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The current international road standards, in order to give organization and safety, promote the classification of roads according to their technical and functional characteristics beyond their administrative membership, but the procedures are yet strongly based on the expertise's judgment. In fact, although this activity has a great importance for the consequences that produces in terms of responsibility and allocation of economic resources, it is solely based on the quantification of some variables without specifying methods or analytical procedures. In this paper, after an instrumental survey of the road environment, we applied data mining techniques that consider the ‘vagueness’ of the analysed scenario. The type of algorithms used, therefore, permits to quantify a degree of membership (among 0 and 1) of a road to the groupings provided and to prepare any corrective action in order to direct the final result towards a specific class with greater precision. In addition, this method is very flexible and willing to contain new variables or observations at different times with great easiness. Moreover, the geographical location of the individual observations, as it was done also in this research, can be transferred to a GIS system, with a positive impact on maintenance programs. [ABSTRACT FROM AUTHOR]

The compaction of a hot mix asphalt depends on a number of important factors whose control, during the execution of the pavement, is complex; however it is essential to achieve the results envisaged in the design with the least possible cost. Therefore, the evaluation of the effect of these variables on the material density is important as well as their quantification in real time. This research, starting from a survey of some environmental variables, together with the hot mix asphalt density using an electrical impedance device, through the application of a neuro-fuzzy technique, proposes a procedure to classify the most important features. These results can be rapidly deduced during the paving operations; calibrations required to correct the compaction can be applied on site without waiting further time necessary for the extraction of the cores and the subsequent laboratory analysis. In this way it is possible to identify with a better precision the aspects of the environmental context requiring more attention. In addition, the model permits the inclusion of new input variables and additional data that can be recorded in following phases. [ABSTRACT FROM AUTHOR]