Ground penetrating radar (GPR) is an intelligent sensor technique that has led to a powerful nondestructive testing (NDT) method for road pavement evaluation. Recent improvements in hardware and, in particular, software processing have contributed to the rapidly expanding popularity and usability of this technique in the pavement engineers community. In the present work, a GPR sensor system mounted on a driving van was used to acquire data from an asphalt pavement. Two different central frequency air-coupled horn antennas were used for data acquisition. The collected radar data was processed and analyzed in order for the accuracy of the antennas to be evaluated. For this purpose, the asphalt layer dielectric values and related thickness values were also estimated. Based on the GPR data analysis results, the performance of the two horn antennas in terms of sensing pavement subsurface is presented and discussed in the paper

Road safety is connected with pavement skid resistance which often triggers pavement maintenance issues. So far, many laboratory studies have been conducted trying to find a link between skid resistance and macrotexture. However, the findings cannot be extrapolated always in the field because of the multi-parametric nature of skid resistance. This triggered the present study that aims to identify key aspects of skid resistance and its main textural components, macro and microtexture. The goal is twofold: to present and assess the effect of seasonal variation on a long-term basis focusing mainly on Mediterranean climate zone and illustrate the cumulative traffic effect on skid resistance and macrotexture in the field, while paying attention to the key-role of microtexture. Study findings showed that under the effect of cumulative traffic, macrotexture and skid resistance start presenting an inverse trend. This trend seems to be related to the equilibrium phase of skid resistance performance, after which seasonal variation becomes less pronounced. In addition, the cumulative traffic in conjunction with weather variations are proved to be critical for the microtexture component. Ultimately, the study findings demonstrated the necessary awareness of constraints in using only macrotexture data to develop models for predicting field skid resistance levels.

This article aims to investigate the feasibility of incorporating of an artificial neural network (ANN) as an innovative technique for modelling the pavement structural condition, into pavement management systems. For the development of the ANN, strain assessment criteria are set in order to characterise the structural condition of flexible asphalt pavements with regards to fatigue failure. This initial task is directly followed with the development of an ANN model for the prediction of strains primarily based on in situ field gathered data and not through the usage of synthetic databases. For this purpose, falling weight deflectometer (FWD) measurements were systematically conducted on a highway network, with ground-penetrating radar providing the required pavement thickness data. The FWD data (i.e. deflections) were back-analysed in order to assess strains that would be utilised as output data in the process of developing the ANN model. A paper exercise demonstrates how the developed ANN model combined with the suggested conceptual approach for characterising pavement structural condition with regard to strain assessment could make provisions for pavement management activities, categorising network pavement sections according to the need for maintenance or rehabilitation. Preliminary results indicate that the ANN technique could help assist policy decision makers in deriving optimum strategies for the planning of pavement infrastructure maintenance.