Implementation of Network-Level Falling Weight Deflectometer Survey

Abstract

The Virginia Department of Transportation (DOT) has been using the results of automated video distress surveys to develop a pavement maintenance budget based on a needs assessment. However, these data consist only of quantities of distress that are visually observable at the pavement surface; no information regarding the actual structural capacity of the pavement system is available. Therefore, it is likely that maintenance activities assigned to certain locations are not the optimal treatment because of conditions unseen at the surface. Previous research conducted at the Virginia Transportation Research Council (now the Virginia Center for Transportation Innovation and Research) developed a protocol to collect data on pavement structural capacity by using the falling weight deflectometer on portions of Virginia's Interstate system. Many U.S. state departments of transportation have performed similar network surveys with the falling weight deflectometer to develop structural data for their pavements. Such data may include the deflection, effective resilient modulus of the subgrade, in situ structural number, layer moduli, overall pavement moduli, deflection basin area, modulus of subgrade reaction, area under the pavement profile, and so on. As the Virginia DOT begins the transition from using the 1993 AASHTO Guide for Design of Pavement Structures to using the Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures, in situ structural characterization of the pavement network is necessary to ensure that funding spent on pavement rehabilitation is used optimally. This study presents the results of a network-level falling weight deflectometer survey of Virginia's Interstate system and describes an implementation process in which the data are used in an updated decision tree structure. The results of this study can be used by pavement design and management engineers to ensure that maintenance funding is optimally spent and to develop condition forecasting tools to assist with future funding allocations based on the structural capacity of the pavement.