Prediction of low-temperature fracture resistance curves of unmodified and crumb rubber modified hot mix asphalt mixtures using a machine learning approach

Created by W.Langdon from gp-bibliography.bib Revision:1.7964

@Article{GHAFARI:2022:CBM,

• author = "Sepehr Ghafari and Mehrdad Ehsani and Fereidoon {Moghadas Nejad}",
• title = "Prediction of low-temperature fracture resistance curves of unmodified and crumb rubber modified hot mix asphalt mixtures using a machine learning approach",
• journal = "Construction and Building Materials",
• volume = "314",
• pages = "125332",
• year = "2022",
• ISSN = "0950-0618",
• DOI = "doi:10.1016/j.conbuildmat.2021.125332",
• URL = "https://www.sciencedirect.com/science/article/pii/S0950061821030737",
• keywords = "genetic algorithms, genetic programming, R-curve, Crack propagation, Hot mix asphalt, Machine learning, Artificial neural networks, Multi-gene genetic programming",
• abstract = "Fracture resistance curves (R-curves) provide a robust tool for a comprehensive insight into the crack propagation regime in engineering materials. In this paper, an extensive research program is conducted to determine R-curves for hot mix asphalt (HMA) mixtures with varying properties. The experimental results are then used to develop R-curve prediction models following a machine learning approach. Three-point single-edge notched beam (SE(B)) experiments were conducted on HMA mixtures incorporating 0percent, 5percent, 10percent, 15percent, and 20percent crumb rubber at low temperatures. The temperature ranged from + 5 degreeC to -20 degreeC while limestone and siliceous aggregate with two gradations were used in developing mixtures with two base bitumen having performance grades of PG58-22 and PG64-22. It was observed that as the temperature is declined to -20 degreeC, the stable crack growth region is significantly diminished in the R-curves, and the mixtures undergo a brittle fracture with abrupt failure of the specimen. A temperature of -15 degreeC could be determined where the transition from quasi-brittle to brittle fracture occurs. Mixtures fabricated incorporating 20percent crumb rubber exhibited a progressively rising R-curve at the lowest test temperature (-20 degreeC) even in the unstable crack propagation phase, which is a desirable material characteristic. Two prediction models were developed for R-curves. Artificial neural networks (ANN) were used in the first model resulting in an R-square value of 0.965. Due to the black-box nature of the ANN, the multi-gene genetic programming approach was also applied in the prediction of the R-curves to derive a mathematical equation between the input data and the outputs. The R-square equaled 0.870 in this method. R-curves could successfully be predicted by both methods considering the negligible to fair errors",

}

Genetic Programming entries for Sepehr Ghafari Mehrdad Ehsani Fereidoon Moghaddas Nejad

Citations