ABSTRACT
The objective of the current study is to research the suitability of using two bio-oils produced from corncob and birch bark as two bio-rejuvenators to regenerate aged asphalt binders. To this end, the pyrolysis characteristics of corncob and birch bark were analysed using a thermogravimetric analyzer. The chemical characteristics of the bio-rejuvenators were then evaluated via an elemental analyzer and the Gas Chromatograph Mass Spectrometer (GC-MS) test. The results revealed the presence of a great quantity of lightly weighted compounds in bio-oils. Pen70 asphalt was used as control asphalt binders. The Rotational Viscometer (RV) test result showed that bio-rejuvenators could help reduce the viscosity of aged asphalt binders. In addition, the Dynamic Shear Rheometer (DSR) experiments revealed that bio-rejuvenators could reduce the complex shear modulus, decrease percent strain recovery, increase non-recoverable creep compliance and restore the fatigue performance of the aged asphalt. The Bending Beam Rheometer (BBR) tests indicated that the cracking performance of the bio-rejuvenated asphalt was also improved compared with the aged asphalt. The Fourier transform infrared spectra demonstrated that the bio-rejuvenators helped reduce the aged asphalt's carbonyl and sulfoxide indexes. In addition, the Gel Permeation Chromatography (GPC) test indicated that bio-rejuvenators reduced the polydispersity and molecular weight of the aged asphalt.
Acknowledgements
This research was financially supported by the National Natural Science Foundation of China (No. 52308445), Research Grant of Key Laboratory of Transport Industry of Comprehensive Transportation Theory (Nanjing Modern Multimodal Transportation Laboratory), Ministry of Transport, China (No. MTF2023015), Major Technology Demonstration of Epoxy Asphalt in Pavement as a Green Highly Efficient Carbon Mitigation Technology of Jiangsu, China (Major Technology Demonstration Program) (No. BE2022615), Major Science and Technology Project of Nanjing (No. 202209012), National Natural Science Foundation of China (No. 52378444, No. 52078130), The Transportation Science and Technology Project of Ningbo City (Funding Number: 202212). This research work is also supported by the Big Data Computing Center of Southeast University.
Disclosure statement
No potential conflict of interest was reported by the author(s).