Compaction of Open-Graded HMAs Evaluated by a Fuzzy Clustering Technique

Abstract

The aim of this paper is the proposal of an expeditious procedure to be used during the execution of an asphalt layer for improving the compaction task. This procedure, based on a fuzzy clustering technique, starts from the knowledge of some information recorded by ordinary measuring instruments and provides an aid to the decision-maker on the number of roller passes needed to achieve a specific density at a certain temperature. This result can be deduced with great rapidity during the paving operations on site without waiting for the time spent in the core extraction and in the subsequent laboratory analysis. In this way it is possible to identify more precisely which aspects of the execution have to be corrected for performing the best compaction.

Optimizing Artificial Neural Networks For The Evaluation Of Asphalt Pavement Structural Performance

“…However, even if these outcomes are remarkable, it is interesting to consider in the next studies a different and probably more performing homogenization approach, based on clustering techniques. Indeed, this solution generally represents a very efficient way for grouping different records according to their similarity in a multi-dimensional space (Amadore, Bosurgi, Pellegrino, & Sollazzo, 2016;Bosurgi, Carbone, Pellegrino, & Sollazzo, 2017) and, thus, it is probably beneficial in such a context.…”