01476254

Component

https://doi.org/10.3141/2367-12

http://scholar.google.com....+Li&publication_year=2013

The new AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions for various what-if scenarios. MEPDG performance predictions for anticipated climatic and traffic conditions will depend on the values of the input parameters that characterize pavement materials, layers, design features, and condition. A comprehensive global sensitivity analysis methodology is proposed for evaluating performance predictions for jointed plain concrete pavement to MEPDG inputs for five climatic conditions and three traffic levels. MEPDG inputs evaluated in the analysis include traffic volume, layer thicknesses, material properties, groundwater depth, and geometric parameters. Correlations between MEPDG inputs were considered as appropriate. The global sensitivity analysis varied all inputs simultaneously across the problem domain for each of the 15 base cases (five climates × three traffic levels). Two response surface modeling approaches, multivariate linear regressions and artificial neural networks, were developed for evaluation of MEPDG input sensitivities across the problem domain. The response surface modeling approaches based on artificial neural networks not only provided robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs but also captured the variation in sensitivity across the problem domain. The normalized sensitivity index for the design limit proposed in the study provides practical interpretation of sensitivity by relating a given percentage change in an MEPDG design input to the corresponding percentage change in predicted distress relative to its design limit value.

01501242

13-2690

English

pp 113–122

2013

Transportation Research Record: Journal of the Transportation Research Board

9780309286909

Print

Figures (4) ; References (17) ; Tables (5)

Concrete pavements; Linear regression analysis; Neural networks; Pavement joints; Pavement performance; Sensitivity analysis

Mechanistic-Empirical Pavement Design Guide

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

PRP, TRIS, TRB, ATRI

Feb 5 2013 12:33PM

• Case Study for Evaluating Benefits of Pavement Research: Final Results
• Characteristics of Base Friction for Concrete Pavement Structure in China: Experimental Study
• Cracking Behavior of Continuously Reinforced Concrete Pavements in Belgium: Characterization of Current Design Concept
• Detection of Subsurface Joint Deterioration: Blind Test Comparison of Ultrasound Array Technology with Conventional Nondestructive Methods
• Development of Texture Measurement System Based on Continuous Profiles from Three-Dimensional Scanning System
• Enhancements to Punchout Prediction Model in Mechanistic–Empirical Pavement Design Guide Procedure
• Evaluation of Structural Response of Cracked Pavements at CEDEX Transport Research Center Test Track
• Matched Filtering Algorithm for Pavement Cracking Detection
• Mobile Cross-Slope Measurement Method Using Lidar Technology
• Modeling of Slab–Foundation Friction in Jointed Concrete Pavements Under Nonlinear Thermal Gradient or Traffic Loads
• Optimal Averaging and Localized Weak Spot Identification of Traffic Speed Deflectometer Measurements
• Spectral Analysis of Surface Wave Scanning for Identifying Debonding Conditions Between Hot-Mix Asphalt Layers in Pavements
• Three-Dimensional Finite Element Modeling of Instrumented Airport Runway Pavement Responses