Prediction of the Bending Strength of Boltless Steel Connections in Storage Pallet Racks: An Integrated Experimental-FEM-SVM Methodology

Due to many differences in the material, geometry, and assembly method of the commercially available beam-end-connectors in steel storage pallet racks (SPR), no common numerical model has been universally accepted to accurately predict the M–θ behavior of complex semirigid connections so far. Despite the fact that the finite element method (FEM) and physical experiment have been used to obtain the mechanical performance of beam-to-column connections (BCCs), those methods have the disadvantages of high computational complexity and test cost. Taking, for example, the boltless steel connections, this paper proposes a data-driven simulation model (DDSM) that combines the experimental test, FEM, and support vector machine (SVM) techniques to determine the bending strength of BCCs by means of data mining from the engineering database. First, a three-dimensional (3D) finite element (FE) model was generated and calibrated against the experimental results. Subsequently, the validated FE model was further extended to perform parametric analysis and enrich the engineering case base of structural characterization of BCCs. Based on the M–θ curve of the FE simulation, support vector machines (SVMs) were trained to predict the flexural rigidity of beam-to-column joints. The predictive power of the SVM algorithms is estimated by comparison with traditional ANN models via the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the correlation coefficient R. The results obtained indicate that the SVM algorithms slightly outperform the ANN algorithms, although both of them are in good agreement with FEM and physical test. From the point of view of engineering application, DDM is able to provide much more effective help for structural engineers to make rapid decisions on steel members design