Analysis of Weigh-in-Motion Data for Truck Weight Grouping in Mechanistic–Empirical Pavement Design Guide

2011 ◽  
Vol 2256 (1) ◽  
pp. 169-178 ◽  
Author(s):  
Young-Jun Kweon ◽  
Benjamin H. Cottrell
Keyword(s):  
Pavement Design ◽  
Motion Data ◽  
Weigh In Motion ◽  
Design Guide ◽  
Truck Weight

Axle Load Distribution Characterization for Mechanistic Pavement Design

1998 ◽  
Vol 1629 (1) ◽  
pp. 13-23 ◽  
Author(s):  
Jong R. Kim ◽  
Leslie Titus-Glover ◽  
Michael I. Darter ◽  
Robert K. Kumapley
Keyword(s):  
Load Distribution ◽  
Pavement Design ◽  
Performance Study ◽  
Traffic Loading ◽  
Design Procedures ◽  
Motion Data ◽  
The North ◽  
Load Distributions ◽  
Weigh In Motion ◽  
The 1960S

Proper consideration of traffic loading in pavement design requires knowledge of the full axle load distribution by the main axle types, including single, tandem, and tridem axles. Although the equivalent single axle load (ESAL) concept has been used since the 1960s for empirical pavement design, the new mechanistic-based pavement design procedures under development by various agencies most likely will require the use of the axle load distribution. Procedures and models for converting average daily traffic into ESALs and axle load distribution are presented, as are the relevant issues on the characterization of the full axle load distributions for single, tandem, and tridem axles for use in mechanistic-based pavement design. Weigh-in-motion data from the North Central Region of the Long-Term Pavement Performance study database were used to develop the models for predicting axle load distribution.

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.

Statistical Investigation of Design Live Load by Applying WIM Data with Different Degree of Compliance with Truck Weight Limit

2018 ◽  
Author(s):  
Inyeol Paik ◽  
Kilhwan Jeong
Keyword(s):  
Measurement Data ◽  
Statistical Investigation ◽  
Live Load ◽  
Overload Control ◽  
Safety Level ◽  
Motion Data ◽  
Weigh In Motion ◽  
Single Vehicle ◽  
Live Load Model ◽  
Truck Weight

<p>The live load model applied to the design of the bridge should be defined so that a target safety level can be secured in the actual traffic environment. In this paper, it is presented that the live load could be greatly affected by the measurement data used for the statistical estimation process. For this purpose, a statistical analysis is performed on WIM (weigh-in-motion) data measured during different periods in terms of the overload control at the same spot of an expressway in Korea. The effects by a single vehicle, the back-to-back series vehicles and the side-by-side parallel vehicles are obtained and compared.</p>

Development of Mechanistic-Empirical Pavement Design in Minnesota

1998 ◽  
Vol 1629 (1) ◽  
pp. 181-188 ◽  
Author(s):  
David Timm ◽  
Bjorn Birgisson ◽  
David Newcomb
Keyword(s):  
Material Characterization ◽  
Design Method ◽  
Laboratory Data ◽  
Design Procedure ◽  
Computer Models ◽  
Pavement Design ◽  
University Of Minnesota ◽  
Motion Data ◽  
Load Spectra ◽  
Weigh In Motion

The next AASHTO guide on pavement design will encourage a broader use of mechanistic-empirical (M-E) approaches. While M-E design is conceptually straightforward, the development and implementation of such a procedure are somewhat more complicated. The development of an M-E design procedure at the University of Minnesota, in conjunction with the Minnesota Department of Transportation, is described. Specifically, issues concerning mechanistic computer models, material characterization, load configuration, pavement life equations, accumulating damage, and seasonal variations in material properties are discussed. Each of these components fits into the proposed M-E design procedure for Minnesota but is entirely compartmentalized. For example, as better computer models are developed, they may simply be inserted into the design method to yield more accurate pavement response predictions. Material characterization, in terms of modulus, will rely on falling-weight deflectometer and laboratory data. Additionally, backcalculated values from the Minnesota Road Research Project will aid in determining the seasonal variation of moduli. The abundance of weigh-in-motion data will allow for more accurate load characterization in terms of load spectra rather than load equivalency. Pavement life equations to predict fatigue and rutting in conjunction with Miner’s hypothesis of accumulating damage are continually being refined to match observed performance in Minnesota. Ultimately, a computer program that incorporates the proposed M-E design method into a user-friendly Windows environment will be developed.

