Data ◽  
Vehicle Class ◽  
Number Of Clusters ◽  
Load Spectra ◽  
Design Guide ◽  
Data Clusters ◽  
Hourly Distribution

Development of traffic data clusters is crucial for use of the Mechanistic–Empirical Pavement Design Guide (MEPDG) when site-specific traffic data are not available and statewide data are too general. However, a preferred approach to traffic data clustering is not specified in the MEPDG. In current clustering practice, subjective decisions are made about issues such as determination of the number of clusters. This paper presents a new clustering combination method, correlation-based clustering, that considers the effects of traffic inputs on pavement design thicknesses, so that determination of the number of clusters is made objectively. For each traffic input required in the MEPDG, the similarity between two sites is evaluated with Pearson's correlation coefficient. Then, this approach evaluates the sensitivity of pavement design thickness to each traffic input to quantify locations to cut the hierarchical clustering trees, which objectively determines the number of clusters. The MEPDG requires many traffic inputs, including vehicle class distributions, four types of axle load spectra (per vehicle class), monthly and hourly distribution factors, and distributions of axle groups per vehicle. This clustering approach is performed for each traffic input so that a unique set of clusters can be developed for each traffic input. The method has been implemented for 22 direction-specific weigh-in-motion stations in Alabama to identify clusters of sites with similar estimated pavement performance for each traffic input of the MEPDG. This paper illustrates the clustering process for one traffic input (single-axle distribution) and presents clustering results for vehicle class distribution.

Evaluation of Clustered Traffic Inputs for Mechanistic-Empirical Pavement Design: Case Study in New Jersey

2019 ◽  
Vol 2673 (11) ◽  
pp. 332-348
Author(s):  
Abbas F. Jasim ◽  
Hao Wang ◽  
Thomas Bennert
Keyword(s):  
Cluster Analysis ◽  
New Jersey ◽  
Hierarchical Cluster ◽  
Pavement Design ◽  
Traffic Data ◽  
Truck Traffic ◽  
Vehicle Class ◽  
Site Specific ◽  
Load Spectra ◽  
Pavement Structures

Truck traffic is one of the significant inputs in design and analysis of pavement structures. This paper focuses on comprehensive cluster analysis of truck traffic in New Jersey for implementation of mechanistic-empirical pavement design. Multiple year traffic data were collected from a large number of weigh-in-motion stations across New Jersey. Statistical analysis was first conducted to analyze directional and temporal (yearly) variations of traffic data. Hierarchical cluster analysis was conducted and three optimum clusters were found for axle load spectra (single, tandem, tridem), vehicle class distribution, and axle/truck ratio, respectively. Road functional classifications were employed to identify different clusters as no common geographic trend could be perceived. The results illustrate that the predicted performance using the site-specific traffic data is comparable with that using the traffic cluster for the selected 10 sites. Among four different traffic inputs, the cluster traffic inputs generated the closest predictions of pavement life as compared with those using site-specific traffic input and the default traffic inputs yielded the highest error. It is recommended to use traffic clusters in mechanistic-empirical pavement design when site-specific data is unavailable.

Load Spectra Development for a Heavy Traffic Expressway

2011 ◽  
Vol 243-249 ◽  
pp. 4235-4239 ◽  
Author(s):  
Jin Cheng Wei ◽  
Cheng Yu ◽  
Shi Jie Ma
Keyword(s):  
Heavy Traffic ◽  
Truck Traffic ◽  
Heavy Load ◽  
Load Spectra ◽  
Traffic Characteristic ◽  
Bimodal Pattern ◽  
Monthly Distribution ◽  
Hourly Distribution ◽  
Second Peak

To study the traffic characteristic and axles load spectra of a heavy load expressway, WIM data was collected, axle configuration and vehicles classification was determined. The characteristic of truck traffic monthly distribution and the truck hourly distribution was studied and axle load spectra for major tuck types were developed. The results show that there were more than 30 truck types running on the expressway, the peaks of truck traffic monthly distribution was in April, September and December and the rush hour of the hourly distribution was at midnight. All major axles had a bimodal pattern of load spectra. The second peak of single axle was about 12 to 13 ton, tandem axle 14 to 22 ton and the tridem axle 28 to 32 ton. More than 50% of axles were overloading.

