Calibration of the Rigid PCC Pavement Performance Models of the AASHTOW Are Pavement ME Design Software in Idaho

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).

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Evaluation of Pavement Performance due to Overload Single-Trip Permit Truck Traffic in Wisconsin

Advances in Civil Engineering ◽

10.1155/2018/1070653 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Hani H. Titi ◽

Nicholas J. Coley ◽

Valbon Latifi

Keyword(s):

Field Work ◽

Test Site ◽

Field Investigation ◽

Pavement Performance ◽

Truck Traffic ◽

Traffic Loads ◽

Pavement Deterioration ◽

Design Software ◽

Surface Distress ◽

Pavement Me Design

This study investigated the impacts of overweight (OW) permit truck traffic on flexible pavement performance in Wisconsin using field investigation and analysis utilizing the AASHTOWare Pavement ME Design software. A database of overweight single-trip permit truck records was analysed to produce a network of Wisconsin corridors heavily travelled by OW trucks. Four Wisconsin highways were selected for investigation due to high levels of OW truck traffic. The research included field work (traffic counts and visual pavement surface distress surveys) and AASHTOWare Pavement ME Design. Comprehensive analyses were conducted to evaluate pavement performance due to normal traffic loads as well as normal traffic loads plus the OW truck traffic loads. The use of mechanistic-empirical (ME) pavement analyses provided a methodology for estimating the proportion of pavement deterioration attributable to OW truck traffic. OW axle load distributions were developed and integrated with baseline truck traffic levels to develop axle load spectra and other traffic input parameters for the ME pavement analysis. The predicted total pavement deterioration levels from the AASHTOWare Pavement ME Design software were generally consistent with the levels of deterioration observed. The proportion of pavement damage and deterioration attributable to OW truck traffic was predicted to constitute a relatively minor proportion of total deterioration, with most distress indices showing relative increases of approximately 0.5% to 4%, with a few outliers. However, due to the small proportion of OW vehicles relative to the overall traffic levels, the OW vehicles were generally predicted to cause up to ten times the per-truck damage as compared with a typical legal-weight truck, depending on the distress mode and the test site.

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Development of Traffic Inputs Library in Pennsylvania for the Use in AASHTOWare Pavement ME Design Software

Airfield and Highway Pavements 2017 ◽

10.1061/9780784480922.005 ◽

2017 ◽

Author(s):

Biplab B. Bhattacharya ◽

Olga Selezneva ◽

Lydia Peddicord

Keyword(s):

Design Software ◽

Pavement Me Design

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Local calibration of rigid pavement performance models using resampling methods

International Journal of Pavement Engineering ◽

10.1080/10298436.2015.1121777 ◽

2015 ◽

Vol 18 (7) ◽

pp. 645-657 ◽

Cited By ~ 4

Author(s):

Syed Waqar Haider ◽

Wouter C. Brink ◽

Neeraj Buch

Keyword(s):

Pavement Performance ◽

Local Calibration ◽

Performance Models ◽

Rigid Pavement ◽

Resampling Methods

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Evaluation of Flexible Pavement Performance Models in Mississippi: A Neural Network Approach

10.1007/978-3-030-77230-7_15 ◽

2021 ◽

pp. 179-190

Author(s):

Patrick Duckworth ◽

Hakan Yasarer ◽

Yacoub Najjar

Keyword(s):

Neural Network ◽

Flexible Pavement ◽

Pavement Performance ◽

Network Approach ◽

Performance Models ◽

Neural Network Approach

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120915889 ◽

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.

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