Procedure to Estimate Loading from Weigh-in-Motion Data

1996 ◽  
Vol 1536 (1) ◽  
pp. 19-24
Author(s):  
Shie-Shin Wu
Keyword(s):  
Monitoring Program ◽  
Traffic Monitoring ◽  
Traffic Classification ◽  
Traffic Loading ◽  
Motion Data ◽  
Weigh In Motion ◽  
Load Factors ◽  
Different Types ◽  
Truck Load ◽  
Truck Weight

Truck weight data collected from weigh-in-motion (WIM) sites were used to develop a procedure to estimate truck load factors for pavement design purposes. A conceptual procedure that uses WIM data to derive equivalent single-axle load factors for different types of trucks is presented. Sets of load factors can be developed for different types of facilities. An example is provided to illustrate how these factors can be used by engineers to calculate project design loading. Applying these factors to traffic classification counts collected from the statewide traffic monitoring program, engineers can also compute network traffic loading history.

scholarly journals Determination of load equivalency factors by statistical analysis of weigh-in-motion data

2016 ◽  
Vol 11 (4) ◽  
pp. 251-258 ◽  
Author(s):  
Zoltán Soós ◽  
Csaba Tóth ◽  
Dávid Bóka
Keyword(s):  
Pavement Design ◽  
Border Crossings ◽  
Design Data ◽  
Vehicle Class ◽  
Motion Data ◽  
Weigh In Motion ◽  
Equivalency Factors ◽  
False Result ◽  
One Year ◽  
Load Equivalency Factors

The load equivalency factors for pavement design currently in use by the Hungarian standard have been developed using Weigh-in-Motion data obtained during the first few years of operations after installing some 30 measuring sites in Hungary in 1996. In the past years, and currently, data is collected mainly at the border crossings of the country, however the data is used only for law enforcement purposes, and no comprehensive statistical analyses have been done. To develop actual load equivalency factors for the use in pavement design, data of one year was collected and statistical methods were applied. An algorithm was used to help managing the multimodal distribution of axle loads in mathematical perspectives. Monte-Carlo methods were applied to determine the factors for each heavy vehicle type and eventually for each vehicle class used by the current Hungarian pavement design manual. The calculated factors are considerably different from the current ones, indicating that the pavement design may lead to a false result. Furthermore, there are three vehicle types suggested to be incorporated into the standard due to their high occurrence.

The use of weigh-in-motion data in pavement design

2001 ◽  
Vol 147 (4) ◽  
pp. 245-254
Author(s):  
B. Al Hakim ◽  
A. C. Collop ◽  
N. H. Thom
Keyword(s):  
Pavement Design ◽  
Motion Data ◽  
Weigh In Motion

The use of weigh-in-motion data in pavement design

2001 ◽  
Vol 147 (4) ◽  
pp. 245-254
Author(s):  
B. Al Hakim ◽  
A. C. Collop ◽  
N. H. Thom
Keyword(s):  
Pavement Design ◽  
Motion Data ◽  
Weigh In Motion

Generating Site-Specific Axle Load Factors for the Mechanistic–Empirical Pavement Design Guide

2013 ◽  
Vol 2339 (1) ◽  
pp. 98-103 ◽  
Author(s):  
Wiley Cunagin ◽  
Richard L. Reel ◽  
Mohammad S. Ghanim ◽  
Drew Roark ◽  
Michael Leggett
Keyword(s):  
Pavement Design ◽  
Frequency Spectra ◽  
Frequency Distributions ◽  
Load Frequency ◽  
Load Spectra ◽  
Weigh In Motion ◽  
Florida Department ◽  
Truck Loading ◽  
And Cluster Analysis ◽  
Truck Weight

Use of the AASHTO DARWin-ME mechanistic–empirical pavement design software requires that truck loading data be provided in the form of normalized axle load frequency distributions (spectra). Default axle load frequency spectra are provided in the software. However, these default distributions were derived from national data and may not suit the needs of individual states. This study analyzed the Florida Department of Transportation's substantial database of truck weight data taken from its network of high-quality weigh-in-motion stations to determine whether site- or state-specific axle load spectra could be generated and how they should be applied. Several analytical procedures were developed and applied to the data, including analysis of variance and cluster analysis. The results of this work were used to develop Level 2 axle load spectra that could be applied to design sections. This paper presents detailed information about the traffic data requirements of the new guide, the process followed for deriving Florida's input values, and the resulting recommended values.

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