Mix design of asphalt plug joint based on response surface method and grey relational analysis

ABSTRACT

To improve the durability of asphalt plug joints (APJs), the principle of central composite design was adopted to design a three-factor and three-level Marshall test scheme for mixtures. Using grey relational analysis, the multi-objective optimisation problem was transformed into a single-objective optimisation problem. The influence weights of the void content (VV), stability (MS), and flow value (FL) on the grey relational grade (GRG) were determined using principal component analysis. A second-order prediction model for the GRG and influence factors was established via a regression analysis. The response surface method was used to analyze the influence law of each influence factor on the VV, MS, FL, and GRG to determine the optimal dosage combination. The mixture produced using the optimal combination design was subjected to a beam bending test, an indirect tensile fatigue test, and a rutting test. The test results indicated that the maximum bending strain and cumulative permanent strain of the APJ were 210,110.3 and 13,428, respectively. The comparison of a commonly used seamless mixture with the APJ mixture revealed that the APJ mixture exhibited a better high-temperature rutting resistance, indicating the suitability of the APJ mixture for small deformations of bridge seamless expansion joints.

KEYWORDS:

• Asphalt plug joint
• principal component analysis
• response surface method
• prediction model
• optimal design

Disclosure statement

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

Additional information

Funding

The authors gratefully acknowledge the financial support provided by the Science Foundation of China Postdoctor (Grant No. 2016M600352), the Science and Technology Agency of Zhejiang Province (Grant No. 2015C33222, LGF19E080012) and the Science and Technology Project of Zhejiang Provincial Department of Transportation (Grant No. 2019H14 and 2018010). Jiaxing Science and Technology Bureau of China under Grant (2021AY10043).