Enhancements to Punchout Prediction Model in Mechanistic–Empirical Pavement Design Guide Procedure

2013 ◽  
Vol 2367 (1) ◽  
pp. 132-141 ◽  
Author(s):  
Chetana Rao ◽  
Michael I. Darter
Keyword(s):  
Prediction Model ◽  
Pavement Design ◽  
Design Guide

Implementing the Mechanistic–Empirical Design Guide Procedure for a Hot-Mix Asphalt–Rehabilitated Pavement in Indiana

2005 ◽  
Vol 1919 (1) ◽  
pp. 121-133 ◽  
Author(s):  
Khaled A. Galal ◽  
Ghassan R. Chehab
Keyword(s):  
Hot Mix Asphalt ◽  
Pavement Design ◽  
Design Parameters ◽  
Design Procedures ◽  
Design Concepts ◽  
Asphalt Overlay ◽  
Design Guide ◽  
Strategic Goals ◽  
And Performance

One of the Indiana Department of Transportation's (INDOT's) strategic goals is to improve its pavement design procedures. This goal can be accomplished by fully implementing the 2002 mechanistic–empirical (M-E) pavement design guide (M-E PDG) once it is approved by AASHTO. The release of the M-E PDG software has provided a unique opportunity for INDOT engineers to evaluate, calibrate, and validate the new M-E design process. A continuously reinforced concrete pavement on I-65 was rubblized and overlaid with a 13–in.-thick hot-mix asphalt overlay in 1994. The availability of the structural design, material properties, and climatic and traffic conditions, in addition to the availability of performance data, provided a unique opportunity for comparing the predicted performance of this section using the M-E procedure with the in situ performance; calibration efforts were conducted subsequently. The 1993 design of this pavement section was compared with the 2002 M-E design, and performance was predicted with the same design inputs. In addition, design levels and inputs were varied to achieve the following: ( a) assess the functionality of the M-E PDG software and the feasibility of applying M-E design concepts for structural pavement design of Indiana roadways, ( b) determine the sensitivity of the design parameters and the input levels most critical to the M-E PDG predicted distresses and their impact on the implementation strategy that would be recommended to INDOT, and ( c) evaluate the rubblization technique that was implemented on the I-65 pavement section.

Use of Long Term Pavement Performance-Seasonal Monitoring Program Data to Develop and Validate a Generalized Regression Model to Predict the In-Situ Resilient Modulus of Subgrade Soils for Pavement Design and Evaluation

2020 ◽  
Vol 2674 (5) ◽  
pp. 673-684
Author(s):  
Mohamed Elshaer ◽  
Christopher DeCarlo ◽  
Wade Lein ◽  
Harshdutta Pandya ◽  
Ayman Ali ◽  
...  
Keyword(s):  
Prediction Model ◽  
Resilient Modulus ◽  
Monitoring Program ◽  
Pavement Design ◽  
Pavement Performance ◽  
Regression Approach ◽  
Seasonal Monitoring ◽  
Generalized Regression

Resilient modulus (Mr) is a critical input for pavement design as it is the main property used to evaluate the contribution of subgrade to the overall pavement structure. Considering this, practitioners need simple and accurate ways to determine the Mr of in-situ subgrade without the need for expensive and time-consuming testing. The objective of this study is to develop a generalized regression prediction model for in-situ Mr of subgrades, compare it with established prediction models, and assess the model’s predictions on pavement performance using the Mechanistic-Empirical Pavement Design Guide (Pavement ME). The prediction model was built using field data from 30 pavement sections studied in the Long Term Pavement Performance (LTPP) Seasonal Monitoring Program where backcalculated modulus from falling weight deflectometer testing, in-situ moisture contents, and subgrade material properties were considered in the model. Based on the results, it was found that liquid limit, plasticity index, WPI (the product of percent passing #200 and plasticity index), percent coarse sand, percent fine sand, percent silt, percent clay, moisture content, and their respective interactions were significant predictors of in-situ Mr values. The findings showed that the generalized regression approach was able to predict Mr more accurately than predictions from the Witczak model. To assess the application of the predictive model on pavement performance, three LTPP sections located in New York, South Dakota, and Texas were analyzed to predict the rutting performance based on Mr values obtained from the developed generalized prediction model and those obtained from the current Pavement ME model and then compared with rut depths measured in the field. The findings showed that, for coarse-grained subgrades that have a low degree of plasticity, the generalized regression model predicted rutting performance similar to the embedded Pavement ME model. For fine-grained subgrades, the developed model tends to predict lower rut depths which were closer to the field measured rut depths. Overall, the generalized regression approach was successfully applied to create a simple, practical, cost-effective and accurate Mr prediction model that can be used to estimate the stiffness of subgrades when designing and evaluating pavements.

Approaches for local calibration of mechanistic-empirical pavement design guide joint faulting model: a case study of Ontario

2018 ◽  
Vol 21 (11) ◽  
pp. 1347-1361 ◽  
Author(s):  
Shi Dong ◽  
Jian Zhong ◽  
Susan L. Tighe ◽  
Peiwen Hao ◽  
Daniel Pickel
Keyword(s):  
Pavement Design ◽  
Local Calibration ◽  
Design Guide
Sign in / Sign up

Export Citation Format

Share Document