Abstract
This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The finite element method (FEM) was employed to increase the available datasets and evaluate the proposed reinforcement method in different geometrical types of sections. The most influential factors on the axial strength were investigated using a feature-selection (FS) method within a multi-layer perceptron (MLP) algorithm. The MLP algorithm was developed by particle swarm optimization (PSO) and FEM results as input. In terms of accuracy evaluation, some of the rolling criteria including results showed that geometrical parameters have almost the same contribution in compression capacity and displacement of the specimens. According to the performance evaluation indexes, the best model was detected and specified in the paper and optimised by tuning other parameters of the algorithm. As a result, the normalised ultimate load and displacement were predicted successfully.
Highlights
Warehousing systems are widely used to manage industrial production
(combination of multi-layer perceptron (MLP), particle swarm optimization (PSO), and feature-selection techniques), a neural network dataset was derived and formed from 10511 rows of data and six-column of values. This prediction consists of six inputs and one target output
In order to select the most suitable combination of the inputs for the evaluation matrix of the displacement and load, the MLP was tuned by PSO and carried out as the neural network model
Summary
Warehousing systems are widely used to manage industrial production. Since coldformed steel (CFS) sections have been developed in racking systems, steel storage systems are extensively employed in various industries [1,2,3,4]. The stability of the racking systems directly depends on uprights where a combination of different failure modes is probable under service loads [5,6,7,8,9,10]. Since racking systems typically experience extreme loading scenarios, the design of the uprights has become a vital task [11,12,13,14,15,16]. The structural performance of the upright raking systems has been widely studied under different axial load scenarios. According to their study, limited tests are promising for designing racking uprights. According to Trouncer and Rasmussen’s study [19], the prediction of the capacity of uprights is more decisive by EN 15512 [20] specifications compared to Rack Manufacturers Institute (RMI). Gilbert and Rasmussen [21] performed general tests on racking systems to enhance EN 15512 specifications and presented some clarifications to determine in-plane stiffness
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