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Research on Data Driven Model for the Ultimate Load Prediction of Thin-Walled Steel Perforated Sections and Its Application

Zhi-Jun Lyu, Xi Huang

Abstract



The rack columns have so distinctive characteristics in their design, which have regular perforations to facilitate installation of the rack system that it is more difficult to be analyzed with traditional cold-formed steel structures design standards or theory. The emergence of industrial “big-data” has been creating more better innovative thinking for those working in various fields including science, engineering and business. In this paper, a novel data driven model (DDM) using artificial neural network technology is presented with finite element data and physical test data for intelligent forecasting of an ultimate load capacity of thin-walled steel specific perforated sections. The data driven model based on machine learning is able to provide a more effective help for decision-making of innovative design in steel members. Compared with the traditional finite element model and physical test, the low cost and high efficient data driven model for the solving the hard problem of complicated steel perforated sections design seems to be very promising.

Keywords


Data driven, artificial neural network, finite element method, perforated sections, thin-walled steel.

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