Mechanistic prediction of rutting in LSAM-50 flexible base pavements: a novel master curve model incorporating temperature and load effects

Permanent deformation (rutting) threatens the structural integrity and service life of asphalt pavements. This study investigated the rutting resistance mechanisms of flexible base pavements incorporating LSAM-50, an ultra-large stone asphalt mixture (nominal maximum aggregate size 53 mm), and developed predictive rutting models. Effects of aggregate gradation, binder type, and temperature were analyzed via shear strength theory, using cohesion c and the internal friction angle φ. Compared with conventional mixtures, LSAM-50 exhibited an approximately 10° higher φ and stronger aggregate interlock, and dynamic stability over 2.7 times that of finer-graded materials. A statistical regression model (SR-DS) with correlation coefficient above 0.95 was established. A rutting-depth master curve method was developed based on time–temperature equivalence, using shift factors to consolidate rutting data onto a reference condition, achieving R 2 > 0.92 and MSE < 0.01. Field measurements confirmed that LSAM-50 flexible base pavements exhibit deflection comparable to semi-rigid base pavements at room temperature, with fewer layers, faster construction, and over 30% material savings. With asphalt layer thicknesses of 42–54 cm, the LSAM-50 pavement system is consistent with perpetual pavement design principles. This study provides a mechanistic understanding and predictive tools for designing durable and rutting-resistant asphalt pavement structures.

Authors

Institutions

Publication Details

Journal
International Journal of Pavement Engineering
Published
2026-08-27
DOI
https://doi.org/10.1080/10298436.2026.2723365
Primary Topic
Asphalt Pavement Performance Evaluation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mechanistic prediction of rutting in LSAM-50 flexible base pavements: a novel master curve model incorporating temperature and load effects

Yingjun Jiang, Jiangtao Fan, Yong Yi, Yun Chen et al.
International Journal of Pavement Engineering
Asphalt Pavement Performance Evaluation
article

Mechanistic prediction of rutting in LSAM-50 flexible base pavements: a novel master curve model incorporating temperature and load effects

Yingjun Jiang, Jiangtao Fan, Yong Yi, Yun Chen, Tian Tian, Yu Zhang, Zhanchuang Han
article en

Abstract

Permanent deformation (rutting) threatens the structural integrity and service life of asphalt pavements. This study investigated the rutting resistance mechanisms of flexible base pavements incorporating LSAM-50, an ultra-large stone asphalt mixture (nominal maximum aggregate size 53 mm), and developed predictive rutting models. Effects of aggregate gradation, binder type, and temperature were analyzed via shear strength theory, using cohesion c and the internal friction angle φ. Compared with conventional mixtures, LSAM-50 exhibited an approximately 10° higher φ and stronger aggregate interlock, and dynamic stability over 2.7 times that of finer-graded materials. A statistical regression model (SR-DS) with correlation coefficient above 0.95 was established. A rutting-depth master curve method was developed based on time–temperature equivalence, using shift factors to consolidate rutting data onto a reference condition, achieving R 2 > 0.92 and MSE < 0.01. Field measurements confirmed that LSAM-50 flexible base pavements exhibit deflection comparable to semi-rigid base pavements at room temperature, with fewer layers, faster construction, and over 30% material savings. With asphalt layer thicknesses of 42–54 cm, the LSAM-50 pavement system is consistent with perpetual pavement design principles. This study provides a mechanistic understanding and predictive tools for designing durable and rutting-resistant asphalt pavement structures.

International Journal of Pavement EngineeringVol. 27(1)
National University of Singapore (SG), Chang'an University (CN), Yangtze Normal University (CN), CCCC Highway Consultants (China) (CN), Xi'an Aeronautical University (CN), Ministry of Education (BD), Xinjiang University (CN)
Openalex Percentile: Top 15%
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.