在水泥材料中的微囊触发力学:一个建模和机器学习方法.
Evan John Ricketts1, Lívia Ribeiro de Souza2, Brubeck Lee Freeman1,3
1School of Engineering, Cardiff University, 3-5 The Walk, Cardiff CF24 3AA, UK.
Materials (Basel, Switzerland)
|April 9, 2024
概括
这项研究开发了一种使用模拟和机器学习来设计更好的自我修复混凝土的新方法. 它优化微囊以有效地释放治疗剂,增强混凝土.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 化学工程是化学工程的重要组成部分.
背景情况:
- 自愈的水泥材料使用含有愈合剂的微囊自主修复裂.
- 一个关键的挑战是优化微囊的特性,以便在混凝土混合和在裂部位的受控破裂期间生存.
研究的目的:
- 开发一种集成的数值建模和机器学习方法,用于定制基于烯酸的微囊,用于自愈混凝土.
- 将定制的微囊属性与其在水泥环境中的性能相连接,以改进自愈混凝土系统.
主要方法:
- 微流体被用于生产具有不同外厚度,强度和水泥兼容性的微囊.
- 连续损伤力学模型模拟囊行为和裂.
- 机器学习 (人工神经网络) 在模拟数据上进行训练,以预测触发行为.
主要成果:
- 一项参数研究确定了控制微囊破裂与矩阵失效的关键性质.
- 机器学习模型生成了设计曲线,将微囊特性 (强度,性,界面键) 与断裂倾向相关联.
- 该框架成功地将定制的微囊属性与它们在水泥基质的表现联系起来.
结论:
- 综合方法为设计强大的自我修复混凝土系统提供了一条途径.
- 优化的微囊增强了混凝土的自主裂密封和结构完整性恢复能力.
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