Evaluation of Curing Effects on Cold In-Place Recycled (CIR) Materials

Most cold In-place recycled (CIR) construction use asphalt emulsion or foamed asphalt with or without active fillers as a stabilizing agent. To ensure the CIR layer gains appreciable stiffness and strength to support traffic, the stabilizing agents have to undergo curing (to gradually develop strength). If traffic is allowed on the CIR layer before sufficient strength and structural capacity is gained, premature damage will occur. Lack of a fast and reliable procedure to determine the extent of in-situ curing significantly increases the risk of such damage. Present construction specifications rely on empirically based time recommendations to ensure sufficient curing. Current empirical time estimates do not account for material variations, climatic inputs and construction process differences. This research used a combination of in-situ testing of actual CIR construction projects and supplementary laboratory tests to develop a model for pavement engineers and practitioners to reliably predict the recommended time (as a function of mechanical property) for allowing traffic and/or placing of overlay on CIR layers. The prediction model incorporates the critical factors that influence curing in CIR including stabilizer type and amount, stabilizer type and amount, initial moisture content, in-situ density, curing temperature and relative humidity. Rigorous regression analysis and machine learning approaches were employed to develop the model, which was further converted into a user-interactive web-based tool (available at: https://annits-predictor.netlify.app/)

Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement

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