Study Results from University of Transport Technology Broaden Understanding of Machine Learning (Predicting Axial Compression Capacity of Cfdst Columns and Design Optimization Using Advanced Machine Learning Techniques)

Robotics & Machine Learning Daily News2024，Issue(Feb.12) ：44-45.DOI:10.1016/j.istruc.2023.105724

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NETL
NSTL
Elsevier

Study Results from University of Transport Technology Broaden Understanding of Machine Learning (Predicting Axial Compression Capacity of Cfdst Columns and Design Optimization Using Advanced Machine Learning Techniques)

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Abstract

Research findings on Machine Learning are discussed in a new report. According to news originating from Hanoi, Vietnam, by NewsRx correspondents, research stated, "Concrete-filled double-skin steel tubular (CFDST) columns are fundamental in civil engineering, known for their exceptional mechanical properties. Their load-bearing capacity, influenced by factors such as geometry, material properties, and loading conditions, is a critical aspect of CFDST column design."

Key words

Hanoi/Vietnam/Asia/Cyborgs/Emerging Technologies/Machine Learning/University of Transport Technology

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出版年

2024

Robotics & Machine Learning Daily News

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