01373453

Component

https://doi.org/10.3141/2305-18

http://scholar.google.com...boub&publication_year=2012

The NCHRP "Mechanistic–Empirical Pavement Design Guide" (MEPDG) is a powerful tool for the design and analysis of highway pavements. Currently, the designer uses an iterative process to select design parameters and predict performance. If performance is unacceptable, the designer must change the design parameters until acceptable performance is achieved. A procedure outlined here used multiple adaptive regression splines (MARS) to develop predictive models obtained from a series of actual pavement design solutions from MEPDG software. Two model structures were developed: a series of models to predict individual pavement distress (rutting, fatigue cracking, and roughness) and a forward solution to predict pavement thickness from a desired level of distress. This model can be used to determine a starting thickness, which then can be used with the MEPDG software to obtain a refined design, potentially with fewer iterations to define an acceptable design thickness. These thickness prediction models can be developed for any subset of desired MEPDG solutions, such as typical designs within a state. The new DARWin-ME software can facilitate the development of these subsets through its sensitivity analysis procedure, which would allow the designer to develop a subset of solutions across a typical range of input values. Results could then be modeled with the MARS process to produce an efficient design solution for pavement thickness along with a quick performance prediction for specific pavement thickness. This procedure can significantly reduce the MEPDG iterations necessary to develop a viable design, to as few as two to three on either side of the predicted MARS model thickness. These results could be merged with a smart decision tree structure to execute the MEPDG procedure efficiently.

01470561

12-4371

English

pp 170–176

2012

Transportation Research Record: Journal of the Transportation Research Board

9780309263139

Print

Figures; References

Decision trees; Mathematical models; Mathematical prediction; Pavement design; Pavement distress; Thickness

DARWin-ME (Computer program); Mechanistic-Empirical Pavement Design Guide; Multivariate Adaptive Regression Splines (MARS)

Design; Highways; Pavements; I22: Design of Pavements, Railways and Guideways

TRIS, TRB, ATRI

Feb 8 2012 5:23PM

• Calibrating "Mechanistic–Empirical Pavement Design Guide" for North Carolina: Genetic Algorithm and Generalized Reduced Gradient Optimization Methods
• Comparison of Design Thicknesses for Flexible Airfield Pavement Based on Agency Limiting Subgrade Strain Criteria
• Concrete Pavement Joint Diagnostics with Ultrasonic Tomography
• Development of Mechanistic–Empirical Design Procedure for Fully Permeable Pavement Under Heavy Traffic
• Establishing Effective Linear Temperature Gradients for Ultrathin Bonded Concrete Overlays on Asphalt Pavements
• Evaluating the Effect of Concrete Slab Curling on Joint Load Transfer Responses
• Evaluation of Representative Loading Frequency for Linear Elastic Analysis of Asphalt Pavements
• Improved Mechanistic–Empirical Continuously Reinforced Concrete Pavement Design Approach with Modified Punchout Model
• Induction Healing of Porous Asphalt
• Joint Load Transfer and Support Considerations for Jointed Precast Concrete Pavements
• Load and Resistance Factors and Design Parameter Offsets for "Mechanistic–Empirical Pavement Design Guide"
• Method for Evaluating Implications of Climate Change for Design and Performance of Flexible Pavements
• Modification of "Mechanistic–Empirical Pavement Design Guide" Procedure for Two-Lift Composite Concrete Pavements
• New Warping and Differential Drying Shrinkage Models for Jointed Plain Concrete Pavements Derived with Nonlinear Shrinkage Distribution
• Rational Use of Terminal Anchorages in Portland Cement Concrete Pavement
• Reliability-Based Mechanistic–Empirical Pavement Design with Statistical Methods
• Structural Coefficient of Open-Graded Friction Course