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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
典雅采珊发布了新的文献求助10
1秒前
cdercder应助smh采纳,获得10
2秒前
拾叁发布了新的文献求助10
2秒前
YSL应助举个栗子8采纳,获得10
3秒前
研友_VZG7GZ应助shouyu29采纳,获得10
4秒前
4秒前
4秒前
酷爱小飞完成签到,获得积分10
5秒前
科研通AI6.3应助乐观冥幽采纳,获得10
5秒前
5秒前
6秒前
z!完成签到 ,获得积分10
6秒前
7秒前
Ashmitte完成签到 ,获得积分10
7秒前
纯真的梦竹完成签到,获得积分10
7秒前
闾阎grit完成签到,获得积分10
7秒前
Orange应助科研通管家采纳,获得10
7秒前
汉堡包应助科研通管家采纳,获得10
7秒前
脑洞疼应助科研通管家采纳,获得10
7秒前
8秒前
8秒前
共享精神应助科研通管家采纳,获得10
8秒前
Owen应助科研通管家采纳,获得10
8秒前
kukudeyu发布了新的文献求助10
8秒前
酷波er应助科研通管家采纳,获得10
8秒前
科研通AI2S应助科研通管家采纳,获得10
8秒前
大个应助典雅采珊采纳,获得10
8秒前
cdercder应助科研通管家采纳,获得10
8秒前
Childwild应助科研通管家采纳,获得10
8秒前
8秒前
8秒前
隐形曼青应助科研通管家采纳,获得10
8秒前
Rikki0326应助科研通管家采纳,获得10
8秒前
ale应助科研通管家采纳,获得10
9秒前
小马甲应助科研通管家采纳,获得10
9秒前
9秒前
9秒前
飞天土豆完成签到,获得积分10
9秒前
CodeCraft应助科研通管家采纳,获得10
9秒前
爱冒险的梦完成签到,获得积分10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Markov Chain Monte Carlo 5000
Evidence Summary. Injection (subcutaneous):op- timal administration 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
Curating Socialism: A Handbook of International Art Exhibitions 1947-1989 530
Lengua e imagen en la comunicación digital 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7484641
求助须知:如何正确求助?哪些是违规求助? 9077106
关于积分的说明 19356978
捐赠科研通 7099483
什么是DOI,文献DOI怎么找? 3248185
关于科研通互助平台的介绍 2417415
邀请新用户注册赠送积分活动 2233549