首页|Studies from University of Georgia Yield New Data on Machine Learning (Involving Prediction of Dynamic Modulus In Asphalt Mix Design With Machine Learning and Mechanical-empirical Analysis)
Studies from University of Georgia Yield New Data on Machine Learning (Involving Prediction of Dynamic Modulus In Asphalt Mix Design With Machine Learning and Mechanical-empirical Analysis)
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NETL
NSTL
Elsevier
Investigators publish new report on Machine Learning. According to news originating from Athens, Georgia, by NewsRx correspondents, research stated, “Dynamic modulus (|E*|) plays a dominant role in comprehensively capturing the mechanical behavior of asphalt mixture. Many researchers tried to consider |E*| as a performance indicator for a mix design stage, but the cost of time and labor for experimentally measuring the |E*| is high.” Financial support for this research came from Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS), a US Department of Transportation University Transportation Center. Our news journalists obtained a quote from the research from the University of Georgia, “Moreover, establishing harmonized |E*| criterion is unrealistic. To involve |E*| in an asphalt mix design, this paper improved the Superpave volumetric mixture design via controlling the performance (rutting) of mixtures, which were estimated by incorporating machine learning (ML) predictive models of |E*| into the mechanicalempirical approach. The data from the concatenation of the original |E*| database and volumetric properties data extracted from the NCHRP 9-19 report were involved. The different ML models including Support Vector Regression (SVR), kernel ridge regression (KRR), artificial Neural Networks (ANN), Gaussian process regression (GPR), gradient boosting (GB), and eXtreme gradient boosting (XGBoost) were optimized, trained and tested, and their performances were compared to empirical Witczak’s predictive equations. To illustrate the improved asphalt mix design, the case study was presented. Candidate asphalt mixtures were prepared by mixing the selected gradation and different asphalt content and subject to a series of |E*| predictions using the best ML model, which were then used to calculate the permanent deformation of these mixtures with elastic layered analysis and transfer functions in MATLAB. The results show that among all the ML models, XGBoost to predict the |E*| at actual scale has the largest prediction accuracy (R2 = 0.9867), and its prediction accuracy is significantly higher than Witczak’s equations. Feature importance analysis suggested that no matter which scale of |E*| is predicted, the main important factors are test conditions and asphalt binder properties.”
AthensGeorgiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Georgia
NETL
NSTL
Elsevier
