Determination by the Falling Weight Deflectometer of the In-situ Subgrade Resilient Modulus and Effective Structural Number for I-77 in Virginia

Report No: 07-R1

Published in 2007

About the report:

The Virginia Department of Transportation (VDOT) manages approximately 27,000 lane-miles of interstate and primary roadways, of which interstate pavements comprise approximately 5,000 lane-miles. These pavements consist of flexible, rigid, and composite pavements. Virginia's pavements are managed using an asset management system (AMS) that incorporates a pavement management system (PMS), which aids VDOT in determining the funding required for various levels of pavement maintenance (i.e., preventive maintenance, rehabilitation, or reconstruction activities). As part of VDOT's AMS (PMS) system, a large portion of the interstate pavement system was visually rated annually to determine a condition index based on load-related and non-load related distresses. Recently, VDOT began using an automated distress collection procedure for this task that incorporates the measurement of pavement condition data such as the international roughness index, rutting in both wheel paths, cracking, and number of patches and potholes. However, there is no current protocol to assess the structural capacity of the pavement on a network level and thus determine the remaining load-carrying capacity (service life) of a pavement structure. Many state departments of transportation use the falling weight deflectometer (FWD) to collect pavement deflection data at the project or network level. The analysis of these data provides the effective roadway resilient modulus, the effective in-situ structural number, the pavement layer moduli, the effective in-situ layer coefficient, or all of these parameters. This process is accomplished through a backcalculation procedure using routines that use the FWD deflection data, known as the deflection basins; the FWD load history; and the pavement layer thicknesses as inputs to this procedure. VDOT currently uses the 1993 AASHTO Guide for Design of Pavement Structures for the design of its new or rehabilitated pavement structures. As VDOT moves to implement the proposed Mechanistic-Empirical Pavement Design Guide (MEPDG), characterizing existing pavement conditions, including the resilient modulus of the subgrade, is necessary to ensure optimum designs. This study collected the in-situ layer conditions, the in-situ structural number, and the in-situ subgrade resilient modulus and deflection data for Virginia's I-77 using FWD network level testing. This testing was found to be a viable tool to classify existing structural network conditions. The information can be used by pavement designers and pavement management engineers to address network needs in terms of rehabilitation strategies and fund management. The study recommends that structural testing on the network level be conducted for all interstate and primary routes in Virginia and used in conjunction with VDOT's AMS. Obtaining such data through traditional destructive testing requires coring and boring operations that incur traffic control, equipment, and personnel costs. To conduct such operations at the network level would cost VDOT approximately $5.06 million annually. The costs for the FWD network level testing used in this study are estimated at $83,200 annually, resulting in an annual cost savings for VDOT of almost $5 million.

Disclaimer Statement:The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.

Authors

Other Authors

Khaled A. Galal, Javed Alam

Last updated: November 26, 2023

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