Sustainable Evaluation of “Four New” Asphalt Pavement Maintenance Technologies: A Multi-Objective Grey Coupled Model Approach

Abstract

To address the challenges of small-sample adaptability, single static dimension, and durability attribution bias in evaluating “four new” asphalt pavement maintenance technologies (new technologies, materials, processes, and equipment promoted under the Chinese highway “four-new” initiative), this paper proposes a GWO-optimized static-dynamic coupling grey evaluation model. The model integrates parameterized iterative standardization with bi-objective optimization, a bidirectional grey A-S relational model for objective weighting and factor direction identification, and multivariable grey differential decomposition that separates intrinsic decay from external loss. Validation on 13 coastal highway sections in Jiangsu, China, yields a restoration error of 1.27% and a DHGM MAPE of 3.59%, with grade consistency of 90% (9/10) for the training set and 3/3 for the validation set; as the latter contains only three sections, this is reported as a preliminary indication rather than evidence of generalization. Compared with eight mainstream methods, the model attains 92.31% full-sample consistency, a disturbance-robust MAPE of 2.15%, and a small-sample attenuation rate of 8.72%, offering a decision-support tool for sustainable life-cycle pavement maintenance.