Author: Shariq A. Momin
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The Mechanistic-Empirical Pavement Design Guide (MEPDG) developed under the National Cooperative Highway Research Program (NCHRP) 1-37A project is based on mechanistic-empirical analysis of the pavement structure to predict the performance of the pavement under different sets of conditions (traffic, structure and environment). MEPDG takes into account the advanced modeling concepts and pavement performance models in performing the analysis and design of pavement. The mechanistic part of the design concept relies on the application of engineering mechanics to calculate stresses, strains and deformations in the pavement structure induced by the vehicle loads. The empirical part of the concept is based on laboratory developed performance models that are calibrated with the observed distresses in the in-service pavements with known structural properties, traffic loadings, and performances. These models in the MEPDG were calibrated using a national database of pavement performance data (Long Term Pavement Performance, LTPP) and will provide design solution for pavements with a national average performance. In order to improve the performance prediction of the models and the efficiency of the design for a given state, it is necessary to calibrate it to local conditions by taking into consideration locally available materials, traffic information and the environmental conditions. The objective of this study was to calibrate the MEPDG flexible pavement performance models to local conditions of Northeastern region of United States. To achieve this, seventeen pavement sections were selected for the calibration process and the relevant data (structural, traffic, climatic and pavement performance) was obtained from the LTPP database. MEPDG software (Version 1.1) simulation runs were made using the nationally calibrated coefficients and the MEPDG predicted distresses were compared with the LTPP measured distresses (rutting, alligator and longitudinal cracking, thermal cracking and IRI). The predicted distresses showed fair agreement with the measured distresses but still significant differences were found. The difference between the measured and the predicted distresses were minimized through recalibration of the MEPDG distress models. For the permanent deformation models of each layer, a simple linear regression with no intercept was performed and a new set of model coefficients (ßr1, ßGB, and ßSG) for asphalt concrete, granular base and subgrade layer models were calculated. The calibration of alligator (bottom-up fatigue cracking) and longitudinal (topdown fatigue cracking) was done by deriving the appropriate model coefficients (C1, C2, and C4) since the fatigue damage is given in MEDPG software output. Thermal cracking model was not calibrated since the measured transverse cracking data in the LTPP database did not increase with time, as expected to increase with time. The calibration of IRI model was done by computing the model coefficients (C1, C2, C3, and C4) based on other distresses (rutting, total fatigue cracking, and transverse cracking) by performing a simple linear regression.
Author:
Publisher: AASHTO
ISBN: 1560514493
Category : Technology & Engineering
Languages : en
Pages : 202
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Book Description
This guide provides guidance to calibrate the Mechanistic-Empirical Pavement Design Guide (MEPDG) software to local conditions, policies, and materials. It provides the highway community with a state-of-the-practice tool for the design of new and rehabilitated pavement structures, based on mechanistic-empirical (M-E) principles. The design procedure calculates pavement responses (stresses, strains, and deflections) and uses those responses to compute incremental damage over time. The procedure empirically relates the cumulative damage to observed pavement distresses.
Author: Hongren Gong
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 152
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Book Description
The Mechanistic-Empirical Pavement Design Guide (MEPDG) represents the state-of-art procedure for pavement design. However, after more than a decade since its publication, the number of agencies that have reported entirely adopting this design system is small. Among the many causes of this phenomenon, the poor predictive accuracy of the performance prediction models is considered the most crucial one. To improve the accuracy of performance predicted by the MEPDG, a preliminary calibration was first conducted for these models with data from the pavement management system (PMS) of Tennessee, and then employed various machine learning algorithms for further improvements. Also, an approach for estimating the modulus of existing asphalt pavement was proposed to enhance the reliability of rehabilitation analysis with the MEPDG. The transfer functions for alligator cracking and longitudinal cracking were validated and calibrated with data collected from the PMS of the state of Tennessee. The results of calibration efforts showed that after calibration, both the bias and variance of the prediction were significantly reduced. It was noted that although local calibration helped improve the accuracy of the transfer functions, the extent of improvement is limited. An observation of the performance models revealed that they were either inadequately formulated or too inflexible to capture sufficient information from the inputs. To further improve the predictive performance of the transfer functions in the MEPDG, several machine learning algorithms were employed including the gradient boosted model (GBM) for fatigue cracking, deep neural networks for rutting, and random forest for IRI. Using the determination of coefficient (R2) and root mean squared error (RMSE) as the measure of model performance, compared with the global transfer functions, the models developed achieved significantly better predictive performance. The results from the regularized regression model indicated that, compared with the model using deflection basins parameters (DBPs), the one without DBPs could still generate modulus prediction of reasonable accuracy. Rehabilitation analyses in the MEPDG with the estimated modulus also contributed to the improved accuracy in pavement performance prediction.
