Viscoelastoplastic Constitutive Model for the AC Lower Surface Layer Considering Time-Varying Characteristics during the Intelligent Compaction Process

Intelligent compaction (IC) technology based on the continuous compaction control (CCC) technology enables real-time monitoring, evaluation, and feedback of compaction quality. However, the lack of a viscoelastoplastic constitutive model of hot asphalt mixture under triaxial stress states, as well as the difficulty in simulating the time-varying characteristics of material properties, hinders the research and application of IC in asphalt concrete (AC) layers. Therefore, this paper proposes a viscoelastoplastic constitutive model for AC20 asphalt mixture and establishes a vibrating compaction numerical model for time-varying characteristics in material properties using finite-element modeling (FEM) analysis software Abaqus. Additionally, the stress state of the pavement was analyzed, and adjustment methods for vibrating compaction technology are proposed to further improve the compaction quality of the AC lower surface layer. Subsequently, the intelligent compaction measurement values (ICMVs) were calculated according to the acceleration and displacement response of the roller, and the reasons for the change in ICMVs were determined. Field test results verified that the established numerical model can realistically simulate the mechanical behavior of asphalt pavement under actual vibrating compaction conditions, thus providing a theoretical basis for the application of IC feedback control technology in AC lower surface layer.

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Publication Details

Journal
Journal of Materials in Civil Engineering
Published
2024-11-26
DOI
https://doi.org/10.1061/jmcee7.mteng-18674
Citations
1
Primary Topic
Non-Destructive Testing Techniques
Type
article
Field-Weighted Citation Impact
0.21
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article

Viscoelastoplastic Constitutive Model for the AC Lower Surface Layer Considering Time-Varying Characteristics during the Intelligent Compaction Process

Zhou Fang, Tao Ma, Yu Zhu, Louay N. Mohammad et al.
1 citations
Journal of Materials in Civil Engineering
Non-Destructive Testing Techniques
0.21
article

Viscoelastoplastic Constitutive Model for the AC Lower Surface Layer Considering Time-Varying Characteristics during the Intelligent Compaction Process

Zhou Fang, Tao Ma, Yu Zhu, Louay N. Mohammad, Feng Chen, Heng Liu, Weiguang Zhang
article en
1 citations

Abstract

Intelligent compaction (IC) technology based on the continuous compaction control (CCC) technology enables real-time monitoring, evaluation, and feedback of compaction quality. However, the lack of a viscoelastoplastic constitutive model of hot asphalt mixture under triaxial stress states, as well as the difficulty in simulating the time-varying characteristics of material properties, hinders the research and application of IC in asphalt concrete (AC) layers. Therefore, this paper proposes a viscoelastoplastic constitutive model for AC20 asphalt mixture and establishes a vibrating compaction numerical model for time-varying characteristics in material properties using finite-element modeling (FEM) analysis software Abaqus. Additionally, the stress state of the pavement was analyzed, and adjustment methods for vibrating compaction technology are proposed to further improve the compaction quality of the AC lower surface layer. Subsequently, the intelligent compaction measurement values (ICMVs) were calculated according to the acceleration and displacement response of the roller, and the reasons for the change in ICMVs were determined. Field test results verified that the established numerical model can realistically simulate the mechanical behavior of asphalt pavement under actual vibrating compaction conditions, thus providing a theoretical basis for the application of IC feedback control technology in AC lower surface layer.

Journal of Materials in Civil EngineeringVol. 37(1)
Louisiana State University (US), Open University of China (CN), Shanghai Open University (CN), Southeast University (CN)
Climate action
Openalex Percentile: Top 49%
Non-Destructive Testing Techniques
0.21
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