Abstract

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.

Highlights

  • With the rapid advancement of e-commerce, automated storage and retrieval systems (AS/RS) have been so widely applied in China that high-rise steel storage pallet racks (SPR) have exhibited an explosive growth in production and logistics system (Figure 1)

  • Taking the riveted beam-to-column connections (BCCs) as our research object, we present a novel data-driven model, using an integrated experimentalFEM-support vector machine (SVM) methodology to overcome many difficulties associated with the mechanical performance of semirigid beam-to-upright joint modeling, which is the main contribution of this paper. e objective of data-driven based predictive models is the development of enabling tools for designers to make rapid and effective decision when big datasets are available on prediction and reasonable number of predictors

  • It can be seen that both SVM model and beam position (BP) model are close to the test value, with mean absolute error (AE) of over 3% and correlation coefficient R close to 1

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Summary

Introduction

With the rapid advancement of e-commerce, automated storage and retrieval systems (AS/RS) have been so widely applied in China that high-rise steel storage pallet racks (SPR) have exhibited an explosive growth in production and logistics system (Figure 1). Acting as one of the most important infrastructures for AS/RS, structural design for SPR needs the elaborate decision-making between structural systems and a variety of cold-formed steel members in such a way that the stability and safety behave as intended by the designer and satisfies the constraints imposed by capital investment, environment, and so on. Of all the members in the SPR, the beam-to-column connections (BCCs) constitute the most critical part of the assembly which largely determines the overall stability of SPR in the down-aisle direction [1].

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