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.

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.

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 Creep Behavior of Passive Bovine Extraocular Muscle

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Lawrence Yoo ◽  
Hansang Kim ◽  
Andrew Shin ◽  
Vijay Gupta ◽  
Joseph L. Demer
Keyword(s):  
Relaxation Function ◽  
Loading Rate ◽  
Creep Function ◽  
Testing Conditions ◽  
Loading Rates ◽  
Creep Coefficient ◽  
Wide Range ◽  
The Mean

This paper characterized bovine extraocular muscles (EOMs) using creep, which represents long-term stretching induced by a constant force. After preliminary optimization of testing conditions, 20 fresh EOM samples were subjected to four different loading rates of 1.67, 3.33, 8.33, and 16.67%/s, after which creep was observed for 1,500 s. A published quasilinear viscoelastic (QLV) relaxation function was transformed to a creep function that was compared with data. Repeatable creep was observed for each loading rate and was similar among all six anatomical EOMs. The mean creep coefficient after 1,500 seconds for a wide range of initial loading rates was at1.37±0.03(standard deviation, SD). The creep function derived from the relaxation-based QLV model agreed with observed creep to within 2.7% following 16.67%/s ramp loading. Measured creep agrees closely with a derived QLV model of EOM relaxation, validating a previous QLV model for characterization of EOM biomechanics.

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.

scholarly journals Creation of Truck Axle Load Spectra Using Weigh-in-Motion Data

2010 ◽  
Vol 47 (4) ◽  
Author(s):  
Yi Jiang ◽  
Shuo Li ◽  
Tommy Nantung ◽  
Kirk Mangold ◽  
Scott A. MacArthur
Keyword(s):  
Design Method ◽  
Pavement Design ◽  
Smooth Transition ◽  
Traffic Information ◽  
Truck Traffic ◽  
Motion Data ◽  
Load Spectra ◽  
Weigh In Motion ◽  
Adjustment Factors ◽  
Axle Loads

To assure a smooth transition from the existing pavement design methods to the new mechanistic-empirical design method in the Indiana Department of Transportation, a study was conducted to create truck traffic inputs and axle load spectra of major interstate and state-owned highways in Indiana. The existing pavement design method is based on the equivalent single-axle loads (ESAL), which converts wheel loads of various magnitudes and repetitions to an equivalent number of "standard" or "equivalent" axle loads. The new design method uses axle load spectra as the measure of vehicle loads on pavements. These spectra represent the percentage of the total axle applications within each load interval for single, tandem, tridem, and quad axles. In this study, the truck traffic and axle load spectra were developed based on the historical traffic data collected at 47 sites with weigh-in-motion technology. The truck traffic information includes hourly, daily, and monthly distributions of various types of vehicles and corresponding adjustment factors, the distributions of the number of axles of each type of truck, the weights of the axles, the spaces between the axles, the proportions of vehicles on roadway lanes, and the proportions of vehicles in driving directions. This paper presents the truck traffic and axle load spectra generated from the weigh-in-motion sites as required by the new pavement design method.

Correlating Laboratory Conditioning with Field Aging for Asphalt using Rheological Parameters

2020 ◽  
Vol 2674 (5) ◽  
pp. 393-404 ◽  
Author(s):  
Runhua Zhang ◽  
Jo E. Sias ◽  
Eshan V. Dave
Keyword(s):  
Asphalt Binder ◽  
Life Time ◽  
Rheological Parameters ◽  
Performance Grade ◽  
Wide Range ◽  
Binder Aging ◽  
Long Term Performance ◽  
Term Performance

Aging has a significant effect on performance of asphalt materials. Reliable characterization of asphalt binder properties with aging is crucial to improving asphalt binder specifications as well as modification and formulation methods. The objective of this study is to correlate the laboratory conditioning methods with field aging using evolution of binder rheological parameters with time and pavement depth. Loose mixtures are aged in the lab (5 and 12 days aging at 95°C, and 24 h at 135°C) and recovered binder rheological properties are compared with those from different layers of field cores. The virgin binder results with 20 h pressure aging vessel (PAV) aging are also included. Binder testing is conducted using a dynamic shear rheometer with a 4 mm plate over a wide range of frequencies and temperatures. Rheological parameters calculated from the master curves, performance grade system, and binder Christensen–Anderson–Marasteanu model are used to evaluate changes with aging. The field aging gradient is evaluated, and the laboratory conditioning durations corresponding with the field aging durations at different pavement depths are calculated. The results show that 5 days of aging can simulate around 8 years of field aging (in New Hampshire) for the top 12.5 mm pavement, and 12 days’ aging can simulate approximately 20 years; 20 h PAV binder aging is not adequate to capture the long-term performance of the pavement. This study provides a way to optimize the laboratory conditioning durations and evaluate the performance of asphalt material with respect to pavement life (time) and depth (location) within the pavement structure.

