首页|Southern University of Science and Technology (SUSTech) Researchers Detail Resea rch in Machine Learning (Integrated behavioural analysis of FRP-confined circula r columns using FEM and machine learning)
Southern University of Science and Technology (SUSTech) Researchers Detail Resea rch in Machine Learning (Integrated behavioural analysis of FRP-confined circula r columns using FEM and machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting from Shenzhen, People's Rep ublic of China, by NewsRx journalists, research stated, "This study investigates the structural behaviour of double-skin columns, introducing novel double-skin double filled tubular (DSDFT) columns, which utilise double steel tubes and conc rete to enhance the load-carrying capacity and ductility beyond conventional dou ble-skin hollow tubular (DSHT) columns, employing a combination of finite elemen t model (FEM) and machine learning (ML) techniques. A total of 48 columns (DSHT+ DSDFT) were created to examine the impact of various parameters, such as double steel tube configurations, thickness of fibre-reinforced polymer (FRP) layer, ty pe of FRP material, and steel tube diameter, on the load-carrying capacity and d uctility of the columns." Our news journalists obtained a quote from the research from Southern University of Science and Technology (SUSTech): "The results were validated against the ex perimental findings to ensure their accuracy. Key findings highlight the advanta ges of the DSDFT configuration. Compared to the DSHT columns, the DSDFT columns exhibited remarkable 19.54 % to 101.21 % increases i n the load-carrying capacity, demonstrating improved ductility and load-bearing capabilities. Thicker FRP layers enhanced the load-carrying capacity up to 15 % , however at the expense of the reduced axial strain. It was also observed that glass FRP wrapping displayed 25 % superior ultimate axial strain t han aramid FRP wrapping. Four different ML models were assessed to predict the a xial load-carrying capacity of the columns, with long short-term memory (LSTM) a nd bidirectional LSTM models emerging as superior choices indicating exceptional predictive capabilities. This interdisciplinary approach offers valuable insigh ts into designing and optimising confined column systems."
Southern University of Science and Techn ology (SUSTech)ShenzhenPeople's Republic of ChinaAsiaCyborgsEmerging T echnologiesMachine Learning
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