首页|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 . ..)

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 . ..)

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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.”

HangzhouPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningPerceptronDepartment of Civil Engineering

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(MAY.2)