Download or read book Development of a Master Plan for Calibration and Implementation of the Mechanistic Empirical Pavement Design Guide written by Kevin Dale Hall and published by . This book was released on 2015 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mechanistic empirical Pavement Design Guide Implementation Plan written by Todd E. Hoerner and published by . This book was released on 2007 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: As AASH is expected to eventually adopt the MEPDG at its primary pavement design method, it is critical that the SDDOT become familiar with the MEPGD documentation and associated design software. The research conducted under this project was a first step toward achieving this goal.

Download or read book Guide for the Local Calibration of the Mechanistic empirical Pavement Design Guide written by and published by AASHTO. This book was released on 2010 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Local Calibration of the Mechanistic Empirical Pavement Design Guide for Kansas written by Abu Ahmed Sufian and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Implementation Plan for the New Mechanistic empirical Pavement Design Guide written by Y. Richard Kim and published by . This book was released on 2007 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mechanistic empirical Pavement Design Guide written by American Association of State Highway and Transportation Officials and published by AASHTO. This book was released on 2008 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Local Calibration of Mechanistic Empirical Pavement Design Guide for North Eastern United States written by Shariq A. Momin and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Draft User s Guide for UDOT Mechanistic empirical Pavement Design written by Michael I. Darter and published by . This book was released on 2009 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Validation of the new AASHTO Mechanistic-Empirical Pavement Design Guide's (MEPDG) nationally calibrated pavement distress and smoothness prediction models when applied under Utah conditions, and local calibration of the new hot-mix asphalt (HMA) pavement total rutting model, were recently completed as documented in UDOT Research Report No. UT-09.11 Implementation of the Mechanistic-Empirical Pavement Design Guide in Utah: Validation, Calibration, and Development of the UDOT MEPDG User's Guide, dated October 2009. This Draft User's Guide incorporates the findings of the model validation and local calibration report and provides information for use by UDOT's pavement design engineers during trial implementation of the MEPDG. This information includes an overview of the MEPDG procedure, information on installation of the software, guidelines for obtaining all needed inputs, guidance to perform pavement design using the software for new and rehabilitated HMA pavement and jointed plain concrete pavement (JPCP), and pavement design examples for new HMA pavement and new JPCP using the MEPDG software.

Download or read book Preparation for Implementation of the Mechanistic empirical Pavement Design Guide in Michigan written by Syed Waqar Haider and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of Part 3 was to locally calibrate and validate the mechanistic-empirical pavement design guide (Pavement-ME) performance models to Michigan conditions. The local calibration of the performance models in the Pavement-ME is a challenging task, especially due to data limitations. A total of 108 and 20 reconstruct flexible and rigid pavement candidate projects, respectively, were selected. Similarly, a total of 33 and 8 rehabilitated pavement projects for flexible and rigid pavements, respectively were selected for the local calibration. The selection process considered pavement type, age, geographical location, and number of condition data collection cycles. The selected set of pavement section met the following data requirements (a) adequate number of sections for each performance model, (b) a wide range of inputs related to traffic, climate, design and material characterization, (c) a reasonable extent and occurrence of observed condition data over time. The national calibrated performance models were evaluated by using the data for the selected pavement sections. The results showed that the global models in the Pavement-ME don't adequately predict pavement performance for Michigan conditions. Therefore, local calibration of the models is essential. The local calibrations for all performance prediction models for flexible and rigid pavements were performed for multiple datasets (reconstruct, rehabilitation and a combination of both) and using robust statistical techniques (e.g. repeated split sampling and bootstrapping). The results of local calibration and validation of various models show that the locally calibrated model significantly improve the performance predictions for Michigan conditions. The local calibration coefficients for all performance models are documented in the report. The report also includes the recommendations on the most appropriate calibration coefficients for each of the performance models in Michigan along with the future guidelines and data needs.

