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Incorporation of GPR and FWD into pavement Mechanistic-Empirical design
HighlightsPavement layer thickness and modulus prediction using GPR and FWD tests.Input generation for pavement M-E analysis from the nondestructive tests.Pavement performance evaluation through the M-E analysis.
AbstractA Mechanistic-Empirical (M-E) design based pavement quality or performance prediction methodology incorporating nondestructive test such as Ground Penetrating Radar (GPR) and Falling Weight Deflectometer (FWD) tests is demonstrated in this study. To facilitate, three pavement sites, namely, US285, US54, and I-40 in New Mexico are selected for nondestructive tests such as GPR and FWD. It is observed that the GPR predicted AC layer thickness is more consistent than base layer thickness. The FWD test was also conducted on the same locations, and like GPR, backcalculated layer moduli also shows varying level of inconsistency in different pavement sections. Based on the GPR and FWD tests, each of these three sites is divided into two major areas: one with the mean and the other with the minimum layer thicknesses and modulus. Layer thicknesses as well as moduli are incorporated to the AASHTOWare-ME software to determine load related distresses such as bottom-up crack and rut. Based on the amount of bottom-up cracks, the US54 pavement section is predicted to fail during 15th year of its service life, and the time of failure can be very early in the area with minimum layer thickness and modulus. Based on the rut prediction, pavement sections in US54 and I-40 is predicted to fail early. Finally, this methodology is recommended to implement to perform pavement quality assessment incorporating GPR and FWD tests in both project and network levels.
Incorporation of GPR and FWD into pavement Mechanistic-Empirical design
HighlightsPavement layer thickness and modulus prediction using GPR and FWD tests.Input generation for pavement M-E analysis from the nondestructive tests.Pavement performance evaluation through the M-E analysis.
AbstractA Mechanistic-Empirical (M-E) design based pavement quality or performance prediction methodology incorporating nondestructive test such as Ground Penetrating Radar (GPR) and Falling Weight Deflectometer (FWD) tests is demonstrated in this study. To facilitate, three pavement sites, namely, US285, US54, and I-40 in New Mexico are selected for nondestructive tests such as GPR and FWD. It is observed that the GPR predicted AC layer thickness is more consistent than base layer thickness. The FWD test was also conducted on the same locations, and like GPR, backcalculated layer moduli also shows varying level of inconsistency in different pavement sections. Based on the GPR and FWD tests, each of these three sites is divided into two major areas: one with the mean and the other with the minimum layer thicknesses and modulus. Layer thicknesses as well as moduli are incorporated to the AASHTOWare-ME software to determine load related distresses such as bottom-up crack and rut. Based on the amount of bottom-up cracks, the US54 pavement section is predicted to fail during 15th year of its service life, and the time of failure can be very early in the area with minimum layer thickness and modulus. Based on the rut prediction, pavement sections in US54 and I-40 is predicted to fail early. Finally, this methodology is recommended to implement to perform pavement quality assessment incorporating GPR and FWD tests in both project and network levels.
Incorporation of GPR and FWD into pavement Mechanistic-Empirical design
Ahmed, Mesbah U. (author) / Tarefder, Rafiqul A. (author)
Construction and Building Materials ; 154 ; 1272-1282
2017-06-18
11 pages
Article (Journal)
Electronic Resource
English
GPR , FWD , Pavement M-E , Thickness , Modulus , AASHTOWare-ME
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|Incorporation of GPR and FWD into pavement Mechanistic-Empirical design
Online Contents | 2017
|Incorporation of GPR and FWD into pavement Mechanistic-Empirical design
British Library Online Contents | 2017
|Incorporation of GPR and FWD into pavement Mechanistic-Empirical design
British Library Online Contents | 2017
|