Iowa Experience on Local Calibration of AASHTOWare Pavement ME Design (PMED) for Jointed Plain Concrete Pavements

2021 ◽  
Author(s):  
Orhan Kaya ◽  
Leela Sai Praveen Gopisetti ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Bora Cetin
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Plain Concrete ◽  
Pavement Performance ◽  
Local Calibration ◽  
Performance Models ◽  
Local Conditions ◽  
Calibration And Validation ◽  
State Highway ◽  
Pavement Me Design

The AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance models and the associated AASHTOWare pavement ME design (PMED) software are nationally calibrated using design inputs and distress data largely obtained from National Long-Term Pavement Performance (LTPP) to predict Jointed Plain Concrete Pavement (JPCP) performance measures. To improve the accuracy of nationally-calibrated JPCP performance models for various local conditions, further calibration and validation studies in accordance with the local conditions are highly recommended, and multiple updates have been made to the PMED since its initial release in 2011, with the latest version (i.e., Ver. 2.5.X) becoming available in 2019. Validation of JPCP performance models after such software updates is necessary as part of PMED implementation, and such local calibration and validation activities have been identified as the most difficult or challenging parts of PMED implementation. As one of the states at the forefront of implementing the MEPDG and PMED, Iowa has conducted local calibration of JPCP performance models extending from MEPDG to updated versions of PMED. The required MEPDG and PMED inputs and the historical performance data for the selected JPCP sections were extracted from a variety of sources and the accuracy of the nationally-calibrated MEPDG and PMED performance prediction models for Iowa conditions was evaluated. To improve the accuracy of model predictions, local calibration factors of MEPDG and PMED performance prediction models were identified and gained local calibration experiences of MEPDG and PMED in Iowa are presented and discussed here to provide insight of local calibration for other State Highway Agencies (SHAs).

Pavement Management Systems: Past, Present, and Future

2003 ◽  
Vol 1853 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Ram B. Kulkarni ◽  
Richard W. Miller
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Systems ◽  
Seamless Integration ◽  
Positioning Systems ◽  
Pavement Management Systems ◽  
Transportation Organization ◽  
Pavement Performance Prediction

The progress made over the past three decades in the key elements of pavement management systems was evaluated, and the significant improvements expected over the next 10 years were projected. Eight specific elements of a pavement management system were addressed: functions, data collection and management, pavement performance prediction, economic analysis, priority evaluation, optimization, institutional issues, and information technology. Among the significant improvements expected in pavement management systems in the next decade are improved linkage among, and better access to, databases; systematic updating of pavement performance prediction models by using data from ongoing pavement condition surveys; seamless integration of the multiple management systems of interest to a transportation organization; greater use of geographic information and Global Positioning Systems; increasing use of imaging and scanning and automatic interpretation technologies; and extensive use of formal optimization methods to make the best use of limited resources.

scholarly journals Statistics and Artificial Intelligence-Based Pavement Performance and Remaining Service Life Prediction Models for Flexible and Composite Pavement Systems

2020 ◽  
Vol 2674 (10) ◽  
pp. 448-460
Author(s):  
Orhan Kaya ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Danny Waid ◽  
Brian P. Moore
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Decision ◽  
Performance Models ◽  
Remaining Service Life ◽  
Management Decision Making ◽  
Decision Making Processes

In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predicting pavement performance in network level analysis. Network level pavement performance models using both statistics and AI-based approaches were also developed to evaluate the relative success of these two models for network level pavement performance modeling. As part of this study, in the development of pavement RSL prediction models, automation tools for future pavement performance predictions were developed and used along with the threshold limits for various pavement performance indicators specified by the Federal Highway Administration. These RSL models will help engineers in decision-making processes at both network and project levels and for different types of pavement management business decisions.