Download or read book Calibrating the Mechanistic empirical Pavement Design Guide for Kansas written by Xiaohui Sun (Writer on roads) and published by . This book was released on 2015 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Kansas Department of Transportation (KDOT) is moving toward the implementation of the new American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (MEPDG) for pavement design. The MEPDG provides a rational pavement design framework based on mechanistic-empirical principles to characterize the effects of climate, traffic, and material properties on the pavement performance, as compared with the 1993 AASHTO Guide for Design of Pavement Structures. Before moving to the MEPDG, the nationally calibrated MEPDG distress prediction models need to be further validated and calibrated to the local condition. The objective of this research was to improve the accuracy of the MEPDG to predict the pavement performance in Kansas. This objective was achieved by evaluating the MEPDG-predicted performance of Kansas projects, as compared with the pavement performance data from the pavement management system (PMS), and calibrating the MEPDG models based on the pavement performance data. In this study, 28 flexible pavement projects and 32 rigid pavement projects with different material properties, traffic volumes, and climate conditions were strategically selected throughout Kansas. The AASHTO ME Design software (Version 1.3) was used in this study. The comparisons between the MEPDG-predicted pavement performance using the nationally calibrated models and the measured pavement performance indicated the need for the calibration of the MEPDG models to the Kansas conditions. For new flexible pavements, the MEPDG using the nationally calibrated models overestimated the rutting due to the overprediction of the deformation of the subgrade layer. Biases also existed between the predicted top-down cracking, thermal cracking, and International Roughness Index (IRI) and the measured data. The relationship between the measured and the predicted IRIs was more obvious than that for the cracking. Using the coefficients determined through local calibration in this study, the biases and the standard errors were minimized for all the models based on the statistical analysis. For new rigid pavements, very low mean joint faulting was measured in actual projects as compared with the default threshold of the MEPDG. The type of base course had a minor effect on the pavement performance. The traditional splitting data method was adopted in the process of local calibration. After the local calibration, the biases between the predicted pavement performance (mean joint faulting and IRI) and the measured pavement performance were minimized, and the standard errors were reduced.

Download or read book Implementation of the AASHTO Mechanistic empirical Pavement Design Guide and Software written by and published by . This book was released on 2014 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction -- Mechanistic-Empirical Pavement Design Guide and AASHTOWare Pavement ME Design (TM) Software Overview -- Survey of Agency Pavement Design Practices -- Common Elements of Agency Implementation Plans -- Case Examples of Agency Implementation -- Conclusions.

Download or read book Development of Local Calibration Factors and Design Criteria Values for Mechanistic empirical Pavement Design written by Bryan Smith and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A mechanistic-empirical (ME) pavement design procedure allows for analyzing and selecting pavement structures based on predicted distress progression resulting from stresses and strains within the pavement over its design life. The Virginia Department of Transportation (VDOT) has been working toward implementing ME design by characterizing traffic and materials inputs, training with the models and design software, and analyzing current pavement designs in AASHTOware Pavement ME Design software. This study compared the measured performance of asphalt and continuously reinforced concrete pavements (CRCP) from VDOTs Pavement Management System (PMS) records to the predicted performance in AASHTOware Pavement ME Design. Model coefficients in the software were adjusted to match the predicted asphalt pavement permanent deformation, asphalt bottom-up fatigue cracking, and CRCP punchout outputs to the measured values from PMS records. Values for reliability, design life inputs, and distress limits were identified as a starting point for VDOT to consider when using AASHTOware Pavement ME Design through consideration of national guidelines, existing VDOT standards, PMS rating formulas, typical pavement performance at time of overlay, and the data used for local calibration. The model calibration coefficients and design requirement values recommended in this study can be used by VDOT with AASHTOware Pavement ME Design as a starting point to implement the software for design, which should allow for more optimized pavement structures and improve the long-term performance of pavements in Virginia.

