Analysis and Determination of Axle Load Spectra and Traffic Input for the Mechanistic-Empirical Pavement Design Guide PDF Download
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Author: Yi Jiang
Publisher: Purdue University Press
ISBN: 9781622600885
Category : Transportation
Languages : en
Pages : 110
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Book Description
The values of equivalent single axle loads (ESAL) have been used to represent the vehicle loads in pavement design. To improve the pavement design procedures, a new method, called the Mechanistic-Empirical Pavement Design Guide (MEPDG), has been developed to use the axle load spectra to represent the vehicle loads in pavement design. These spectra represent the percentage of the total axle applications within each load interval for single, tandem, tridem, and quad axles. Using axle load spectra as the traffic input, the MEPDG method is able to analyze the impacts of varying traffic loads on pavement and provide an optimal pavement structure design. In addition, the new method can be used to analyze the effects of materials and the impacts of seasons, to compare rehabilitation strategies, and to perform forensic analyses of pavement conditions. The MEPDG utilizes mechanistic-empirical approaches to realistically characterize inservice pavements and allows the full integration of vehicular traffic loadings, climatic features, soil characteristics, and paving materials properties into the detailed analysis of pavement structural behaviors and the resulting pavement performance. In order to provide the traffic data input required by the MEPDG, the Indiana Department of Transportation (INDOT) made an effort to obtain truck traffic information from the traffic data collected through weigh-in-motion (WIM) stations. This study was conducted to create the truck traffic spectra and other traffic inputs for INDOT to implement the new pavement design method. Furthermore, the INDOT AADT data were used in this study to analyze the spatial distributions of the traffic volumes in Indiana and to obtain the spatial distributions of traffic volumes.
Author: Yi Jiang
Publisher: Purdue University Press
ISBN: 9781622600885
Category : Transportation
Languages : en
Pages : 110
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Book Description
The values of equivalent single axle loads (ESAL) have been used to represent the vehicle loads in pavement design. To improve the pavement design procedures, a new method, called the Mechanistic-Empirical Pavement Design Guide (MEPDG), has been developed to use the axle load spectra to represent the vehicle loads in pavement design. These spectra represent the percentage of the total axle applications within each load interval for single, tandem, tridem, and quad axles. Using axle load spectra as the traffic input, the MEPDG method is able to analyze the impacts of varying traffic loads on pavement and provide an optimal pavement structure design. In addition, the new method can be used to analyze the effects of materials and the impacts of seasons, to compare rehabilitation strategies, and to perform forensic analyses of pavement conditions. The MEPDG utilizes mechanistic-empirical approaches to realistically characterize inservice pavements and allows the full integration of vehicular traffic loadings, climatic features, soil characteristics, and paving materials properties into the detailed analysis of pavement structural behaviors and the resulting pavement performance. In order to provide the traffic data input required by the MEPDG, the Indiana Department of Transportation (INDOT) made an effort to obtain truck traffic information from the traffic data collected through weigh-in-motion (WIM) stations. This study was conducted to create the truck traffic spectra and other traffic inputs for INDOT to implement the new pavement design method. Furthermore, the INDOT AADT data were used in this study to analyze the spatial distributions of the traffic volumes in Indiana and to obtain the spatial distributions of traffic volumes.
Author: Ala R. Abbas
Publisher:
ISBN:
Category : Live loads
Languages : en
Pages : 227
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Book Description
The recently developed mechanistic-empirical pavement design guide (MEPDG) requires a multitude of traffic inputs to be defined for the design of pavement structures, including the initial two-way annual average daily truck traffic (AADTT), directional and lane distribution factors, vehicle class distribution, monthly adjustment factors, hourly truck distribution factors, traffic growth rate, axle load spectra by truck class (Class 4 to Class 13) and axle type (single, tandem, tridem, and quad), and number of axles per truck. Since it is not always practical to obtain site-specific traffic data, the MEPDG assimilates a hierarchal level concept that allows pavements to be designed using statewide averages and MEPDG default values without compromising the accuracy of the pavement design. In this study, a Visual Basic for Application (VBA) code was developed to analyze continuous traffic monitoring data and generate site-specific and statewide traffic inputs. The traffic monitoring data was collected by 143 permanent traffic monitoring sites (93 automated vehicle classifier (AVC) and 50 weigh-in-motion (WIM) sites) distributed throughout the State of Ohio from 2006 to 2011. The sensitivity of the MEPDG to the various traffic inputs was evaluated using two baseline pavement designs, one for a new flexible pavement and one for a new rigid pavement. Key performance parameters for the flexible pavement included longitudinal (top-down) fatigue cracking, alligator (bottom-up) fatigue cracking, transverse (low-temperature) cracking, rutting, and smoothness (expressed using IRI), while key performance parameters for the rigid pavement included transverse cracking (% slabs cracked), joint faulting, and smoothness. The sensitivity analysis results revealed that flexible pavements are moderately sensitive to AADTT, growth rate, vehicle class distribution, and axle load spectra; and not sensitive to hourly distribution factors, monthly adjustment factors, and number of axles per truck. Furthermore, it was found that rigid pavements are moderately sensitive to AADTT, growth rate, hourly distribution factors, vehicle class distribution, and axle load spectra; and not sensitive to monthly adjustment factors and number of axles per truck. Therefore, it is recommended to estimate the AADTT and the vehicle class distribution from site-specific short-term or continuous counts and obtain the truck growth rate from ODOT Modeling and Forecasting Section (Certified Traffic). As for the other traffic inputs, statewide averages can be used for the hourly distribution factors, axle load spectra, and number of axles per truck; and MEPDG defaults can be used for the monthly adjustment factors.
