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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: 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:
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: 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: 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: Syed Waqar Haider
Publisher:
ISBN:
Category : Asphalt emulsion mixtures
Languages : en
Pages :
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Book Description
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.
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: Matthew L. Steele
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 248
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Book Description
Author: Danny Xingqiang Xiao
Publisher:
ISBN: 9781267549495
Category : Pavements
Languages : en
Pages : 544
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Book Description
Reliability used in the Mechanistic Empirical Pavement Design Guide (MEPDG) is a congregated indicator defined as the probability that each of the key distress types and smoothness will be less than a selected critical level over the design period. For such a complex system as the MEPDG which does not have closed-form design equations, classic reliability methods are not applicable. A robust reliability analysis can rely on Monte Carlo Simulation (MCS). The ultimate goal of this study was to improve the reliability model of the MEPDG using surrogate modeling techniques and Monte Carlo simulation. To achieve this goal, four tasks were accomplished in this research. First, local calibration using 38 pavement sections was completed to reduce the system bias and dispersion of the nationally calibrated MEPDG. Second, uncertainty and risk in the MEPDG were identified using Hierarchical Holographic Modeling (HHM). To determine the critical factors affecting pavement performance, this study applied not only the traditional sensitivity analysis method but also the risk assessment method using the Analytic Hierarchy Process (AHP). Third, response surface models were built to provide a rapid solution of distress prediction for alligator cracking, rutting and smoothness. Fourth, a new reliability model based on Monte Carlo Simulation was proposed. Using surrogate models, 10,000 Monte Carlo simulations were calculated in minutes to develop the output ensemble, on which the predicted distresses at any reliability level were readily available. The method including all data and algorithms was packed in a user friendly software tool named ReliME. Comparison between the AASHTO 1993 Guide, the MEPDG and ReliME was presented in three case studies. It was found that the smoothness model in MEPDG had an extremely high level of variation. The product from this study was a consistent reliability model specific to local conditions, construction practices and specifications. This framework also presented the feasibility of adopting Monte Carlo Simulation for reliability analysis in future mechanistic empirical pavement design software.
Author: Nima Kargah-Ostadi
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 140
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Book Description
This research study devised two scenarios for application of multi-objective optimization to enhance calibration of performance models in the American Association of State Highway and Transportation Officials (AASHTO) AASHTOWare® Pavement ME Design software.(1) In the primary scenario, mean and standard deviation of prediction error are simultaneously minimized to increase accuracy and precision at the same time. In the second scenario, model prediction error on data from Federal Highway Administration’s Long-Term Pavement Performance test sections and error on available accelerated pavement testing data are treated as independent objective functions to be minimized simultaneously. The multi-objective optimization results in a final pool of tradeoff solutions, where none of the viable sets of calibration factors are eliminated prematurely. Exploring the final front results in more reasonable calibration coefficients that could not be identified using single-objective approaches. This report demonstrates the application of engineering judgment and qualitative criteria to select reasonable calibration coefficients from the final pool of solutions that result from the multi-objective optimization. More reasonable calibration factors result in a more justifiable pavement design considering multiple aspects of pavement performance. This investigation revealed that simply evaluating the bias and standard error is not adequate for a comprehensive evaluation of performance prediction models.
Author: C. E. Zapata
Publisher: Transportation Research Board National Research
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 76
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Book Description
"This report summarizes the results of research to evaluate, calibrate, and validate the Enhanced Integrated Climatic Model (EICM) incorporated in the original Version 0.7 (July 2004 release) of the Mechanistic-Empirical Pavement Design Guide (MEPDG) software with measured materials data from the Long-Term Pavement Performance Seasonal Monitoring Program (LTPP SMP) pavement sections. The report further describes subsequent changes made to the EICM to improve its prediction of moisture equilibrium for granular bases. The report will be of particular interest to pavement design engineers in state highway agencies and industry ..."--Foreword.
