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- W4200213470 abstract "The objective of the study was to give a suitable prediction method of air voids of asphalt layers in the process of construction. Seven different methods are utilized to predict the air voids of asphalt layers: nonlinear data fitting, Back Propagation neural network (BPNN) algorithm, Radial Basis Function neural network (RBFNN) algorithm, support vector machine for regression (SVR), Gaussian process regression (GPR), regression trees, and random forest regression. The results of laboratory experiments and field tests showed that the intelligent algorithm of SVR is more accurate and suitable for estimating the air voids of asphalt layers. The compaction quality of asphalt layers can be evaluated by this proposed new prediction method of air voids." @default.
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- W4200213470 date "2022-01-01" @default.
- W4200213470 modified "2023-10-18" @default.
- W4200213470 title "Prediction of air voids of asphalt layers by intelligent algorithm" @default.
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- W4200213470 doi "https://doi.org/10.1016/j.conbuildmat.2021.125908" @default.
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