Development of Performance-Related Specifications for Hot-Mix Asphalt Pavements Through WesTrack

1997 ◽  
Vol 1575 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Stephen B. Seeds ◽  
Rudramunniyappa Basavaraju ◽  
Jon A. Epps ◽  
Richard M. Weed
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Permanent Deformation ◽  
Fatigue Cracking ◽  
Primary Objective ◽  
Pavement Performance ◽  
Void Content ◽  
Target Values ◽  
Pavement Performance Prediction

The primary objective of the FHWA-sponsored WesTrack project is to further the development of performance-related specifications for hotmix asphalt construction. This objective is being achieved, in part, through the accelerated loading of a full-scale test track facility in northern Nevada. Twenty-six hot-mix asphalt test sections constructed to meet the criteria set forth in a statistically based experiment design are providing performance data that will be used to improve existing (or develop new) pavement performance prediction relationships that better account for the effects that “off-target” values of asphalt content, air-void content, and aggregate gradation have on such distress factors as fatigue cracking, permanent deformation, roughness, raveling, and tirepavement friction. The concept of the planned new performance-related specification and how it will incorporate the modified pavement performance prediction models are described. The current plan for assessing contractor pay adjustments (i.e., penalties and bonuses) based on data collected from the as-constructed pavement is also discussed.

scholarly journals Comparisons of Faulting-Based Pavement Performance Prediction Models

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Weina Wang ◽  
Yu Qin ◽  
Xiaofei Li ◽  
Di Wang ◽  
Huiqiang Chen
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Pavement Performance ◽  
Physical Parameters ◽  
Ann Model ◽  
Climate Data ◽  
Good Tool ◽  
Advantages And Disadvantages ◽  
Lower Accuracy ◽  
Pavement Performance Prediction

Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR) model, artificial neural network (ANN) model, and Markov Chain (MC) model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

Implementation of AASHTOWare Pavement ME Design Software for Asphalt Pavements in Kansas

2019 ◽  
Vol 2673 (4) ◽  
pp. 490-499 ◽  
Author(s):  
Shuvo Islam ◽  
Mustaque Hossain ◽  
Christopher A. Jones ◽  
Avishek Bose ◽  
Ryan Barrett ◽  
...  
Keyword(s):  
Prediction Models ◽  
Permanent Deformation ◽  
Method Comparison ◽  
Implementation Strategies ◽  
Fatigue Cracking ◽  
Performance Models ◽  
Design Guide ◽  
Sample Method ◽  
Aashto 1993 ◽  
Pavement Me Design

Many highway agencies are transitioning from the 1993 AASHTO pavement design guide to the AASHTOWare Pavement ME Design (PMED). Pavement performance models embedded in the PMED software need to be calibrated for new and reconstructed hot-mix asphalt (HMA) pavements. Twenty-seven newly constructed HMA pavements were used to calibrate the prediction models—twenty-one for calibration and six for validation. Local calibration for permanent deformation, top-down fatigue cracking, and the International Roughness Index (IRI) models was done using the traditional split-sample method. Comparison with the results from the 1993 AASHTO design guide for ten new HMA pavement sections with varying traffic levels was done. The results show that the thicknesses obtained from locally calibrated PMED are within 1 inch of the AASHTO 1993 design guide prediction for low to medium-low traffic. For sections with high traffic level, the 1993 AASHTO design guide yielded higher thickness than PMED. The PMED implementation strategies adopted in Kansas and relevant concerns are discussed. Finally, an automated calibration technique has been proposed to help highway agencies to perform periodic in-house calibration of the performance models.

scholarly journals Local calibration of flexible pavement performance models in Michigan

2016 ◽  
Vol 43 (11) ◽  
pp. 986-997 ◽  
Author(s):  
Syed Waqar Haider ◽  
Wouter C. Brink ◽  
Neeraj Buch
Keyword(s):  
Flexible Pavement ◽  
Pavement Performance ◽  
Local Calibration ◽  
Performance Models
Sign in / Sign up

Export Citation Format

Share Document