The results from the study show that backcalculation methods can be used to determine the moduli of aggregate and asphalt surfaced pavement layers. Finally, a regression equation was developed for the prediction of backcalculated modulus from pavement surface deflection basin for aggregate surfaced roads. Also, for a known stress state, attempts were made to develop a regression equation which can be used to predict modulus as a function of dry density and moisture content. Attempts were made to develop regression equations relating subgrade moisture content to easy-to-measure variables, such as rainfall, site aspect, pavement thickness and elevation. Also, the backcalculated moduli were compared to laboratory determined values. The backcalculated moduli obtained from different equipment for any test site were compared to each other. The backcalculation procedures used were BISDEF, MODCOMP2, and SEARCH. The deflection data collected using NDT devices were used to backcalculate pavement layers and subgrade moduli. A total of 27 project sites were studied. Samples of the pavement materials and subgrade were taken for evaluation in the laboratory using standard methods. Soil moisture cells were implanted in the subgrade to monitor the moisture content and temperature of the subgrade. Pavement surface deflection measurements were taken using three NDT devices, namely the Dynaflect, Road Raterand Falling Weight Deflectometer (FWD). This was achieved by the use of Nondestructive Testing Methods (NDT) and backcalculation techniques. As a result, consistent pavement layer moduli can be obtained through this inverse analysis procedure.This study involved the determination of the structural layer properties of aggregate and asphalt surfaced pavements for use in the evaluation of pavement load carrying capacity and the seasonal effects of moisture.
The recorded time histories of the LWD load were used as the known inputs into the pavement system while the measured time-histories of surface central deflections and subgrade deflections measured with a linear variable differential transformers (LVDT) were considered as the outputs. While the common practice in backcalculating pavement layer properties still assumes a static FWD load and uses only peak values of the load and deflections, dynamic analysis was conducted to simulate the impulse LWD load. A lightweight deflectometer (LWD) was used to infer the moduli of instrumented three-layer scaled flexible pavement models. In this study, an inverse analysis procedure that combines the finite element analysis and a population-based optimization technique, Genetic Algorithm (GA) has been developed to determine the pavement layer structural properties. Backcalculation of flexible pavement layer properties is an inverse problem with known input and output signals based upon which unknown parameters of the pavement system are evaluated. A backcalculation procedure is commonly used to estimate the pavement layer moduli based on the non-destructive falling weight deflectometer (FWD) tests. With the movement towards the implementation of mechanistic-empirical pavement design guide (MEPDG), an accurate determination of pavement layer moduli is vital for predicting pavement critical mechanistic responses.