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
王小波完成签到 ,获得积分10
1秒前
研友_VZG7GZ应助程锦采纳,获得10
1秒前
wanci应助科研通管家采纳,获得10
2秒前
顺心人达发布了新的文献求助10
2秒前
2秒前
lixinglei应助科研通管家采纳,获得20
3秒前
搜集达人应助科研通管家采纳,获得10
3秒前
CipherSage应助科研通管家采纳,获得30
3秒前
科目三应助科研通管家采纳,获得10
3秒前
bkagyin应助科研通管家采纳,获得10
3秒前
慕青应助科研通管家采纳,获得10
3秒前
思源应助科研通管家采纳,获得10
4秒前
斯文败类应助科研通管家采纳,获得10
4秒前
烦死了啦完成签到,获得积分10
4秒前
4秒前
4秒前
Hiki完成签到,获得积分10
4秒前
4秒前
科研通AI2S应助科研通管家采纳,获得10
4秒前
TENG完成签到,获得积分20
4秒前
研友_VZG7GZ应助黄海采纳,获得10
4秒前
5秒前
香蕉觅云应助科研通管家采纳,获得10
5秒前
pluto应助科研通管家采纳,获得10
5秒前
zyd1201发布了新的文献求助10
6秒前
6秒前
zhu完成签到,获得积分10
6秒前
dp完成签到,获得积分10
7秒前
DrLiu完成签到,获得积分10
8秒前
北欧海盗发布了新的文献求助10
9秒前
lemon发布了新的文献求助10
9秒前
起落发布了新的文献求助10
9秒前
10秒前
10秒前
tangyunfeng关注了科研通微信公众号
11秒前
12秒前
12秒前
12秒前
13秒前
击水三千里完成签到,获得积分10
14秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
Pediatric Dermoscopy Trichoscopy & Onychoscopy 2030
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
Handbuch Trainingswissenschaft – Trainingslehre 500
Additive Manufacturing Design and Applications (ASM Handbook, Volume 24A) 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 7577418
求助须知:如何正确求助?哪些是违规求助? 9157111
关于积分的说明 19590484
捐赠科研通 7161335
什么是DOI,文献DOI怎么找? 3265338
关于科研通互助平台的介绍 2430294
邀请新用户注册赠送积分活动 2255998