Incorporating spatial variability of pavement foundation layers stiffness in reliability-based mechanistic-empirical pavement performance prediction. (1st December 2018)

• Record Type: Journal Article
• Authors: Alhasan, Ahmad; Ali, Ayman; Offenbacker, Daniel; Smadi, Omar; Lewis-Beck, Colin
• Is Part Of: Transportation geotechnics. Volume 17(2018)Part A
• Page Start: 1
• Page End: 13
• Publication Date: 2018-12-01
• Subjects: Mechanistic-empirical pavement performance -- Risk/reliability surface -- Spatial statistics -- Intelligent compaction -- Stochastic finite element; Engineering geology -- Periodicals; Soil mechanics -- Periodicals; Rock mechanics -- Periodicals; Transportation -- Periodicals; 624.15105
• DOI: 10.1016/j.trgeo.2018.08.001
• Languages: English
• ISSNs: 2214-3912

Abstract

Abstract: Adequate pavement quality and performance are critical for road users' safety, ride comfort, vehicle operation and travel delay costs, and vehicle durability. An accurate and robust pavement design is essential for realistic life cycle cost analysis, as well as overall management of the infrastructure. Compared to deterministic design methods, probabilistic methods are more realistic and can capture the inherent uncertainty in pavement and foundation materials; and loading conditions. In this study, spatial variability and systematic measurement errors in foundation layers' (including the base and subgrade layers) stiffness are incorporated in reliability-based mechanistic-empirical (ME) pavement performance models. Geospatial models are used to characterize both, the spatial variability and systematic measurement errors. To predict the long term pavement performance, the geospatial models were used to construct stochastic finite element (FE) models, which were then used to predict the performance based on the mechanistic-empirical pavement design guide equations (MEPDG). It is found that the typical covariance functions, also known as semivariograms or variograms, should be handled carefully when used in probabilistic performance modeling. Separating the inherent spatial variability from other uncertainties is necessary for performing risk and reliability analysis. Moreover, incorporating the inherent spatial variability in the stochastic FE models can alter the location of the critical response as described in the MEPDG.