Author: Bryan C. Smith
Publisher:
ISBN:
Category : Axial loads
Languages : en
Pages : 42
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Book Description
This study developed traffic inputs for use with the Guide for the Mechanistic-Empirical Design of New & Rehabilitated Pavement Structures (MEPDG) in Virginia and sought to determine if the predicted distresses showed differences between site-specific and default traffic inputs for flexible and rigid pavements. The axle-load spectra, monthly adjustment factors, vehicle class distribution factors, and number of axles per truck inputs were considered. The predicted distresses based on site-specific traffic inputs from eight interstate and seven primary route weigh-in-motion sites in Virginia were compared to predicted distresses using MEPDG default traffic inputs. These comparisons were performed by use of a normalized difference statistic for each site-specific traffic input and the coefficient of variation for each pavement distress model. In addition, the practical significance for flexible pavements was considered from the difference in the predicted time to failure between site-specific and default traffic inputs. The analysis showed that the effect of the site-specific traffic inputs was generally not statistically significant when the uncertainty of the distress models was considered. However, the site-specific axle-load spectra and vehicle class distribution inputs showed a statistically significant effect on certain predicted distresses for flexible and rigid pavements, respectively. The study recommends that site-specific axle-load spectra data be considered for analysis of flexible pavements. Alternatively, summary (statewide average) axle-load spectra data for analysis of interstate and primary flexible pavements should be considered preferentially over default axle-load spectra. Site-specific vehicle class distribution factors should be considered for analysis of rigid pavements on the interstate system. Alternatively, summary (statewide average) vehicle class distribution factors for analysis of interstate rigid pavements should be considered preferentially over default vehicle class distribution data. Default traffic data are recommended for analysis of primary rigid pavements. This study also recommends that a local calibration process be completed to determine if the predictive models accurately predict the conditions found on Virginia's roadways. If the predictive models are modified, the results may impact the recommendations resulting from this study. The implementation of the recommendations of this study and the use of the MEPDG in general will provide the Virginia Department of Transportation with a more advanced means of designing and analyzing pavements. This should result in optimal designs that are more efficient in terms of initial construction and future maintenance costs.
Author: American Association of State Highway and Transportation Officials
Publisher: AASHTO
ISBN: 156051423X
Category : Pavements
Languages : en
Pages : 218
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Book Description
Author: Jorge A. Prozzi
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 164
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Book Description
The goal of this research study was to assess and address the implications of the axle load spectra approach proposed by the M-E Design Guide. In addition, recommendations were developed regarding traffic data needs and availability to aid in deciding the installation locations of future WIM stations in Texas. A methodology for specifying the required accuracy of WIM equipment based on the effect that this accuracy has on pavement performance prediction was also developed. Regarding traffic volume forecasting, a methodology is presented that allows optimum use of available data by simultaneously estimating traffic growth and seasonal traffic variability.
Author: Ferdous Intaj
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Proper characterization of traffic data is a prerequisite for the determination of appropriate traffic inputs to Mechanistic-Empirical Pavement Design Guide (MEPDG). The development of proper traffic inputs helps reflect the traffic conditions over the life of pavement which would decrease the maintenance, repair and traffic disruptions and improve the traffic conditions of a road network. The objective of the study was to characterize the traffic data and suggest the sitespecific, regional or state wide average values for traffic inputs to MEPDG for New York State. Vehicle class distribution (VCD), monthly distribution factors (MDF), hourly distribution factors (HDF), average number of axle groups per vehicle (AGPV) and axle load spectra were obtained from vehicle classification and WIM sites in New York State for the years of 2007-2011. These traffic data was processed with TrafLoad software. Cluster analysis was performed on the processed VCD, MDF and HDF data collected during the time period. This statistical analysis could not be done for AGPV values and axle load spectra due to the unavailability of sufficient number of WIM sites. However, MEPDG runs were carried out to investigate the effect of the variability of traffic inputs on the pavement performance of typical new flexible and rigid pavement structures. The statistical analysis showed consistent results for VCD and HDF over the years. However, the results of statistical analysis on MDF were not consistent over the time period. Site specific values for VCD, MDF, AGPV and axle load spectra showed little variation with statewide average values after the cluster analysis and MEPDG runs for the vehicle classification and WIM data of the year of 2010. This was observed for both flexible and rigid pavements. However, HDF did not show any effect on the design of pavement with MEPDG. These findings were also verified from the analysis of vehicle classification and WIM data of the other years.