Download or read book Calibrating Mechanistic Empirical Design Guide Permanent Deformation Models Based on Accelerated Pavement Testing written by Feng Hong and published by . This book was released on 2009 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the challenges to the implementation of the mechanistic-empirical pavement design guide (MEPDG) comes from calibrating the transfer functions. This paper focuses on calibration of one of the major distress models in flexible pavement: permanent deformation or rutting. Two key aspects are critical to a successful rutting model calibration: data and method. Regarding the data, existing in-field information only provides total rut depth, which could not meet the requirement of permanent deformation in each structural layer by the MEPDG. Concerning the method, existing work either fails to address calibration factors from a holistic perspective by only focusing on individual sections separately or ignores variability inherent in those factors. In this study, layer-wise permanent deformation from instrumented pavement under accelerated pavement testing serves to accommodate the models calibration. A systematic calibration procedure is established, which globally optimizes all available information across all test sections. Through simulation and numerical optimization, optimal calibration shift factors for three typical flexible pavement materials, asphalt mixture, unbound granular base, and finegrain soil are obtained as 0.60, 0.49, and 0.84, respectively. This implies that the uncalibrated MEPDG is biased toward overprediction of rut depth. It is further suggested that a more rational result for each calibrated factor is to introduce an appropriate distribution to characterize its uncaptured variability. In addition, a case study involving using calibrated MEPDG to predict pavement performance or life indicates that (1) model calibration has a significant impact on the prediction and (2) the "fourth power law" is supported by the MEPDG.

Download or read book Mechanistic empirical Pavement Design Guide Flexible Pavement Performance Prediction Models for Montana Reference manual written by Harold L. Von Quintus and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Calibration of the Mechanistic empirical Pavement Design Guide for Local Paved Roads in Wyoming written by Taylor J. Kasperick and published by . This book was released on 2013 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Analysis of the Mechanistic empirical Pavement Design Guide Performance Predictions written by Stacey D. Diefenderfer and published by . This book was released on 2010 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures (MEPDG) is an improved methodology for pavement design and the evaluation of paving materials. The Virginia Department of Transportation (VDOT) is expecting to transition to using the MEPDG methodology in the near future. The purpose of this research was to support this implementation effort. A catalog of mixture properties from 11 asphalt mixtures (3 surface mixtures, 4 intermediate mixtures, and 4 base mixtures) was compiled along with the associated asphalt binder properties to provide input values. The predicted fatigue and rutting distresses were used to evaluate the sensitivity of the MEPDG software to differences in the mixture properties and to assess the future needs for implementation of the MEPDG. Two pavement sections were modeled: one on a primary roadway and one on an interstate roadway. The MEPDG was used with the default calibration factors. Pavement distress data were compiled for the interstate and primary route corresponding to the modeled sections and were compared to the MEPDG-predicted distresses. Predicted distress quantities for fatigue cracking and rutting were compared to the calculated distress model predictive errors to determine if there were significant differences between material property input levels. There were differences between all rutting and fatigue predictions using Level 1, 2, and 3 asphalt material inputs, although not statistically significant. Various combinations of Level 3 inputs showed expected trends in rutting predictions when increased binder grades were used, but the differences were not statistically significant when the calibration model error was considered. Pavement condition data indicated that fatigue distress predictions were approximately comparable to the pavement condition data for the interstate pavement structure, but fatigue was over-predicted for the primary route structure. Fatigue model predictive errors were greater than the distress predictions for all predictions. Based on the findings of this study, further refinement or calibration of the predictive models is necessary before the benefits associated with their use can be realized. A local calibration process should be performed to provide calibration and verification of the predictive models so that they may accurately predict the conditions of Virginia roadways. Until then, implementation using Level 3 inputs is recommended. If the models are modified, additional evaluation will be necessary to determine if the other recommendations of this study are impacted. Further studies should be performed using Level 1 and Level 2 input properties of additional asphalt mixtures to validate the trends seen in the Level 3 input predictions and isolate the effects of binder grade changes on the predicted distresses. Further, additional asphalt mixture and binder properties should be collected to populate fully a catalog for VDOT's future implementation use. The implementation of these recommendations and use of the MEPDG are expected to provide VDOT with a more efficient and effective means for pavement design and analysis. The use of optimal pavement designs will provide economic benefits in terms of initial construction and lifetime maintenance costs.

Download or read book Enhancement and Local Calibration of Mechanistic empirical Pavement Design Guide written by Hongren Gong and published by . This book was released on 2018 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.