As a result, consistent pavement layer moduli can be obtained through this inverse analysis procedure. 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. The references (4) through (12) provided in the reference section of this manual are informational material on backcalculation analysis.
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.
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