Predictive modelling

Gradation

Engineering

Statistics

Geotechnical engineering

Materials science

Energy (signal processing)

Aggregate (composite)

Asphalt concrete

Computer science

Asphalt

Mathematics

Artificial intelligence

Composite material

Structural engineering

Fracture (geology)

Artificial neural network

Machine learning

Gene expression programming

Measurement

This paper presents innovative machine learning methods called gene expression programming (GEP) and hybrid artificial neural network/simulated annealing (ANN/SA) to predict the fracture energy of asphalt mixture specimens. The GEP and ANN/SA models are developed using an experimental database including a number of disk-shaped compact tension (DC(T)) test results for fracture energy. The fracture energy is formulated in terms of various predictor variables such as asphalt binder performance grading (PG), asphalt content, aggregate size, aggregate gradation, reclaimed asphalt pavement (RAP) content, reclaimed asphalt shingles (RAS) content, crumb rubber content, and test temperature. A calculation procedure is presented to interpret the models and transform them into practical design equations. A sensitivity analysis is conducted to evaluate the effect of these predictor variables on the fracture energy. Based on the results, the proposed design equations accurately characterize the fracture energy of asphalt mixtures. The GEP model appears to be more practical than the ANN/SA model because of its better generalization and simpler functional structure. The models are recommended for pre-design purposes or as a means to determine asphalt mixture fracture energy when testing is not feasible.

New machine learning-based prediction models for fracture energy of asphalt mixtures