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Abstract
Interface bonding conditions between pavement layers have a major impact on pavement performance. The objective of this study was to assess interface bonding conditions between pavement layers using falling weight deflectometer deflection measurements. In addition, the effect of interface bonding conditions on the backcalculated layer moduli was investigated. To achieve these objectives, a new methodology was developed based on FWD measurements collected in 46 sections in Louisiana. These measurements were used to backcalculate the layer moduli using MODULUS 6.0 software. BISAR 3.0 software was then used in conjunction with MODULUS 6.0 to backcalculate the asphalt concrete (AC)/base bonding condition and to correct the backcalculated base layer moduli. The results of the proposed methodology were verified through evaluation of extracted cores. Based on the results of this analysis, it was found that the interface bonding conditions significantly influenced the accuracy of the backcalculation analysis. The impact of interface bonding conditions was more significant on the base moduli than on the AC moduli. A bonding index, which is a function of the interface bonding condition, was introduced to describe the quality of bonding between the AC and base layers. Results showed that the bonding index varied with the characteristics of the base layer. Non-stabilised base layers experienced relatively weak interface bonding at the AC/base interface. Cement-stabilised granular bases mostly experienced weak to moderate interface bonding. On the other hand, stabilised base layers treated with reclaimed asphalt pavement exhibited superior interface bonding. A simple artificial neural network model was developed to allow pavement engineers to correct the backcalculated base layer moduli based on interface bonding conditions. The developed model showed an acceptable overall prediction accuracy of 95.1%.