This paper describes the development of novel/state-of-art computational framework to accurately predict the degree of binder activity of a reclaimed asphalt pavement sample as a percentage of the indirect tensile strength (ITS) using a reduced number of input variables that are relatively easy to obtain, namely compaction temperature, air voids and ITS. Different machine learning (ML) techniques were applied to obtain the most accurate data representation model. Specifically, three ML techniques were applied: 6th-degree multivariate polynomial regression with regularization, artificial neural network and random forest regression. The three techniques produced models with very similar precision, reporting a mean absolute error ranging from 12.2 to 12.8% of maximum ITS on the test data set. The work presented in this paper is an evolution in terms of data analysis of the results obtained within the interlaboratory tests conducted by Task Group 5 of the RILEM Technical Committee 264 on Reclaimed Asphalt Pavement. Hence, despite it has strong bonds with this framework, this work was developed independently and can be considered as a natural follow-up.

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In the last few decades, the need for reducing the exploitation of finite sources has led to an increasing use of reclaimed asphalt pavement (RAP) for the construction, maintenance, and rehabilitation of road pavements. During hot mix asphalt (HMA) plant production, RAP can be added at ambient temperature or preheated into special drums. When designing an RAP mixture, it is essential to dose the correct amount of virgin binder according to the percentage of the reactivated aged one. RAP preheating plays a key role in this process, although no reliable methods are available for achieving an accurate estimation. This article presents an innovative digital image-based methodology, named overlapped digital image (OIA) analysis, to better understand the role of reactivated binder on the cracking behavior of HMA-RAP mixtures. A recycled mixture was produced in the laboratory by adding RAP both at room temperature (25°C) and after preheating (150°C) and subsequently tested according to the Superpave IDT at 10°C and 25°C. The tests were combined with the OIA analysis to identify how the strain patterns and the initiation and propagation of fractures developed, according to the reactivation of the aged asphalt binder. The results show important outcomes in terms of cracking behaviors highlighting the important role of RAP preheating for inducing aged binder reactivation.