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.

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.

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.

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.

Evasion of Weight-Enforcement Stations by Trucks

1997 ◽  
Vol 1570 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Wiley Cunagin ◽  
W. A. Mickler ◽  
Charles Wright
Keyword(s):  
Traffic Monitoring ◽  
Department Of Transportation ◽  
Weigh In Motion ◽  
Florida Department ◽  
Low Levels ◽  
Axle Loads ◽  
Truck Weight ◽  
Enforcement Activity ◽  
Avoidance Problem ◽  
Northeast Corner

Highway engineers have become increasingly concerned about the deterioration of the nation’s highway pavements. They suspect that overweight trucks are a primary cause of the problem. Before 1979, the data needed to assess the magnitude of the overweight-truck problem did not exist. Consequently, several studies were conducted to address the magnitude and location of overweight trucking. These studies included efforts to improve truck weight-enforcement programs and to assess the feasibility of using weigh-in-motion (WIM) equipment for weight enforcement. The Florida Department of Transportation (FDOT) undertook the study documented in this paper. The objective of the study was to assess the magnitude, expressed in equivalent single-axle loads (ESALs), of the problem of avoidance of weight-enforcement stations by overweight trucks. The study also considered whether bypassing traffic was local or interstate. FDOT selected the I-95 corridor in the northeast corner of Florida. Two permanent weight-enforcement stations and four bypass route locations were used as traffic monitoring sites. The results of the study indicate that the numbers of overweight vehicles decrease with increasing enforcement activity, but that vehicles attempt to bypass permanent truck weight-enforcement stations. In general, the violations at the permanent weight-enforcement stations were minor, whereas those on the bypass routes were much more severe. These results, when considered with the WIM data and the experience of truck weight-enforcement officers, suggest that only intensive enforcement activity can reduce violations to low levels. Information for enforcement, especially in the definition and quantification of the scale avoidance problem, is provided. Recommendations are made concerning corridor areas and extended and random enforcement operations.

scholarly journals Comparative Accuracy Analysis of Truck Weight Measurement Techniques

2021 ◽  
Vol 11 (2) ◽  
pp. 745
Author(s):  
Sylwia Stawska ◽  
Jacek Chmielewski ◽  
Magdalena Bacharz ◽  
Kamil Bacharz ◽  
Andrzej Nowak
Keyword(s):  
Measurement Techniques ◽  
Highway Traffic ◽  
Highway Infrastructure ◽  
Measurement Systems ◽  
Motion System ◽  
Weigh In Motion ◽  
Comparative Accuracy ◽  
Bridge Weigh In Motion ◽  
Rational Management ◽  
Truck Weight

Roads and bridges are designed to meet the transportation demands for traffic volume and loading. Knowledge of the actual traffic is needed for a rational management of highway infrastructure. There are various procedures and equipment for measuring truck weight, including static and in weigh-in-motion techniques. This paper aims to compare four systems: portable scale, stationary truck weigh station, pavement weigh-in-motion system (WIM), and bridge weigh-in-motion system (B-WIM). The first two are reliable, but they have limitations as they can measure only a small fraction of the highway traffic. Weigh-in-motion (WIM) measurements allow for a continuous recording of vehicles. The presented study database was obtained at a location that allowed for recording the same traffic using all four measurement systems. For individual vehicles captured on a portable scale, the results were directly compared with the three other systems’ measurements. The conclusion is that all four systems produce the results that are within the required and expected accuracy. The recommendation for an application depends on other constraints such as continuous measurement, installation and operation costs, and traffic obstruction.

Estimation of bridge static response and vehicle weights by frequency response analysis

1998 ◽  
Vol 25 (4) ◽  
pp. 631-639 ◽  
Author(s):  
G Thater ◽  
P Chang ◽  
D R Schelling ◽  
C C Fu
Keyword(s):  
Dynamic Effect ◽  
Dynamic Responses ◽  
Response Analysis ◽  
Static Response ◽  
Actual Weight ◽  
Service Testing ◽  
Weigh In Motion ◽  
Moving Vehicles ◽  
Bridge Structures ◽  
Truck Weight

A methodology is developed to more accurately estimate the static response of bridges due to moving vehicles. The method can also be used to predict dynamic responses induced by moving vehicles using weigh-in-motion (WIM) techniques. Historically, WIM is a well-developed technology used in highway research, since it has the advantage of allowing for the stealthy automatic collection of weight data for heavy trucks. However, the lack of accuracy in determining the dynamic effect in bridges has limited the potential for its use in estimating the fatigue life of bridge structures and their components. The method developed herein amends the current WIM procedures by filtering the dynamic responses accurately using the Fast Fourier Transform (FFT). Example applications of the proposed method are shown by using computer-generated data. The method is fast and improves the predicted truck weight up to 5% of the actual weight, as compared to errors up to 10% using the current WIM methods.Key words: weigh-in-motion, digital filters, FFT, bridge dynamics, in-service testing.

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 Investigation of Weigh-in-Motion Measurement Accuracy on the Basis of Steering Axle Load Spectra

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3272 ◽  
Author(s):  
Dawid Rys
Keyword(s):  
Data Quality ◽  
Vehicle Speed ◽  
Traffic Data ◽  
Obvious Effect ◽  
Road Infrastructure ◽  
Load Spectra ◽  
Weigh In Motion ◽  
Infrastructure Design ◽  
Load Sensor ◽  
Measurement Results

Weigh-in-motion systems are installed in pavements or on bridges to identify and reduce the number of overloaded vehicles and minimise their adverse effect on road infrastructure. Moreover, the collected traffic data are used to obtain axle load characteristics, which are very useful in road infrastructure design. Practical application of data from weigh-in-motion has become more common recently, which calls for adequate attention to data quality. This issue is addressed in the presented paper. The aim of the article is to investigate the accuracy of 77 operative weigh-in-motion stations by analysing steering axle load spectra. The proposed methodology and analysis enabled the identification of scale and source of errors that occur in measurements delivered from weigh-in-motion systems. For this purpose, selected factors were investigated, including the type of axle load sensor, air temperature and vehicle speed. The results of the analysis indicated the obvious effect of the axle load sensor type on the measurement results. It was noted that systematic error increases during winter, causing underestimation of axle loads by 5% to 10% for quartz piezoelectric and bending beam load sensors, respectively. A deterioration of system accuracy is also visible when vehicle speed decreases to 30 km/h. For 25% to 35% of cases, depending on the type of sensor, random error increases for lower speeds, while it remains at a constant level at higher speeds. The analysis also delivered a standard steering axle load distribution, which can have practical meaning in the improvement of weigh-in-motion accuracy and traffic data quality.

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