Analysis of fatigue behaviors of asphalt mixture under actual loading waveforms using pseudo-strain-based approaches

ABSTRACT

This research analyzes the fatigue behaviours of asphalt mixture under haversine wave and two more realistic loading waveforms induced by the single axle and tandem axles, based on the pseudo-strain-based approaches. It is found that the fatigue lives of asphalt mixture under the single-axle wave are higher than those under the haversine wave, while the tandem–axle wave generates the lowest fatigue life. This fact suggests testing asphalt mixture using the actual loading waves occurring in the field to improve fatigue life prediction of the asphalt layer. Three PSE-related indicators, namely initial dissipated PSE, cumulative dissipated PSE and the average changing rate of PSE per cycle, all generate a unique linear relationship with the mixture's fatigue life on the log–log scale. The linear relationship is independent of strain level and loading waveform. The damage characteristic curves of asphalt mixture obtained from the VECD approach are independent of strain level but affected by loading waveform. One of VECD-related indicators named D^R stays stable as the loading wave or strain level changes, while another indicator named S_app changes considerably at different waves and strain levels. The latter indicator shows the high potential to predict the asphalt mixture’s fatigue life.

KEYWORDS:

• Asphalt mixture
• fatigue life
• loading waveform
• multi-axles
• pseudo strain
• VECD
• dissipated energy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

&This work was supported by National Natural Science Foundation of China: [Grant Number 5210081231]; Postdoctoral Science Foundation China: [Grant Number BX2021216 & 2021M702479]; Research Grants Council, University Grants Committee: [Grant Number 15208518]; Research Grants Council, University Grants Committee: [Grant Number 152568/16E]; Shanghai Municipal Education Commission: [Grant Number 2019-01-07-00-07-E00025].