基于材料信息学的沥青混合料性能提升综述及展望  

Review and Prospects of Performance Enhancement of Asphalt Mixtures Based on Material Informatics

作　　者：刘志杨 董泽蛟[2] 周涛 单丽岩[2] 马宪永 LIU Zhi-yang;DONG Ze-jiao;ZHOU Tao;SHAN Li-yan;MA Xian-yong(School of Civil Engineering and Transportation,Northeast Forestry University,Harbin 150001,Heilongjiang,China;School of Transportation Science and Engineering,Harbin Institute of Technology,Harbin 150090,Heilongjiang,China)

机构地区：[1]东北林业大学土木与交通学院,黑龙江哈尔滨150001 [2]哈尔滨工业大学交通科学与工程学院,黑龙江哈尔滨150090

出　　处：《中国公路学报》2024年第4期98-120,共23页China Journal of Highway and Transport

基　　金：国家重点研发计划项目(2022YFB2602600);国家自然科学基金区域联合基金项目(U20A20315);国家自然科学基金项目(52108410,52208432,52278448);中国博士后科学基金项目(2022T150432,2021M702260);黑龙江省自然科学基金项目(YQ2023E024)。

摘　　要：材料信息学是推进材料基因组计划的理论核心,为组成多相、结构随机、行为复杂、具有显著多尺度特征的沥青混合料性能提升与新材料研发提供了重要手段。为推动材料信息学在沥青混合料相关研究中的应用,综述了材料信息学理论在沥青及沥青混合料性能预测与耐久性提升方面的应用研究。首先,总结了材料基因组及材料信息学理论基本内涵及其在沥青路面材料领域的应用;然后,介绍了常见的材料数据标准,总结了沥青及沥青混合料材料多尺度特征与材料特征基因数据库发展;进而,介绍了基于化学组分、胶体结构基因特性的沥青性能预测及改性沥青组成优化研究,总结了数据挖掘及机器学习算法在沥青混合料力学及路用性能预测方面的应用,主要包括混合料设计指标、动态模量、高温抗车辙能力、抗疲劳性能、低温抗裂性及水稳定性预测模型,分析了基于性能预测及智能寻优方法的沥青混合料组成结构优化,从而实现混合料路用性能的定向提升;最后,讨论了沥青混合料材料信息学应用架构,分析了沥青混合料材料基因特性体系与基于机器学习的混合料性能预测研究可能面临的挑战,展望了未来沥青混合料材料信息学研究可能需要解决的问题。该综述对沥青路面材料耐久性提升具有推动作用。Material informatics are the theoretical core of the Materials Genome Initiative, which provides critical methods for performance improvement and new material development of asphalt mixtures composed of multiphase, random structures, and complex behaviors at multiple scales. This paper reviews the application of material informatics in the performance prediction and durability enhancement of bitumen and asphalt mixtures to promote the research and application of material informatics. First, the essential connotations of the material genome and material informatics are analyzed, and their applications in asphaltic materials are summarized. Common material data standards are then summarized. The development of multiscale characteristics and material gene databases for bitumen and asphalt mixtures is reviewed. Furthermore, research on asphalt property prediction and modified asphalt composition optimization based on the chemical composition and colloidal structure genes is introduced. The application of data mining and machine learning algorithms for predicting the mechanical and service performances of asphalt mixtures is outlined, including the mixture design indicators, dynamic modulus, high-temperature rutting resistance, fatigue resistance, low-temperature cracking resistance, and water stability. The composition and structure optimization of asphalt mixtures based on performance prediction and intelligent optimization methods are analyzed to improve the performance of the asphalt mixture. Finally, the framework of the informatics for asphalt mixture materials is discussed. The potential challenges in the asphalt mixture gene system and performance prediction using machine learning are analyzed. Potential problems for future material informatics research are also discussed. This review could provide promotion to the durability improvement of asphalt pavement materials.

关 键 词：路面工程 沥青混合料 综述 材料信息学 性能提升 机器学习 材料基因组 

分 类 号：U416.217[交通运输工程—道路与铁道工程]