摘要
不均匀受力容易导致复合钢结构中的局部应力集中、变形不均等问题,进而导致结构的失稳和损坏。为了保障复合钢结构的安全使用,提出复合钢结构不均匀受力下形变弹性预测仿真。结合熵增原理与热力第一、第二定律,通过分析不均匀受力状态下的复合钢结构内部能量分布情况,明确复合钢结构弹性形变分布情况。基于复合钢结构弹性形变过程中的力学分析,对弹性形变发生过程中的刚性振动现象展开分析,并利用广义力对因弹性形变造成的复合钢振动形态事实描述,获取其弹性形变动力学参数。将获取的参数输入反向传播(Back Propagation, BP)神经网络并对其训练,使BP神经网络具备复合钢结构形变弹性预测能力。通过反向权值修正,优化BP神经网络的预测精度,实现复合钢结构不均匀受力下形变弹性的精准预测。实验证明所提方法预测效果精准,且形变弹性分布预测结果与实际基本一致,为复合钢结构的广泛应用提供重要保障。
Typically,uneven force application can easily lead to localized stress concentration and uneven deformation in composite steel structures,even structural instability and damage.To ensure the safe use of composite steel structures,this article put forward a simulation for predicting elastic deformation under uneven force application in composite steel structures.Firstly,according to the entropy increase principle as well as the first and second laws of thermodynamics,we analyzed the internal energy distribution of composite steel structures under uneven force application,thereby clarifying the distribution of elastic deformation in composite steel structures.Based on the mechanical analysis in the process of elastic deformation,we analyzed the phenomenon of rigid vibration during the elastic deformation,and then used the generalized force to describe the vibration mode of the composite steel caused by elastic deformation,thus obtaining its dynamic parameters of elastic deformation.After that,we input these parameters into a Back Propagation(BP)neural network and trained them to enable the BP neural network to predict the elastic deformation of composite steel structures.Finally,through reverse weight correction,we optimized the prediction accuracy of the BP neural network,thus achieving an accurate prediction,Experiment results prove that the proposed method has accurate prediction results,and the predicted distribution of elastic deformation is consistent with the actual situation.This method provides an important guarantee for the widespread application of composite steel structures.
作者
吕晓丹
高山凤
LV Xiao-dan;GAO Shan-feng(School of Intelligent Engineering,Jinzhong College of Information,Jinzhong Shanxi 030800,China;School of Automation and Soffwaye Engineering,Shanxi University,Taiyuan Shanxi 030006,China)
出处
《计算机仿真》
2024年第8期466-470,共5页
Computer Simulation
基金
山西省高等学校教学改革创新项目(J20221450)
山西省高等学校教学改革创新项目(J20221448)。
关键词
熵增原理
热力第一定律
热力第二定律
刚性振动
神经网络
Principle of entropy increase
The first law of thermodynamics
The second law of thermodynamics
Rigid vibration
BP neural network