Jointed Plain Concrete (JPC) Pavement Variability and Method to Complement JPC Design with 3D Pavement Data

2021 ◽  
pp. 036119812199782
Author(s):  
Georgene Malone Geary ◽  
Yichang (James) Tsai
Keyword(s):  
Crack Detection ◽  
Plain Concrete ◽  
Pavement Design ◽  
Pavement Performance ◽  
Local Calibration ◽  
Design Guide ◽  
Unique Method ◽  
Departments Of Transportation ◽  
Transportation Agencies

3D pavement data are increasing in use and availability and open up new opportunities to evaluate variability in pavements. The majority of information we currently have on existing pavements is the result of the Long Term Pavement Performance Program (LTPP). While the program is comprehensive and the data are immense, the study sections are typically only 500 ft in length, which limits the ability to accurately gauge the variability of the distresses in a pavement over a longer length, especially cracking in Jointed Plain Concrete (JPC) slabs. 3D pavement data already collected by transportation agencies have the opportunity to complement LTPP data to analyze variability and improve the use of LTPP data. This paper presents a unique method to complement LTPP data using 3D pavement data, consisting of four steps: (1) crack detection using 3D pavement data; (2) categorize detected cracks by orientation and extent by slab using 3D slab-based methodology; (3) convert categorized slab level cracking into mechanistic-empirical pavement design guide cracking; and (4) perform local calibration with the 3D converted input values. The method uses 3D pavement data to provide a non-discrete value for percent cracking in GPS-3 LTPP sections for the purposes of local calibration. The proposed method is shown to be feasible using 3D pavement data and two JPC LTPP sections in Georgia. The method could be extended to any of the state Departments of Transportation that have active LTPP sections and are now or will shortly be collecting 3D pavement data.

scholarly journals Truck Traffic and Load Spectra of Indiana Roadways for the Mechanistic-Empirical Pavement Design Guide

2020 ◽  
Author(s):  
Jieyi Bao ◽  
Xiaoqiang Hu ◽  
Cheng Peng ◽  
Yi Jiang ◽  
Shuo Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Critical Role ◽  
Fatigue Cracking ◽  
Pavement Design ◽  
Easy Access ◽  
Traffic Data ◽  
Truck Traffic ◽  
Load Spectra ◽  
Design Guide ◽  
Input Module

The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed for pavement design by the Indiana Department of Transportation (INDOT) since 2009 and has generated efficient pavement designs with a lower cost. It has been demonstrated that the success of MEPDG implementation depends largely on a high level of accuracy associated with the information supplied as design inputs. Vehicular traffic loading is one of the key factors that may cause not only pavement structural failures, such as fatigue cracking and rutting, but also functional surface distresses, including friction and smoothness. In particular, truck load spectra play a critical role in all aspects of the pavement structure design. Inaccurate traffic information will yield an incorrect estimate of pavement thickness, which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness. The primary objective of this study was to update the traffic design input module, and thus to improve the current INDOT pavement design procedures. Efforts were made to reclassify truck traffic categories to accurately account for the specific axle load spectra on two-lane roads with low truck traffic and interstate routes with very high truck traffic. The traffic input module was updated with the most recent data to better reflect the axle load spectra for pavement design. Vehicle platoons were analyzed to better understand the truck traffic characteristics. The unclassified vehicles by traffic recording devices were examined and analyzed to identify possible causes of the inaccurate data collection. Bus traffic in the Indiana urban areas was investigated to provide additional information for highway engineers with respect to city streets as well as highway sections passing through urban areas. New equivalent single axle load (ESAL) values were determined based on the updated traffic data. In addition, a truck traffic data repository and visualization model and a TABLEAU interactive visualization dashboard model were developed for easy access, view, storage, and analysis of MEPDG related traffic data.

Large-sample hydrology to foster open and collaborative research: a review of recent progress and grand challenges

2020 ◽  
Author(s):  
Gemma Coxon ◽  
Nans Addor ◽  
Camila Alvarez-Garreton ◽  
Hong X. Do ◽  
Keirnan Fowler ◽  
...  
Keyword(s):  
Human Impacts ◽  
Individual Case ◽  
Large Sample ◽  
Large Sets ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Diverse Data

<p>Large-sample hydrology (LSH) relies on data from large sets (tens to thousands) of catchments to go beyond individual case studies and derive robust conclusions on hydrological processes and models and provide the foundation for improved understanding of the link between catchment characteristics, climate and hydrological responses. Numerous LSH datasets have recently been released, covering a wide range of regions and relying on increasingly diverse data sources to characterize catchment behaviour. These datasets offer novel opportunities for open hydrology, yet they are also limited by their lack of comparability, accessibility, uncertainty estimates and characterization of human impacts.</p><p>Here, we underscore the key role of LSH datasets in open hydrologic science and highlight their potential to enhance the transparency and reproducibility of hydrological studies.  We provide a review of current LSH datasets and identify their limitations, including the current difficulties of inter-dataset comparison and limited accessibility of hydrological observations. To overcome these limitations, we propose simple guidelines alongside long-term coordinated actions for the community, which aim to standardize and automatize the creation of LSH datasets worldwide. This presentation will highlight how, by producing and using common LSH datasets, the community can increase the comparability and reproducibility of hydrological research.</p><p>This research was performed as part of the Panta Rhei Working Group on large-sample hydrology and is based on https://doi.org/10.1080/02626667.2019.1683182.</p>

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