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A platform for research: civil engineering, architecture and urbanism
Investigation of average asphalt film thickness of dense graded asphalt mixtures with compaction effects
Highlights A compaction-property-dependent model for asphalt film thickness was developed. Aggregate surface area factors and shape coefficients were derived and verified. 112 dense-graded asphalt mixtures from different sources were collected and analyzed. Two regression models and a BP neural network were built for multi-parametric study. Relationship between new AFT and volumetric parameters was evaluated.
Abstract The asphalt mixtures with inadequate durability may become too lean, resulting in low rutting and fatigue resistance. The asphalt film thickness (AFT) has been used as a crucial property for the durability of asphalt mixtures. However, the relationship between AFT and volumetric properties is ambiguous with the absence of compaction effects in the standard film thickness equation. This study aims to investigate the relationship between AFT and volumetric properties of dense-graded asphalt mixtures with consideration of the compaction degree. The compaction-property-dependent (CPD) AFT model considering compaction effects was proposed and compared with the standard AFT equation. Then, asphalt mixtures with 112 different gradations were used to calculate the AFT with and without compaction effects. The relationship between new AFT and volumetric parameters (Pb, VMA, VFA, Vc, Vfine and Vfiller) was investigated by linear correlation analysis. After that, two regression models and a back-propagation (BP) neural network were used to predict the new AFT with volumetric parameters. By comparing the prediction accuracy of these models, a reasonable model with higher prediction accuracy was selected. Results show that the new AFT is 2% to 30% less than the AFT in the standard model, and the surface area of air void boundary has a negative effect on the new AFT. The correlation analysis indicates that the new AFT of dense-graded asphalt mixtures has a rough correlation with changes in VMA, binder content and VFA. The prediction accuracy of the BP neural network has the highest R-squared. The relative importance and sensitivity analysis of the BP neural network verified a relatively good relationship between VMA and new AFT. These findings can give a considerable interest for the tendency of using the new AFT to supplement the current specified VMA criteria.
Investigation of average asphalt film thickness of dense graded asphalt mixtures with compaction effects
Highlights A compaction-property-dependent model for asphalt film thickness was developed. Aggregate surface area factors and shape coefficients were derived and verified. 112 dense-graded asphalt mixtures from different sources were collected and analyzed. Two regression models and a BP neural network were built for multi-parametric study. Relationship between new AFT and volumetric parameters was evaluated.
Abstract The asphalt mixtures with inadequate durability may become too lean, resulting in low rutting and fatigue resistance. The asphalt film thickness (AFT) has been used as a crucial property for the durability of asphalt mixtures. However, the relationship between AFT and volumetric properties is ambiguous with the absence of compaction effects in the standard film thickness equation. This study aims to investigate the relationship between AFT and volumetric properties of dense-graded asphalt mixtures with consideration of the compaction degree. The compaction-property-dependent (CPD) AFT model considering compaction effects was proposed and compared with the standard AFT equation. Then, asphalt mixtures with 112 different gradations were used to calculate the AFT with and without compaction effects. The relationship between new AFT and volumetric parameters (Pb, VMA, VFA, Vc, Vfine and Vfiller) was investigated by linear correlation analysis. After that, two regression models and a back-propagation (BP) neural network were used to predict the new AFT with volumetric parameters. By comparing the prediction accuracy of these models, a reasonable model with higher prediction accuracy was selected. Results show that the new AFT is 2% to 30% less than the AFT in the standard model, and the surface area of air void boundary has a negative effect on the new AFT. The correlation analysis indicates that the new AFT of dense-graded asphalt mixtures has a rough correlation with changes in VMA, binder content and VFA. The prediction accuracy of the BP neural network has the highest R-squared. The relative importance and sensitivity analysis of the BP neural network verified a relatively good relationship between VMA and new AFT. These findings can give a considerable interest for the tendency of using the new AFT to supplement the current specified VMA criteria.
Investigation of average asphalt film thickness of dense graded asphalt mixtures with compaction effects
Zhang, Yao (author) / Chen, Hu (author) / Xiao, Peng (author) / Deng, Yong (author) / Kang, Ai-Hong (author)
2022-01-30
Article (Journal)
Electronic Resource
English
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