Evaluating the evolution of fiber-reinforced emulsified asphalt cold-recycled mixture damage using digital image correlation

ABSTRACT

The cold recycled mixture has insufficient resistance to cracking, and the process of fracture is incompletely understood. This study used digital image correlation (DIC) technology and a semi-circular bending test to investigate the anti-crack performances and fracture propagation of basalt fiber-reinforced cold recycled mixture (BFCRM). The load-displacement, full-field strain, full-field displacement, displacement rate and crack propagation map were obtained to evaluate the BFCRM’s anti-crack performances. The control cold recycled mixture without fibers was also prepared for comparison. Meanwhile, the fracture process zone (FPZ) during loading were compared and quantitatively assessed by digital image correlation analyses, and the fiber distribution and microscopic features at fracture surface was observed by scanning electron microscopy. The results show that the formation of microcracks of BFCRM takes more time than that of the control group without fiber. Based on DIC observations, the basalt fibers maintain strong anti-crack performance in cold-recycled mixtures and reduce fracture damage. The fracture surface morphology also revealed that basalt fiber exhibited adsorption, bridging, anchoring, stability, and strengthening effects at micro/nano scale, which further improved anti-crack performance e and hindered crack development of cold recycled asphalt mixtures.

KEYWORDS:

• Fiber-reinforced cold recycled mixture
• digital image correlation
• strain distribution
• anti-crack performances
• fracture process zone
• microstructural characteristic

Disclosure statement

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

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 51978116]; Sichuan Youth Science and Technology Foundation [grant number 2021JDTD0023]; Sichuan Science and Technology Program [grant number 2021YJ0065].