Impact of Various Trucks on Pavement Design and Analysis

2013 ◽  
Vol 2339 (1) ◽  
pp. 120-127
Author(s):  
Olga Selezneva ◽  
Aditya Ramachandran ◽  
Endri Mustafa ◽  
Regis Carvalho
Keyword(s):  
Pavement Design ◽  
Loading Conditions ◽  
Vehicle Class ◽  
Load Spectra ◽  
Heavy Trucks ◽  
Weigh In Motion ◽  
Wide Range ◽  
Design Guide

This investigation assessed the sensitivity of Mechanistic–Empirical Pavement Design Guide (MEPDG) outcomes to normalized axle load spectra representing various loading conditions observed in the Specific Pavement Studies Transportation Pooled Fund Study of the Long-Term Pavement Performance program. The goal was to determine what vehicle classes and axle types with a wide range of axle loading conditions are likely to cause differences in pavement design outcomes when the MEPDG is used. Significant differences found in the MEPDG outcomes support the need for characterization of axle loading beyond a single default value for heavy trucks that dominate vehicle class distributions, especially for Class 9 trucks. The absence of differences for lightweight and under-represented trucks indicates that load spectra from various sites could be combined to develop a single default for some vehicle classes and axle types. The effect of bias in weigh-in-motion (WIM) axle weight measurements on the normalized axle load spectra estimates and the associated MEPDG outcomes was also investigated. It was found that drift in WIM system calibration leading to a more than 5% bias in mean error between true and WIM-measured axle weight could lead to significant differences in MEPDG design outcomes. These results were used to develop recommendations for creating axle loading defaults for the MEPDG.

Study of Load Spectra of the Jaozhou Bay Expressway Based on MEPDG

2011 ◽  
Vol 97-98 ◽  
pp. 402-407
Author(s):  
Lan Ying Guo ◽  
Jin Cheng Wei ◽  
Shi Ping Cui
Keyword(s):  
Class Groups ◽  
Truck Traffic ◽  
Load Spectra ◽  
Traffic Characteristic ◽  
Bimodal Pattern ◽  
Monthly Distribution ◽  
Hourly Distribution

To study the traffic characteristic and axles load spectra of Jaozhou bay expressway, WIM data was collected, axle configuration and vehicles classification was determined. The characteristic of truck traffic monthly distribution and the truck hourly distribution was studied and axle load spectra for major tuck class groups were developed. The results show that there were more than 22 truck types running on the expressway, the peaks of truck traffic monthly distribution was in December and the rush hour of the hourly distribution was at noon. All major axles had a bimodal pattern of load spectra.

Use of Long-Term Pavement Performance Data to Develop Traffic Defaults in Support of Mechanistic-Empirical Pavement Design Procedures

2003 ◽  
Vol 1855 (1) ◽  
pp. 176-182 ◽  
Author(s):  
Weng On Tam ◽  
Harold Von Quintus
Keyword(s):  
Pavement Design ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Traffic Data ◽  
Design Procedures ◽  
Load Spectra ◽  
State Highway ◽  
Pavement Structures ◽  
Empirical Design

Traffic data are a key element for the design and analysis of pavement structures. Automatic vehicle-classification and weigh-in-motion (WIM) data are collected by most state highway agencies for various purposes that include pavement design. Equivalent single-axle loads have had widespread use for pavement design. However, procedures being developed under NCHRP require the use of axle-load spectra. The Long-Term Pavement Performance database contains a wealth of traffic data and was selected to develop traffic defaults in support of NCHRP 1-37A as well as other mechanistic-empirical design procedures. Automated vehicle-classification data were used to develop defaults that account for the distribution of truck volumes by class. Analyses also were conducted to determine direction and lane-distribution factors. WIM data were used to develop defaults to account for the axle-weight distributions and number of axles per vehicle for each truck type. The results of these analyses led to the establishment of traffic defaults for use in mechanistic-empirical design procedures.

scholarly journals Variability in Pavement Design

2015 ◽  
Vol 16 (2) ◽  
pp. 50-67 ◽  
Author(s):  
Paola Dalla Valle ◽  
Nick Thom
Keyword(s):  
Pavement Design ◽  
Pavement Performance ◽  
Design Parameters ◽  
Probability Density Distribution ◽  
Analysis And Design ◽  
The Subject ◽  
The Mean ◽  
Subgrade Modulus ◽  
Input Variables ◽  
The Uk

Abstract This paper presents the results of a review on variability of key pavement design input variables (asphalt modulus and thickness, subgrade modulus) and assesses effects on pavement performance (fatigue and deformation life). Variability is described by statistical terms such as mean and standard deviation and by its probability density distribution. The subject of reliability in pavement design has pushed many highway organisations around the world to review their design methodologies, mainly empirical, to move towards mechanistic-empirical analysis and design which provide the tools for the designer to evaluate the effect of variations in materials on pavement performance. This research has reinforced this need for understanding how the variability of design parameters affects the pavement performance. This study has only considered flexible pavements. The sites considered for the analysis, all in the UK (including Northern Ireland), were mainly motorways or major trunk roads. Pavement survey data analysed were for Lane 1, the most heavily trafficked lane. Sections 1km long were considered wherever possible. Statistical characterisation of the variation of layer thickness, asphalt stiffness and subgrade stiffness is addressed. A sensitivity analysis is then carried out to assess which parameter(s) have the greater influence on the pavement life. The research shows that, combining the effect of all the parameters considered, the maximum range of 15th and 85th percentiles (as percentages of the mean) was found to be 64% to 558% for the fatigue life and 94% to 808% for the deformation life.

Sign in / Sign up

Export Citation Format

Share Document