目的 针对传统有限元方法在工业货架梁柱节点性能评估中存在的建模过程繁琐、计算效率低下等难以工程化应用的问题,提出一种兼具计算精度与效率的混合预测方法。方法 首先基于梁柱节点的真实受力特性构建有限元模型,并通过正交试验设计系统探究结构参数对节点刚度与失效力矩的影响规律,形成仿真数据库。在此基础上,进一步引入卷积神经网络算法,构建梁柱节点性能预测模型。结果 研究发现,铆钉数量和横梁高度分别是影响节点刚度和失效力矩的主导因素。所建立的神经网络预测模型在精度上与有限元分析结果一致,而计算建模效率提升了约45倍。结论 为梁柱节点力学性能的快速、准确地评估提供了有效的工程技术手段。
Abstract
The work aims to propose a hybrid prediction method that integrates computational accuracy with efficiency to address the challenges of applying traditional finite element methods (FEM), which involves complicated modeling and low computational efficiency, in evaluating the performance of beam to upright connectors in industrial racks. Firstly, a finite element model was established according to the actual stress characteristics of beam to column connectors, and the effects of structural parameters on connector stiffness and failure moment were systematically examined through an orthogonal experimental design, thereby generating a simulation database. On this basis, a convolutional neural network was introduced to develop a performance prediction model for beam to upright connectors. The research results indicated that the connector stiffness was significantly affected by the number of rivets, whereas the failure moment was more strongly affected by the height of the beam. The established neural network prediction model was shown to be consistent with the finite element analysis results in terms of accuracy, while computational efficiency was improved by approximately 45 times. The work provides an effective engineering technique for the rapid and accurate assessment of beam to upright connector performance.
关键词
梁柱节点 /
物理模拟 /
有限元分析建模 /
卷积神经网络 /
正交试验方法
Key words
beam to upright connector /
physical simulation /
finite element method (FEM) /
convolutional neural networks (CNN) /
orthogonal experimental design
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基金
新疆建设兵团重点领域科技攻关专项（2025AB074）; 中央高校基本科研业务费项目（2232024D-24）
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