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: Matthew L. Steele
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 248
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Book Description
Author: Abu Ahmed Sufian
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The Kansas Department of Transportation is transitioning from adherence to the 1993 American Association of State Highway and Transportation Officials (AASHTO) Pavement Design Guide to implementation of the new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) for flexible and rigid pavement design. This study was initiated to calibrate MEPDG distress models for Kansas. Twenty-seven newly constructed projects were selected for flexible pavement distress model calibration, 21 of which were used for calibration and six that were selected for validation. In addition, 22 newly constructed jointed plain concrete pavements (JPCPs) were selected to calibrate rigid models; 17 of those projects were selected for calibration and five were selected for validation. AASHTOWare Pavement ME Design (ver. 2.2) software was used for design analysis, and the traditional split sampling method was followed in calibration. MEPDG-predicted distresses of Kansas road segments were compared with those from Pavement Management Information System data. Statistical analysis was performed using the Microsoft Excel statistical toolbox. The rutting and roughness models for flexible pavement were successfully calibrated with reduced bias and accepted null hypothesis. Calibration of the top-down fatigue cracking model was not satisfactory due to variability in measured data, and the bottom-up fatigue cracking model was not calibrated because measured data was unavailable. AASHTOWare software did not predict transverse cracking for any projects with global values. Thus thermal cracking model was not calibrated. The JPCP transverse joint faulting model was calibrated using sensitivity analysis and iterative runs of AASHTOWare to determine optimal coefficients that minimize bias. The IRI model was calibrated using the generalized reduced gradient nonlinear optimization technique in Microsoft Excel Solver. The transverse slab cracking model could not be calibrated due to lack of measured cracking data.
Author: Stefan Anton Romanoschi
Publisher:
ISBN:
Category :
Languages : en
Pages : 225
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Book Description
The New York State Department of Transportation (NYSDOT) has used the AASHTO 1993 Design Guide for the design of new flexible pavement structures for more than two decades. The AASHTO 1993 Guide is based on the empirical design equations developed from the data collected in the AASHO Road Test in the early 1960s. A newer pavement design method, called the Mechanistic-Empirical Pavement Design Guide (MEPDG), was developed by the National Cooperative Highway Research Program (NCHRP) to provide a more efficient and accurate design method that is based on sound engineering principles. The MEPDG models have been incorporated in the AASHTOWare Pavement ME Design 2.1 software program. Due to the advanced principles and design capabilities of the AASHTOWare program, NYSDOT decided to implement the MEPDG and calibrate the distress models included in the software for the conditions in the state. This report summarizes the local calibration of the distress models for the Northeast (NE) region of the United States and the development of new design tables for new flexible pavement structures. Design, performance, and traffic data collected on the Long-Term Pavement Performance (LTPP) sites in the NE region of the United States were used to calibrate the distress models. First, the AASHTOWare Pavement ME Design 2.1 with global calibration factors was used to compare the predicted and measured distress values. The local bias was assessed for all distress models except for the longitudinal cracking model; it was found the bias existed for this model even after calibration. The thermal cracking model was not calibrated because of inaccurate measured data. The calibration improved the prediction capability of the rutting, fatigue cracking, and smoothness prediction models. The calibrated AASHTOWare software was used to run design cases for combinations of traffic volume and subgrade soil stiffness (resilient modulus, Mr) for 24 locations in the state of New York. The runs were performed for a road classified as Principal Arterial Interstate, 90% design reliability level, and 15- and 20-year design periods. State-wide average traffic volume parameters and axle load spectra were used to define the traffic. The configuration specified in the current design table used by NYSDOT, which is included in the Comprehensive Pavement Design Manual (CPDM), was followed for the pavement design solutions. The thicknesses for the select granular subgrade materials and the asphalt layer thicknesses were varied to include several values higher and lower than the thickness recommended by the CPDM. The thicknesses of asphalt surface and binder layers were kept constant; only the thickness of the asphalt base layer was changed. For each design combination, the design case with the thinnest asphalt layer for which the predicted distress was less than the performance criteria was selected as the design solution. The design solutions for each of the 24 locations were assembled in design tables. The comparison of the design tables showed that some variation in the design thickness for the asphalt layers exists with thicker asphalt layers being needed for the locations in the upper part of the New York State. The comparison between the new design tables and the table included in the CPDM proved that the new design tables require thinner asphalt layers at low Annual Average Daily Truck Traffic (AADTT) and thicker asphalt layers at high AADTT than the corresponding designs in the CPDM table.
