Citation

Uddin Ahmed Zihan Z, Elseifi MA, Gaspard K, Zhang Z. Transp. Res. Rec. 2018; 2672(40): 315-325.

Affiliation

1Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 2Pavement Geotechnical Research Administrator, Louisiana Transportation Research Center, Louisiana State University, Baton Rouge, LA Corresponding Author: Address correspondence to Mostafa A. Elseifi: elseifi@lsu.edu

Copyright

(Copyright © 2018, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198118758058

PMID

unavailable

Abstract

The importance of incorporating pavement structural conditions in the selection of maintenance and rehabilitation strategies along with functional indices has been recognized by state agencies. To measure in-service pavement structural capacity, surface deflection under a defined load has been typically used. The traffic speed deflectometer (TSD) has emerged as a continuous deflection-measuring device as it operates at traffic speed and reduces lane closure and user delays. The present study developed a nonlinear regression model to predict pavement structural number (SN) based on surface deflections measured by the TSD along with the total pavement layer thickness and traffic volume. The proposed model was successfully developed and validated with SN calculated based on TSD and falling weight deflectometer deflection data obtained from two testing programs in Louisiana and Idaho. The model was further validated with respect to its prediction of in-service structural capacity loss and deficiency. Based on statistical measures and the model's ability in identifying structurally deficient sections, results showed satisfactory accuracy of the model and supports its use for network-level decision-making processes in the pavement management system.

Language: en