Classification of measures from deflection tests by means of fuzzy clustering techniques

As everybody knows, non-destructive tests carried out with the Heavy (or Falling) Weight Deflectometer
equipment permit to identify the mechanical properties of the layers constituting a road or an airport
pavement.
The ordinary activity generally causes at least two issues: (a) impossibility to anticipate the stiffness of
the pavement analyzed during the trial; (b) probable mistakes induced by punctual degradations. In the
latter case it would be more appropriate to discard the reading of one or more geophones for a correct
determination of the modules.
In order to overcome the above limitations, we propose a procedure based on a fuzzy clustering technique
that enables the classification of the deflections in real time, reducing the number of drops (generally
equal to 3), with no need for traditional back-analysis. Any uncertainty of the result achieved is
quantified by the fuzzy membership degree for which the analyst has an objective measure of the representativeness
of the data detected.