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• W4292476211 abstract "This work presents a deep-learning model referred to as a deep belief network (DBN) to investigate the end-two-flange (ETF) web crippling behaviour of roll-formed aluminium alloy lipped channels (both unfastened and fastened to the supports) with centred and offset web circular holes. A total of 1,080 data points was generated for training the DBN, using an elasto-plastic finite element model that was validated using 30 new experimental results presented in this paper. When the DBN predictions were compared to the experimental results, they were found to be 5% conservative on an average. A parametric analysis was then carried out using the DBN predictions, to study the effects of hole size, hole position, section thickness, and the bearing plate on web crippling strength of perforated roll-formed aluminium alloy channels. The DBN predictions were utilised to evaluate the performance of the current design rules of American Iron and Steel Institute (AISI 2016), Australian and New Zealand Standards (i.e., AS/NZS 1664, AS/NZS 4600:2018), and Eurocode (CEN 2007). The current design standards were demonstrated to be over conservative in predicting the web crippling strength of roll-formed aluminium alloy perforated channels. From the parametric analysis, new web crippling strength and web crippling strength reduction factor formulae for roll-formed aluminium alloy perforated channels were proposed. Additionally, a reliability analysis revealed that the proposed equations accurately predicted the ETF web crippling strength of roll-formed aluminium alloy perforated lipped channels." @default.
• W4292476211 created "2022-08-21" @default.
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• W4292476211 date "2022-10-01" @default.
• W4292476211 modified "2023-10-03" @default.
• W4292476211 title "Assessment of end-two-flange web crippling strength of roll-formed aluminium alloy perforated channels by experimental testing, numerical simulation, and deep learning" @default.
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• W4292476211 doi "https://doi.org/10.1016/j.engstruct.2022.114753" @default.
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