Author: Ala R. Abbas
Publisher:
ISBN:
Category : Live loads
Languages : en
Pages : 0
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Book Description
The recently developed mechanistic-empirical pavement design guide (MEPDG) requires a multitude of traffic inputs to be defined for the design of pavement structures, including the initial two-way annual average daily truck traffic (AADTT), directional and lane distribution factors, vehicle class distribution, monthly adjustment factors, hourly truck distribution factors, traffic growth rate, axle load spectra by truck class (Class 4 to Class 13) and axle type (single, tandem, tridem, and quad), and number of axles per truck. Since it is not always practical to obtain site-specific traffic data, the MEPDG assimilates a hierarchal level concept that allows pavements to be designed using statewide averages and MEPDG default values without compromising the accuracy of the pavement design. In this study, a Visual Basic for Application (VBA) code was developed to analyze continuous traffic monitoring data and generate site-specific and statewide traffic inputs. The traffic monitoring data was collected by 143 permanent traffic monitoring sites (93 automated vehicle classifier (AVC) and 50 weigh-in-motion (WIM) sites) distributed throughout the State of Ohio from 2006 to 2011. The sensitivity of the MEPDG to the various traffic inputs was evaluated using two baseline pavement designs, one for a new flexible pavement and one for a new rigid pavement. Key performance parameters for the flexible pavement included longitudinal (top-down) fatigue cracking, alligator (bottom-up) fatigue cracking, transverse (low-temperature) cracking, rutting, and smoothness (expressed using IRI), while key performance parameters for the rigid pavement included transverse cracking (% slabs cracked), joint faulting, and smoothness. The sensitivity analysis results revealed that flexible pavements are moderately sensitive to AADTT, growth rate, vehicle class distribution, and axle load spectra; and not sensitive to hourly distribution factors, monthly adjustment factors, and number of axles per truck. Furthermore, it was found that rigid pavements are moderately sensitive to AADTT, growth rate, hourly distribution factors, vehicle class distribution, and axle load spectra; and not sensitive to monthly adjustment factors and number of axles per truck. Therefore, it is recommended to estimate the AADTT and the vehicle class distribution from site-specific short-term or continuous counts and obtain the truck growth rate from ODOT Modeling and Forecasting Section (Certified Traffic). As for the other traffic inputs, statewide averages can be used for the hourly distribution factors, axle load spectra, and number of axles per truck; and MEPDG defaults can be used for the monthly adjustment factors.
Author: Olga Selezneva
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 216
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Book Description
The GDOT is preparing for implementation of the Mechanistic-Empirical Pavement Design Guide (MEPDG). As part of this preparation, a statewide traffic load spectra program is being developed for gathering truck axle loading data. This final report presents the results of a comprehensive research effort that culminated in recommendations for a statewide Traffic Load Spectra Program for collecting and processing truck axle loading data to support MEPDG implementation in Georgia. The recommendations include an optimal axle loading data collection plan that balances pavement design data needs, cost and number of WIM sites, and types of equipment used in obtaining the data. The report also shows how the available GDOT traffic data and other applicable data resources were used to develop traffic loading inputs and defaults to support local calibration of MEPDG models in Georgia. The available axle loading data were analyzed and the interim traffic loading defaults were developed for different groups of roads designed and maintained by GDOT, along with the recommendations for future updates of the defaults. In addition, user guidelines, decision trees, and software tools were developed to facilitate using the traffic loading defaults in MEPDG applications
Author: Jieyi Bao
Publisher:
ISBN:
Category : Traffic flow
Languages : en
Pages :
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Book Description
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.
Author: Kun Zhang
Publisher: Springer
ISBN: 3319957899
Category : Science
Languages : en
Pages : 179
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Book Description
This book presents new studies dealing with the attempts made by the scientists and practitioners to address contemporary issues in pavement engineering such as aging and modification of asphalt binders, performance evaluation of warm mix asphalt, and mechanical-based pavement structure analysis, etc.. Asphalt binder and mixture have been widely used to construct flexible pavements. Mechanical and Chemical characterizations of asphalt materials and integration of these properties into pavement structures and distresses analysis are of great importance to design a sustainable flexible pavement. This book includes discusses and new results dealing with these issues. Papers were selected from the 5th GeoChina International Conference 2018 – Civil Infrastructures Confronting Severe Weathers and Climate Changes: From Failure to Sustainability, held on July 23 to 25, 2018 in HangZhou, China.
