PDF(Pubmed)
Abstract：
Self-healing cementitious materials containing microcapsules filled with healing agents can autonomously seal cracks and restore structural integrity. However, optimising the microcapsule mechanical properties to survive concrete mixing whilst still rupturing at the cracked interface to release the healing agent remains challenging. This study develops an integrated numerical modelling and machine learning approach for tailoring acrylate-based microcapsules for triggering within cementitious matrices. Microfluidics is first utilised to produce microcapsules with systematically varied shell thickness, strength, and cement compatibility. The capsules are characterised and simulated using a continuum damage mechanics model that is able to simulate cracking. A parametric study investigates the key microcapsule and interfacial properties governing shell rupture versus matrix failure. The simulation results are used to train an artificial neural network to rapidly predict the triggering behaviour based on capsule properties. The machine learning model produces design curves relating the microcapsule strength, toughness, and interfacial bond to its propensity for fracture. By combining advanced simulations and data science, the framework connects tailored microcapsule properties to their intended performance in complex cementitious environments for more robust self-healing concrete systems.
摘要：
含有填充有愈合剂的微胶囊的自愈合胶凝材料可以自主地密封裂缝并恢复结构完整性。然而,优化微胶囊的机械性能以在混凝土混合中存活，同时在破裂的界面处仍破裂以释放愈合剂仍然具有挑战性。这项研究开发了一种集成的数值建模和机器学习方法，用于定制基于丙烯酸酯的微胶囊，以在胶结基质中触发。微流体首先用于生产具有系统变化的壳厚度的微胶囊，力量,和水泥相容性。使用能够模拟开裂的连续损伤力学模型对胶囊进行表征和模拟。参数研究调查了决定壳破裂与基质破坏的关键微胶囊和界面特性。仿真结果用于训练人工神经网络，以根据胶囊特性快速预测触发行为。机器学习模型产生与微胶囊强度相关的设计曲线，韧性,和界面结合其断裂倾向。通过结合先进的模拟和数据科学，该框架将定制的微胶囊特性与其在复杂胶结环境中的预期性能联系起来，以实现更坚固的自修复混凝土系统。
