论文标题
通过基于同源的机器学习的坡度故障过程的拓扑特征研究
Topological feature study of slope failure process via persistent homology-based machine learning
论文作者
论文摘要
使用软件UDEC在地震载荷下模拟斜率的不稳定性故障过程，研究斜率故障的动态响应，获得斜坡的变形特征和位移云图，然后使用持久同源性理论分析斜率的不稳定性状态，从而产生条形码图并从条形码图中提取斜率的特征。发现与斜率不稳定性的临界状态相对应的拓扑特征，并建立了拓扑特征与不稳定性演化之间的关系。最后，它为坡度故障预测提供了拓扑研究工具。结果表明，最长的Betti 1条形码的变化反映了斜率的演变过程和不稳定性故障定律。使用离散的元素方法和持续的同源理论研究外部负载下斜率的故障特征可以更好地理解斜率的故障机制，为工程保护提供理论基础，并为斜率安全设计和灾难预测研究提供了新的数学方法。
Using software UDEC to simulate the instability failure process of slope under seismic load, studing the dynamic response of slope failure, obtaining the deformation characteristics and displacement cloud map of slope, then analyzing the instability state of slope by using the theory of persistent homology, generates bar code map and extracts the topological characteristics of slope from bar code map. The topological characteristics corresponding to the critical state of slope instability are found, and the relationship between topological characteristics and instability evolution is established. Finally, it provides a topological research tool for slope failure prediction. The results show that the change of the longest Betti 1 bar code reflects the evolution process of the slope and the law of instability failure. Using discrete element method and persistent homology theory to study the failure characteristics of slope under external load can better understand the failure mechanism of slope, provide theoretical basis for engineering protection, and also provide a new mathematical method for slope safety design and disaster prediction research.
