Laboratory Investigation and Machine Learning Modeling of Road Pavement Asphalt Mixtures Prepared with Construction and Demolition Waste and RAP

Fabio Rondinella, Cristina Oreto, Francesco Abbondati and Nicola Baldo ()
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Fabio Rondinella: Polytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via del Cotonificio 114, 33100 Udine, Italy
Cristina Oreto: Department of Civil, Construction and Environmental Engineering (DICEA), Federico II University of Naples, 80125 Naples, Italy
Francesco Abbondati: Department of Engineering, University of Naples Parthenope, 80143 Naples, Italy
Nicola Baldo: Polytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via del Cotonificio 114, 33100 Udine, Italy

Sustainability, 2023, vol. 15, issue 23, 1-15

Abstract: Due to the decreasing availability of virgin materials coupled with an increased awareness of environmental sustainability issues, many researchers have focused their efforts on investigating innovative technological solutions in the civil engineering domain. This paper aims to evaluate the suitability of construction and demolition waste (C and DW) and reclaimed asphalt pavement (RAP) reused within asphalt mixtures (AMs) prepared for the binder layer of road pavements. Both hot and cold mixing methodologies were investigated. The technical assessment was based on the volumetric and mechanical suitability, according to saturated surface dry voids (SSDV) and indirect tensile strength (ITS) tests carried out at 10 °C, respectively. Laboratory findings showed that all the hot AMs matched the desired target SSDV at the design gyrations number at different optimum bitumen content levels, alternatively showing a non-significant variation or a significant increase in ITS compared to conventional hot mix asphalt. Conversely, the cold AMs with cement and emulsion bitumen showed a greater volume of voids and moisture sensitivity, and lower temperature susceptibility compared to hot AMs, reaching, on average, 11% lower ITS when using coarse C and DW aggregates and 43% lower ITS when using filler from C and DW. These volumetric and mechanical properties were modeled by means of support vector machines and categorical boosting (CatBoost) machine learning algorithms. The results proved to be satisfactory, with CatBoost determination coefficients R 2 referring to SSDV and ITS equal to 0.8678 and 0.9916, respectively. This allowed for the mechanical performance of these sustainable mixtures to be predicted with high accuracy and implemented within conventional mix design procedures.

Keywords: road pavement; asphalt mixtures; construction and demolition waste; RAP; machine learning; support vector machine; categorical boosting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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