Research on Strongly Skeleton-Interlocked Gradation of LSAM-50 Mixture Based on Interlock-Dense Theory

Large stone asphalt mixture (LSAM) base layers for pavements demonstrate outstanding rutting resistance and lower engineering costs, potentially mitigating the structural damage often observed in traditional flexible base layer asphalt pavements. Drawing upon the interlock-dense theory and the variable I method, this study proposes optimal gradations for coarse and fine aggregates; it also suggests an optimal mortar dosage through exploring the impact of asphalt mortar dosage on the mixture’s mechanical properties. Further, the study optimizes the sensitivity of the coarse aggregate mass fraction to the mechanical indices of the mixture to refine the critical sieve size. Eventually, LSAM-50 with strongly skeleton-interlocked gradation was proposed, and a test road was paved to verify its performance. Results indicate that continuous grading significantly improves mechanical properties. Specifically, the mineral grading for LSAM-50 is set as S1∶S2∶S4=5∶2∶3, and S6∶S5=3∶1, with a fine aggregate grading reduction coefficient i of 0.75, and an asphalt mortar dosage of Mj+3%. Compared to traditional asphalt base course mixes, LSAM-50 significantly enhances compressive strength (CS) by at least 37%, splitting strength (SS) by at least 13%, and high temperature performance by over 400%. Both the test road surface and the coarse aggregates in the core samples exhibit a cohesive, strongly integrated dense skeleton structure.

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Publication Details

Journal
Journal of Materials in Civil Engineering
Published
2025-08-30
DOI
https://doi.org/10.1061/jmcee7.mteng-20901
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
Type
article
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article

Research on Strongly Skeleton-Interlocked Gradation of LSAM-50 Mixture Based on Interlock-Dense Theory

Changqing Deng, Yingjun Jiang, Chenfan Bai, Chao Li et al.
Journal of Materials in Civil Engineering
Spectroscopy Techniques in Biomedical and Chemical Research
article

Research on Strongly Skeleton-Interlocked Gradation of LSAM-50 Mixture Based on Interlock-Dense Theory

Changqing Deng, Yingjun Jiang, Chenfan Bai, Chao Li, Yu Zhang, Yue Wang, Ya Tan
article en

Abstract

Large stone asphalt mixture (LSAM) base layers for pavements demonstrate outstanding rutting resistance and lower engineering costs, potentially mitigating the structural damage often observed in traditional flexible base layer asphalt pavements. Drawing upon the interlock-dense theory and the variable I method, this study proposes optimal gradations for coarse and fine aggregates; it also suggests an optimal mortar dosage through exploring the impact of asphalt mortar dosage on the mixture’s mechanical properties. Further, the study optimizes the sensitivity of the coarse aggregate mass fraction to the mechanical indices of the mixture to refine the critical sieve size. Eventually, LSAM-50 with strongly skeleton-interlocked gradation was proposed, and a test road was paved to verify its performance. Results indicate that continuous grading significantly improves mechanical properties. Specifically, the mineral grading for LSAM-50 is set as S1∶S2∶S4=5∶2∶3, and S6∶S5=3∶1, with a fine aggregate grading reduction coefficient i of 0.75, and an asphalt mortar dosage of Mj+3%. Compared to traditional asphalt base course mixes, LSAM-50 significantly enhances compressive strength (CS) by at least 37%, splitting strength (SS) by at least 13%, and high temperature performance by over 400%. Both the test road surface and the coarse aggregates in the core samples exhibit a cohesive, strongly integrated dense skeleton structure.

Journal of Materials in Civil EngineeringVol. 37(11)
Shaoyang University (CN), Chang'an University (CN)
Openalex Percentile: Top 84%
Spectroscopy Techniques in Biomedical and Chemical Research
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Research on Strongly Skeleton-Interlocked Gradation of LSAM-50 Mixture Based on Interlock-Dense Theory — Changqing Deng, Yingjun Jiang, et al. · Journal of Materials in Civil Engineering (2025) | TGRS Research Map | TGRS