Balanced Mix Design and Performance Analysis of High-Modulus Asphalt Mixtures

The objective of this study was to design mixtures that satisfy multiple performance criteria to build long-life pavement. A balanced mix design was employed to optimize AC-16, BBME-13, and EME-20 mixtures. The initial asphalt contents were determined using 4% air voids for the AC-16 mixture and the abundance coefficient K for BBME-13 and EME-20. Mixtures with two additional asphalt contents were also tested. A uniaxial penetration test, semi-circle bending test (to measure the flexibility index), and semi-circle bending test (to measure fracture energy) were used as performance tests. Then, a performance space diagram was created for a balanced analysis of the mixtures’ high-, intermediate-, and low-temperature performance. Finally, fatigue performance was verified. The results show that BMD could be used to evaluate different asphalt mixtures. The 20# BBME-13 (5.1%, 5.4%, and 5.7%), 50# BBME-13 5.4%, and 20# AC-16 4.7% mixtures had well-balanced performances and are recommended. The BBME-13 and EME-20 mixtures had higher fatigue lives and lower sensitivity to stress than the AC-16 mixture. For the BBME-13 and AC-16 mixtures, reducing the asphalt grade significantly increased fatigue life and decreased the sensitivity to stress. At a low stress ratio, the 20# EME-20 mixture had the best fatigue performance, whereas at a high stress ratio, the 20# BBME-13 mixture had the best fatigue performance.

Authors

Institutions

Publication Details

Journal
Materials
Published
2026-06-30
DOI
https://doi.org/10.3390/ma19132777
Primary Topic
Asphalt Pavement Performance Evaluation
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Balanced Mix Design and Performance Analysis of High-Modulus Asphalt Mixtures

Xinhai Liu, N Li, Qirong Li, Peiwen Hao et al.
Materials
Asphalt Pavement Performance Evaluation
article

Balanced Mix Design and Performance Analysis of High-Modulus Asphalt Mixtures

Xinhai Liu, N Li, Qirong Li, Peiwen Hao, Xinquan Xu, Jie Yang, Jiwei Liu
article en

Abstract

The objective of this study was to design mixtures that satisfy multiple performance criteria to build long-life pavement. A balanced mix design was employed to optimize AC-16, BBME-13, and EME-20 mixtures. The initial asphalt contents were determined using 4% air voids for the AC-16 mixture and the abundance coefficient K for BBME-13 and EME-20. Mixtures with two additional asphalt contents were also tested. A uniaxial penetration test, semi-circle bending test (to measure the flexibility index), and semi-circle bending test (to measure fracture energy) were used as performance tests. Then, a performance space diagram was created for a balanced analysis of the mixtures’ high-, intermediate-, and low-temperature performance. Finally, fatigue performance was verified. The results show that BMD could be used to evaluate different asphalt mixtures. The 20# BBME-13 (5.1%, 5.4%, and 5.7%), 50# BBME-13 5.4%, and 20# AC-16 4.7% mixtures had well-balanced performances and are recommended. The BBME-13 and EME-20 mixtures had higher fatigue lives and lower sensitivity to stress than the AC-16 mixture. For the BBME-13 and AC-16 mixtures, reducing the asphalt grade significantly increased fatigue life and decreased the sensitivity to stress. At a low stress ratio, the 20# EME-20 mixture had the best fatigue performance, whereas at a high stress ratio, the 20# BBME-13 mixture had the best fatigue performance.

MaterialsVol. 19(13)
Chang'an University (CN), Construction Development (Qatar) (QA), Research Institute of Highway (CN), Guangzhou Electronic Technology (China) (CN)
Guangdong Science and Technology Department
Openalex Percentile: Top 12%
Asphalt Pavement Performance Evaluation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.