The linear amplitude sweep (LAS) test has been proposed for fatigue specifications of asphalt binders. Recently, a newly developed fatigue failure definition and energy-based failure criterion have been proposed for improving the LAS procedure and data interpreting; however, these improvements only affect LAS data conducted at a constant temperature. This paper, therefore, discusses the loading temperature effects on LAS-based binder fatigue, damage evolution, and final fatigue failure occurrence. Experimental results indicate that both damage characteristic curves (DCCs) and the failure criterion derived from the simplified-viscoelastic continuum damage (S-VECD) model are strongly affected by the testing temperature. However, the temperature shift factors determined from the dynamic shear modulus testing within the linear viscoelastic domain have been proven to be effective in eliminating the temperature influence for both fatigue DCCs and the failure criterion. The validation on binder phase demonstrates that the time-temperature superposition principle (TTSP) shift factor is only a function of temperature and is independent of strain level. TTSP application on both undamaged and damaged material behaviors is able to greatly improve the testing efficiency of the LAS-based binder fatigue characterization.

Asphalt pavement temperatures greatly influence on the bearing capacity and performance, especially in high‐temperature season. The variation rules of pavement temperatures under the high‐temperature range affect the design and maintenance management of the asphalt pavement, as well as the accurate prediction for pavement temperatures. However, asphalt pavement temperature is greatly affected by various strongly correlated environmental factors and cannot be measured directly or predicted effectively. In this project, temperature sensors were embedded in the pavement of in‐service road to collect temperature data by continuous record measurement, and regression model was conducted by the partial least squares method through comprehensive analysis on the pavement temperature data and synchronously environmental data from local weather station measured in July 2013, July 2014, and July 2015. The quantitative relationships in high‐temperature season between environmental factors and pavement temperature were determined, and a model was established to predict the temperature of asphalt pavement based on environmental data. The model was verified by the recorded data from July 1, 2016, to July 31, 2016, and the results indicated that the pavement temperature can be predicted accurately and reliably by the proposed model.