A Comparative Study on the Performance of FEM, RA and ANN Methods in Strength Prediction of Pallet-Rack Stub Columns
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International Journal of Steel Structures https://doi.org/10.1007/s13296-020-00386-6
A Comparative Study on the Performance of FEM, RA and ANN Methods in Strength Prediction of Pallet‑Rack Stub Columns ZhiJun Lyu1,2 · Jie Zhang1,2 · Ning Zhao1,2 · Qian Xiang1,2 · YiMing Song3 · Jie Li1 Received: 22 April 2019 / Accepted: 24 July 2020 © Korean Society of Steel Construction 2020
Abstract The rack column is one of the essential elements in the pallet rack system. However, due to its distinctive perforation feature, it is challenging to analyze its stability using traditional theories for cold-formed steel structures. In this paper, we are interested in the comparison analysis of strength prediction on the perforated columns using finite element method (FEM), regression analysis (RA) and artificial neural network (ANN) methods respectively. First, a refined finite element (FE) model considering the perforation and nonlinearity behavior was generated and calibrated against the experimental results. Subsequently, the validated FE model was used to perform the parametric analysis for the different holes in columns. Given experimental and simulated data, a regression model with an equivalent thickness was proposed for the design strength prediction of thin-walled steel perforated sections. For comparison of the RA model, two powerful tools such as the FEM and ANN are also employed to predict the design strength of different perforated sections. Four indicators were used to assess the accuracy and generalization performance of the three models, including the root mean square error, the mean absolute percentage error, the correlation coefficient and the mean relative percentage. The obtained results show that although they both have good consistency, FEM still slightly outperforms the other two models. Since the values calculated from ANN and regression models are usually smaller than the experimental data, they are reasonably recommended as effective and safer design tools than FEM models from the perspective of engineering applications. Keywords Strength prediction · Regression analysis (RA) · Finite element method (FEM) · Artificial neural network (ANN) · Thin-walled steel perforated sections
1 Introduction The rapid advancements in the E-commerce fields have been promoting the wide application of automated storage and retrieval system (AS/RS) in China. As one of the critical elements for AS/RS, pallet racks play a significant role whose structural design needs the consideration of reliability, cost and environmental requirements. The thin-wall steel has a high strength to weight ratio and ease of fabrication and assembly. Therefore, it has been widely used in industrial * ZhiJun Lyu [email protected] 1
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
2
Shanghai Engineering Research Centre of Storage and Logistics Equipment, Shanghai 201611, China
3
Shanghai Motor Vehicle Testing Center Technology Co. Ltd, Shanghai 201805, China
storage racks (Shah et al. 2016). Cold forming
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