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An efficient and robust method for predicting asphalt concrete dynamic modulus
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DOI：10.1080/10298436.2020.1865533.png)
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This study developed gradient decision tree boosting (GDTB) models to estimate dynamic moduli (vertical bar E*vertical bar) of hot mix asphalt (HMA) mixtures. The GDTB used as input the binder properties, mixture volumetric, and aggregate gradation of the mixtures. The data used for training the GDTB were extracted from a report of the National Cooperative Highway Research Program (NCHRP) project 9-19 [Witczak, M., 2006. Simple performance tests: summary of recommended methods and database. Washington, D.C.: Transportation Research Board, No. 547 in NCHRP Report.]. Totally, 7400 records of data for 346 mixtures were involved, among which 6700 were randomly chosen for training, 200 for validation, and 500 for testing. Comparative analyses were conducted among the GDTB, the two Witczak's equations, and two neural networks (NNs). This study emphasized both the predictive accuracy and computation efficiency of the models. The results indicated that the GDTB achieved predictive accuracy that was significantly higher than the Witczak's models and was in parallel to the more complex NNs. Compared to the Witczak's equations, for the viscosity-based model, the GDTB increased the coefficients of determination (R-2) by 51.5% (arithmetic) and 11.5% (logarithmic), respectively; for the vertical bar G*vertical bar based model, it respectively increased the R-2 by 22.5% (arithmetic) and 8% (logarithmic). Besides the enhanced predictive accuracy, the GDTB only marginally increased the computing time comparing with the empirical equations.
Keyword:
Gradient decision tree boosting
HMA
dynamic modulus
machine learning
computation efficiency
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