The aim of this paper is to contribute to the improvement of the methodologies used in structural pavement evaluation, concerning in particular the backcalculation of layer moduli based on Falling Weight Deflectometer (FWD) together with Ground Penetrating Radar (GPR) test results and using Artificial Neural Network (ANN) technique for the analysis. A brief description of the test procedures and analysis methods is made. The bearing capacity of a pavement is generally evaluated through non-destructive load testing (NDT). Based on the deflections measured in situ and taking into account the pavement layer thickness and material characteristics a response model of the pavement is established. The layer thicknesses are normally obtained through cores and, if available, GPR measurements. The elastic moduli of the layers are adjusted until the deflections calculated are close to the deflections measured during testing (backcalculation). There are several methods for backcalculation of layer moduli from Falling Weight Deflectometer (FWD) test results. However, the results obtained are not always satisfactory, for several reasons, such as the existence of multiple solutions or errors in the pavement layer thickness. An application of the proposed method, combining FWD and GPR tests, is presented. The analysis of the results showed the suitability and advantages of the proposed methodology for structural pavement evaluation. From the experience gathered some recommendations for use of ANN in pavement structural evaluation were drawn, in view of obtaining reliable results.
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