Road performance (e.g., rolling resistance, friction, noise, and hydroplaning) is affected by surface macrotexture. The characteristics above need to be controlled and predicted at the design stage because of the consequences on sustainability and safety. However, macrotexture is quite difficult to govern when designing a mixture and this fact poses many issues, especially when focusing on Low-nominal maximum aggregate size, NMAS, mixtures. Consequently, tools are needed, at the design stage, to better predict a pavement surface macrotexture (e.g., mean texture depth, MTD). For these reasons, the main objective of this study is to set up and implement a model to predict MTD at the design stage. To this end, three main data sets of bituminous mixtures were taken into account. The first data set was created considering technical specifications limits, the second one consisted of data from the literature, and the third one consisted of mixtures especially created in the laboratory (low-NMAS mixtures). Modelling was carried out by setting up several equations, which are based on the sphere packing model and on the concept of filling volume, FV, herein introduced. Results demonstrate that (1) NMAS and FV can explain up to 88 % of MTD variance, (2) the lower specification limits set up in technical specifications must not be considered as good descriptors of the expected MTD, and (3) for low-NMAS mixtures the impact of FV appears less important, while the impact of NMAS seems to be still important. Results can benefit both re-searchers and practitioners.

Road pavement macrotexture estimation at the design stage

Pratico, FG;Fedele, R
2023-01-01

Abstract

Road performance (e.g., rolling resistance, friction, noise, and hydroplaning) is affected by surface macrotexture. The characteristics above need to be controlled and predicted at the design stage because of the consequences on sustainability and safety. However, macrotexture is quite difficult to govern when designing a mixture and this fact poses many issues, especially when focusing on Low-nominal maximum aggregate size, NMAS, mixtures. Consequently, tools are needed, at the design stage, to better predict a pavement surface macrotexture (e.g., mean texture depth, MTD). For these reasons, the main objective of this study is to set up and implement a model to predict MTD at the design stage. To this end, three main data sets of bituminous mixtures were taken into account. The first data set was created considering technical specifications limits, the second one consisted of data from the literature, and the third one consisted of mixtures especially created in the laboratory (low-NMAS mixtures). Modelling was carried out by setting up several equations, which are based on the sphere packing model and on the concept of filling volume, FV, herein introduced. Results demonstrate that (1) NMAS and FV can explain up to 88 % of MTD variance, (2) the lower specification limits set up in technical specifications must not be considered as good descriptors of the expected MTD, and (3) for low-NMAS mixtures the impact of FV appears less important, while the impact of NMAS seems to be still important. Results can benefit both re-searchers and practitioners.
2023
Low-noise
Low-NMAS
Air void content
Mean texture depth modelling
Filling volume
Road pavement
Specifications limits
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/133888
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