Development of Pavement Performance Models to Account for Frost Effects and Their Application to Mechanistic–Empirical Design Guide Calibration

2007 ◽  
Vol 1990 (1) ◽  
pp. 95-101 ◽  
Author(s):  
J. Puccinelli ◽  
N. Jackson
Keyword(s):  
Pavement Performance ◽  
Performance Models ◽  
Design Guide ◽  
Empirical Design

Implementation Initiatives of the Mechanistic–Empirical Pavement Design Guides in Indiana

2005 ◽  
Vol 1919 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Tommy Nantung ◽  
Ghassan Chehab ◽  
Scott Newbolds ◽  
Khaled Galal ◽  
Shuo Li ◽  
...  
Keyword(s):  
Pavement Design ◽  
Sensitivity Analyses ◽  
Pavement Performance ◽  
Design Parameters ◽  
State Highway ◽  
Design Guide ◽  
Input Parameters ◽  
Pavement Structures ◽  
State Highway Agencies ◽  
Empirical Design

The release of the Mechanistic–Empirical Design Guide for New and Rehabilitated Pavement Structures (M-E design guide) generated a new paradigm for designing and analyzing pavement structures. It is expected to replace the commonly used empirical design methodologies. The M-E design guide uses a comprehensive suite of input parameters deemed necessary to design pavements with high reliability and to predict pavement performance and distresses realistically. However, the considerable amount of input needed and the selection of the corresponding reliability level for each might present state highway agencies with complexities and challenges in its implementation. An overview is presented of ongoing investigative studies, sensitivity analyses, and preimplementation initiatives conducted by the Indiana Department of Transportation (INDOT) in an effort to accelerate the adoption of the new pavement design guide by efficiently using existing design parameters and determining those parameters that influence the predicted performance the most. Once the sensitive inputs are identified, the large amount of other required design input parameters can be significantly reduced to a manageable level for implementation purposes. A matrix of trial runs conducted with the M-E design guide software suggests that a higher design level input does not necessarily guarantee a higher accuracy in predicting pavement performance. The software runs also confirmed the need to use input values obtained from local rather than national calibration. Such findings are important for state highway agencies such as INDOT in drafting initiatives for implementing the M-E design guide.

Estimating the Sensitivity of Design Input Variables for Rigid Pavement Analysis with a Mechanistic–Empirical Design Guide

2005 ◽  
Vol 1919 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Kevin D. Hall ◽  
Steven Beam
Keyword(s):  
Plain Concrete ◽  
Relative Sensitivity ◽  
Pavement Performance ◽  
Portland Cement Concrete ◽  
Traffic Loading ◽  
Pavement Distress ◽  
Design Guide ◽  
Pavement Structures ◽  
Input Variables ◽  
Empirical Design

Many highway agencies use AASHTO methods for the design of pavement structures. Current AASHTO methods are based on empirical relationships between traffic loading, materials, and pavement performance developed from the AASHO Road Test (1958–1961). The applicability of these methods to modern-day conditions has been questioned; in addition, the lack of realistic inputs regarding environmental and other factors in pavement design has caused concern. Research sponsored by the NCHRP has resulted in the development of a mechanistic–empirical design guide (M-E design guide) for pavement structural analysis. The new M-E design guide requires more than 100 inputs to model traffic, environmental, material, and pavement performance to provide estimates of pavement distress over the design life of the pavement. Many designers may lack specific knowledge of the data required. A study was performed to assess the relative sensitivity of the models used in the M-E design guide to inputs relating to portland cement concrete materials in the analysis of jointed plain concrete pavements. Twenty-nine inputs were evaluated by analysis of a standard pavement section and change of the value of each input individually. The three pavement distress models (cracking, faulting, and roughness) were not sensitive to 17 of the 29 inputs. All three models were sensitive to six of the 29 inputs. Combinations of only one or two of the distress models were sensitive to six of the 29 inputs. These data may aid designers in focusing on inputs that have the most effect on desired pavement performance.

Use of Long-Term Pavement Performance Data to Develop Traffic Defaults in Support of Mechanistic-Empirical Pavement Design Procedures

2003 ◽  
Vol 1855 (1) ◽  
pp. 176-182 ◽  
Author(s):  
Weng On Tam ◽  
Harold Von Quintus
Keyword(s):  
Pavement Design ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Traffic Data ◽  
Design Procedures ◽  
Load Spectra ◽  
State Highway ◽  
Pavement Structures ◽  
Empirical Design

Traffic data are a key element for the design and analysis of pavement structures. Automatic vehicle-classification and weigh-in-motion (WIM) data are collected by most state highway agencies for various purposes that include pavement design. Equivalent single-axle loads have had widespread use for pavement design. However, procedures being developed under NCHRP require the use of axle-load spectra. The Long-Term Pavement Performance database contains a wealth of traffic data and was selected to develop traffic defaults in support of NCHRP 1-37A as well as other mechanistic-empirical design procedures. Automated vehicle-classification data were used to develop defaults that account for the distribution of truck volumes by class. Analyses also were conducted to determine direction and lane-distribution factors. WIM data were used to develop defaults to account for the axle-weight distributions and number of axles per vehicle for each truck type. The results of these analyses led to the establishment of traffic defaults for use in mechanistic-empirical design procedures.

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.

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