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Discussion on erosion and accumulation behaviours in the process of soil-rock flow migration with deep learning experimental analysis method and numerical simulation

泥石流 腐蚀 沉积作用 流量(数学) 地质学 岩土工程 碎片 内腐蚀 过程(计算) 计算机科学 机械 地貌学 沉积物 物理 海洋学 操作系统
作者
Shih-Hao Chou
出处
期刊:Impact [Science Impact]
卷期号:2022 (2): 9-11
标识
DOI:10.21820/23987073.2022.2.9
摘要

The frequency of debris flows occurring has increased in Taiwan and mitigation strategies are important to protect property and save lives. Dr Shih-Hao Chou is a research scholar based in the Department of Mechanical Engineering, National Central University, Taiwan, is exploring how AI and deep learning can be applied to the mitigation of debris flow. He is investigating the physical mechanisms of debris flow, as well as migration behaviour and the potential scale of future disasters, which includes analysing flow behaviour and comparing it with an occurrence model. In their work, Chou and his collaborators are using a deep learning experimental analysis method to observe the current situation of debris flow in real time, and further predict the downstream debris flow behaviour. The researchers are also utilising numerical simulation in order to observe the internal movement behaviour in the earth-rock flow field and the damage caused to engineering facilities. Chou is conducting this research in collaboration with Professor Hsiau Shusan from the Department of Mechanical Engineering, National Central University. Chou is also looking at erosion transport or sedimentation behaviour in the process of collapse and flow, responding to a knowledge gap in this area. This involves studying the erosion and sedimentation behaviour of an artificial dam-break particle flow field on the bottom bed and, following the dam break, studying the particles in the collapse process using image and particle tracking technology and numerical simulation technology, looking at avalanche speed, avalanche time, erosion and sedimentation phenomena.

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