Researchers from Department of Civil Engineering Provide Details of New Studies and Findings in the Area of Machine Learning (Predictions of Failure Mode and Ar resting Efficiency of Integral Buckle Arrestors Using Fem and Machine Learning . ..)

Robotics & Machine Learning Daily News2024，Issue(MAY.2) ：117-118.

来源：

NETL
NSTL

Researchers from Department of Civil Engineering Provide Details of New Studies and Findings in the Area of Machine Learning (Predictions of Failure Mode and Ar resting Efficiency of Integral Buckle Arrestors Using Fem and Machine Learning . ..)

扫码查看

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Fresh data on Machine Learning are pre sented in a new report. According to newsoriginating from Hangzhou, People’s Re public of China, by NewsRx correspondents, research stated,“Integral buckle arr estors are regarded as the most effective arresting devices, which can provide a nobstruction to a propagating buckle thereby protecting downstream pipelines. I n the present study,numerical frameworks were established to reproduce the phen omenon of buckling propagating and crossingunder hydrostatic pressure, and a st rong consistency between measurements and predictions was achieved.”

Key words

Hangzhou/People’s Republic of China/As ia/Cyborgs/Emerging Technologies/Machine Learning/Perceptron/Department of Civil Engineering

引用本文复制引用

出版年

2024

Robotics & Machine Learning Daily News

ISSN：

段落导航