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.

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.

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.

scholarly journals Estimation of Extreme Load Effects on Long-Span Bridges Using Traffic Image Data

2018 ◽  
Vol 13 (4) ◽  
pp. 429-446 ◽  
Author(s):  
Elena Alexandra Micu ◽  
Eugene John Obrien ◽  
Abdollah Malekjafarian ◽  
Michael Quilligan
Keyword(s):  
Extreme Values ◽  
Image Data ◽  
Traffic Load ◽  
Long Span Bridges ◽  
Motion Data ◽  
Congested Traffic ◽  
Long Span ◽  
Weigh In Motion ◽  
Extreme Load ◽  
One Year

This paper proposes an algorithm for the estimation of extreme intensity of traffic load on long-span bridges. Most Weigh-in-Motion technologies do not operate in congested conditions which are the governing cases for these bridges. In the absence of Weigh-in-Motion data on the bridge itself, a correlation between vehicle weights and their lengths is established here using a (free- flowing) Weigh-in-Motion database. Photographic images of congested traffic are modelled here for three bridges using weights estimated from lengths and one year of Weigh-in-Motion data. The actual weights are taken from the Weigh-in- Motion data, and the results are compared to test the method. The gaps between vehicles are firstly set to a constant value and later to Beta-distributed values according to vehicle type. The intensity of traffic load for all pictures is calculated and compared to the loads obtained from the recorded weights. A return period of 75-year is chosen to evaluate the extreme values of intensity. The probability that intensity of load is being exceeded is obtained using normal probability paper for both recorded and simulated weights. This study demonstrates the feasibility of the proposed concept of using lengths to estimate the extreme traffic load events with acceptable accuracy.

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

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.

Neural Network Optimal Model for Classification of Unclassified Vehicles in Weigh-in-Motion Traffic Data

2021 ◽  
pp. 036119812199067
Author(s):  
Cheng Peng ◽  
Yi Jiang ◽  
Shuo Li ◽  
Tommy Nantung
Keyword(s):  
Neural Network ◽  
Traffic Congestion ◽  
Algorithm Design ◽  
Robustness Analysis ◽  
Pavement Design ◽  
Traffic Data ◽  
Optimal Model ◽  
Design Data ◽  
Lane Changing ◽  
Weigh In Motion

A weigh-in-motion (WIM) system has the capability to perform on-site vehicle classifications based on the FHWA schema. However, WIM datasets often contain a significant portion of vehicles that could not be classified into any of the 13 vehicle classes by WIM devices. Possible reasons for the WIM classifier failing to classify these vehicles are tailgating, lane changing, traffic congestion, and equipment malfunction. Analysis of unclassified vehicles was performed with WIM-recorded data. A neural network model was established to determine the appropriate allocations of unclassified vehicles to vehicle classes. Since the number of unclassified vehicles is often fairly high, the allocations will help to improve the accuracy of truck traffic data and thus improve pavement design. Video records of traffic streams on an interstate section and traffic data from a nearby WIM station were used to identify causes for vehicle misclassifications. The optimal model was developed through model algorithm design, data processing, model training, validation, robustness analysis, and verification of video records. It was found that the optimal model was effective in allocating unclassified vehicles to appropriate vehicle classes. The optimal model was able to reclassify the unclassified vehicles that had non-zero attributes with high accuracy. The optimal model provides a useful tool for properly allocating the unclassified vehicles to the FHWA specified vehicle classes. The developed allocations can be applied to allocate unclassified vehicles appropriately to vehicle classes for pavement design and would potentially increase benefit and reduce cost with reliable and realistic pavement designs.

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