Author: Fadi Munir Jadoun
Publisher:
ISBN:
Category :
Languages : en
Pages : 470
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Book Description
Author: Ali Qays Abdullah
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 257
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Book Description
New York State Department of Transportation (NYSDOT) has used the AASHTO 1993 Design Guide for the design of new flexible pavement structures for more than three decades. The AASHTO 1993 Guide is based on the empirical relationships developed for the data collected in the AASHO Road Test in the early 1960's. A newer pavement design method, called the Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed by the National Cooperative Highway Research Program to provide a more efficient and accurate design method and based on sound engineering principles. The MEPDG models have been incorporated in the AASHTOWare Pavement ME 2.1 software program that can be purchased from AASHTO. Due to the advanced principles and design capabilities of the AASHTOWare program, NYSDOT decided to implement the MEPDG and calibrate the distress models included in the software for the conditions in the state. The work conducted in this research included the local calibration of the distress models for the North East (NE) region of the United States. Design, performance and traffic data collected on Long Term Pavement Performance (LTPP) sites in the NE region of the United States were used to calibrate the distress models. First, the AASHTOWare Pavement ME 2.1 with global calibration factors was used to compare the predicted and measured distresses, values that were used for model calibration. The local bias was assessed for all distresses models except for the longitudinal cracking model; it was found the bias existed for this model even after calibration. The thermal cracking model was not calibrated because of erroneous measured data. The calibration improved the prediction accuracy for the rutting, fatigue cracking and smoothness prediction models. The AASHTOWare software was used to run design cases for combinations of traffic volume and subgrade soil stiffness (Mr) for twenty-four locations in New York State. The runs were performed for a road classified as Principal Arterial Interstate, the 90% design reliability level and 15 years design period. State-wide average traffic volume parameters and axle load spectra were used to define the traffic. The NYSDOT's Comprehensive Pavement Design Manual (CPDM) was initially used to obtain pavement design solutions. The thicknesses for the select granular subgrade materials and the asphalt layer thicknesses were varied to include several values higher and lower than the thickness recommended by CPDM. The thicknesses of asphalt surface and binder layers were kept constant; only the thickness of the asphalt base layer was changed. For each design combination, the design case with thinnest asphalt layer for which the predicted distress was less the performance criteria was selected as the design solution. The design solutions for each of the 24 locations were assembled in design tables. The comparison of the design tables showed that some variation in the design thickness for the asphalt layers exists even, with thicker asphalt layers being needed for the locations in the Upper part of the New York State. The comparison between the new design tables and the table included in the CPDM proved that the new design tables require thinner asphalt layers at low AADTT and thicker asphalt layers at high AADTT than the corresponding design in the CPDM table. For stiff subgrade soil and low AADTT, the design thicknesses are almost the same in the new design tables and in the CPDM table.
Author: Taylor J. Kasperick
Publisher:
ISBN: 9781303160103
Category : Pavements
Languages : en
Pages : 190
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Book Description
The Mechanistic-Empirical Pavement Design Guide (MEPDG) was released in 2004 under NCHRP Project 1-37A. Since that time, considerable efforts to calibrate the program and its performance prediction models for local conditions have taken place in multiple states attempting to implement the program. Currently, Wyoming DOT is in the process of implementing the DARWIN-ME (the MEPDG in its most current form) on the interstate and the state highway systems. In order to compliment that effort, this study attempted to develop a set of calibration coefficients and localized traffic inputs that can be used on local paved roads. Wyoming is an energy rich state and has seen an increase in the amount of heavy truck traffic that its roadways encounter, thus requiring locally calibrated inputs for the DARWIN-ME. Predicted distresses using the DARWIN-ME were largely different from measured distresses on local paved roadways included in this study, particularly IRI, rutting, alligator cracking, transverse cracking, and longitudinal cracking. These distresses were measured on the local paved roads using Pathway Services Inc. and the surface imaging that it provided. Inputs for trial runs using the DARWIN-ME were determined through work with local county road maintenance superintendents, WYDOT, and previous research regarding climatic data in Wyoming. Localized traffic inputs were developed using Weigh-In Motion (WIM) data collected on non-interstate roadways across Wyoming. Once a significant error and bias were found between predicted and measured distresses, calibration coefficients for IRI, alligator cracking, rutting, and longitudinal cracking were altered to reduce bias and sum of squared errors. The final calibration coefficients settled on in this study reduced the sum of squared errors and bias significantly. A sensitivity analysis was also performed during this study to determine the effect of layer thicknesses on the prediction capabilities of the DARWIN-ME. The process followed in this study can be utilized by other local governments around the country to help them implement the DARWIN-ME.
