scholarly journals Definition of pavement performance models as a result of experimental measurements

2019 ◽  
Vol 40 ◽  
pp. 201-208
Author(s):  
Michaela Ďurinová ◽  
Ján Mikolaj
Author(s):  
Orhan Kaya ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Danny Waid ◽  
Brian P. Moore

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.


Author(s):  
Abdullah Al-Mansour ◽  
Saleh Al-Swailmi ◽  
Swailem Al-Swailem

2020 ◽  
Vol 15 (3) ◽  
pp. 111-129
Author(s):  
Igoris Kravcovas ◽  
Audrius Vaitkus ◽  
Rita Kleizienė

The key factors for effective pavement management system (PMS) are timely preservation and rehabilitation activities, which provide benefit in terms of drivers’ safety, comfort, budget and impact on the environment. In order to reasonably plan the preservation and rehabilitation activities, the pavement performance models are used. The pavement performance models are usually based on damage and distress observations of rural roads, and can be applied to forecast the performance of urban roads. However, the adjustment of the parameters related to traffic volume, speed and load, climate conditions, and maintenance has to be made before adding them to PMS for urban roads. The main objective of this study is to identify the performance indicators and to suggest pavement condition establishment methodology of urban roads in Vilnius. To achieve the objective, the distresses (rut depth and cracks), bearing capacity, and international roughness index (IRI) were measured for fifteen urban roads in service within a two-year period. The distresses, rut depth and IRI were collected with the Road Surface Tester (RST) and bearing capacity of pavement structures were measured with a Falling Weight Deflectometer (FWD). The measured distresses were compared to the threshold values identified in the research. According to the measured data, the combined pavement condition indices using two methodologies were determined, as well as a global condition index for each road. The analysed roads were prioritized for maintenance and rehabilitation in respect to these criteria. Based on the research findings, the recommendations for further pavement condition monitoring and pavement performance model implementation to PMS were highlighted.


2016 ◽  
Vol 43 (11) ◽  
pp. 986-997 ◽  
Author(s):  
Syed Waqar Haider ◽  
Wouter C. Brink ◽  
Neeraj Buch

2021 ◽  
Author(s):  
Orhan Kaya ◽  
Leela Sai Praveen Gopisetti ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Bora Cetin

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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