Coarse‐grained molecular dynamics reveals rejuvenation behavior in aged asphalt

IF 9.1 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-07-22 DOI:10.1111/mice.70017

Liang He, Haitao Huang, Wentao Zhang, Changjiang Dai, Guannan Li, Alessio Alexiadis, Mengzhe Tao, Wim Van den bergh, Karol J. Kowalski

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引用次数: 0

Abstract

To address the issue of insufficient spatiotemporal scales in all‐atom molecular dynamics (MD) simulations, this study employs coarse‐grained MD (CGMD) based on the MARTINI 3.0 force field to simulate the rejuvenation behavior of asphalt. Based on all‐atom molecular structure coarse‐grained mapping, molecular models of both virgin and aged asphalt are constructed, with iterative optimization of parameters. Coarse‐grained models of asphalt were constructed and validated through density, glass transition temperature, and visualization analysis. Rejuvenation diffusion model is constructed, penetration tube tests are designed to analyze the rejuvenation behavior, and further verified through rheological performance and microstructural analysis. The results show that the rejuvenation efficiency depends on temperature, dosage, and structure of the rejuvenator molecule. Aromatic oil shows stronger interaction, while peanut oil demonstrates higher diffusivity. This study explored the application of MARTINI 3.0 force field in CGMD, providing guidance for the further application of coarse‐grained methods in asphalt behavior analysis.

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粗粒分子动力学揭示了老化沥青的再生行为

为了解决全原子分子动力学（MD）模拟中时空尺度不足的问题，本研究采用基于MARTINI 3.0力场的粗粒度分子动力学（CGMD）来模拟沥青的再生行为。基于全原子分子结构粗粒度映射，构建了初沥青和老化沥青的分子模型，并对参数进行了迭代优化。我们构建了粗粒度沥青模型，并通过密度、玻璃化转变温度和可视化分析进行了验证。建立再生扩散模型，设计侵彻管试验分析再生行为，并通过流变性能和微观结构分析进一步验证。结果表明，返老还童的效果取决于温度、用量和返老还童分子的结构。芳香油表现出较强的相互作用，而花生油表现出较高的扩散性。本研究探索了MARTINI 3.0力场在CGMD中的应用，为粗粒度方法在沥青行为分析中的进一步应用提供了指导。

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来源期刊

Computer-Aided Civil and Infrastructure Engineering 工程技术-工程：土木

CiteScore

17.60

自引率

19.80%

发文量

146

审稿时长

1 months

期刊介绍： Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.