Optimizing the dosage of asphalt mixing plant recycled powder in cement-stabilized macadam: A multi-criteria pavement performance evaluation
Source abstract
Abstract
The recycling of recycled powder (RP), a high‑volume by‑product from asphalt mixing plants, into cement‑stabilized macadam (CSM) is hindered by both a scarcity of durability data and the lack of an optimized dosage for multiple key performance indicators. This study systematically evaluated the frost, scour, and drying shrinkage resistance of CSM incorporating 0–7% RP, optimized its dosage by integrating mechanical and durability criteria through an EW‑TOPSIS‑cluster framework, and validated the fatigue performance. Laboratory results revealed a critical performance threshold at 5% RP. Frost resistance improved within the 0–5% RP range but declined beyond this point, whereas both scour resistance and drying shrinkage deteriorated significantly once the RP content exceeded 5%. EW-TOPSIS identified 5% RP as the discrete optimum. Hierarchical clustering further indicated that 4–5% RP formed a high-performance core, while 3% and 6% RP belonged to an adjacent transitional performance zone. Considering the independent drying-shrinkage constraint (RP ≤ 5%), the practical dosage range within the tested dosage set was finally determined as 3–5%. Fatigue tests were further conducted on representative formulations (0% and 5% RP, each with 3% and 5% cement). Among the tested mixtures, the formulation with 5% cement and 5% RP achieved the best fatigue resistance, represented by lg N = 1.311–14.410lg S at 95% reliability. This work provides a performance-driven optimization strategy that combines multi-criteria ranking, cluster-assisted interpretation, and engineering constraint screening to support the high-value and reliable use of RP in sustainable pavement bases.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Asphalt Pavement Performance Evaluation · Recycled Aggregate Concrete Performance · Urban Stormwater Management Solutions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (34)
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