面向索桁架结构的温度响应分析及因素耦合研究

Research on Temperature Response Analysis and Factor Coupling for Cable Truss Structure

摘要

当前针对温度作用下索桁架结构力学响应和影响因素耦合分析的研究较为缺乏．如何实现连续动态分析温度作用对结构力学性能的影响并给出合理的补偿措施,成为提高大型索结构服役期工作性能的关键．本文以索桁架结构为研究对象,提出了温度响应分析方法,并研究多影响因素的耦合关系．首先,根据索桁架试验模型设计了温度作用工况和力学响应的采集机制．以索力实测值作为评估有限元仿真模型精度的指标．通过修正结构设计参数建立了高保真的有限元仿真模型,获取各类温度作用下的力学响应．在仿真分析的基础上建立可靠的数据集,提出了基于卷积神经网络的温度响应预测方法．在神经网络中建立温度作用与力学响应之间的数据映射关系,实现连续动态分析．基于神经网络得到了最不利的温度作用位置和具体工况．其次,为了进一步给出温度作用下的补偿措施,进行了随机森林驱动的影响因素的耦合分析．在此基础上进行了最关键因素的参数分析,得到具体的响应规律．研究结果表明,所建立的有限元仿真模型计算误差控制在3%以内,可以精准有效地分析温度作用下的力学响应．基于卷积神经网络的温度响应预测方法实现了结构力学响应的连续动态分析,分析时间成本降低了27.8%．针对本试验模型,最不利的温度作用工况为连续8跨升温37 ℃．适当提高预应力水平是改善温度作用下结构工作性能的最佳补偿措施．

Abstract

At present, there is a lack of researches on the coupling analysis of mechanical response and influencing factors of cable truss structures under the temperature effect. How to realize the continuous dynamic analysis of temperature effect on the mechanical properties of structures and give reasonable compensation measures has become a key to improving the working performance of large cable structures during their service period. In this paper, a cable truss structure is taken as the research object, its temperature response analysis method is proposed, and the coupling relationship of multiple influencing factors is studied. First, the acquisition mechanism of temperature effect condition and mechanical response is designed according to a test model of the cable truss. The measured value of cable force is used as an index to evaluate the accuracy of a finite element simulation model. By modifying the structural design parameters, a high-fidelity finite element simulation model is built to obtain the mechanical response at various temperatures. Based on the simulation analysis, a reliable data set is established, and a temperature response prediction method based on convolutional neural network is proposed. The data mapping relationship between the temperature effect and mechanical response is established in the neural network to realize the continuous dynamic analysis. Based on the neural network, the most unfavorable temperature effect position and specific working conditions are obtained. Second, to further give the compensation measures under the temperature effect, the coupling analysis of influencing factors driven by random forest is carried out. On this basis, the parameter analysis of key factors is performed, and the specific response law is formulated. Results show that the calculation error of the established finite element simulation model is controlled within 3%, so that this model can accurately and effectively analyze the mechanical response under the temperature effect. The temperature response prediction method based on convolutional neural network realizes the continuous dynamic analysis of structural mechanical response, and the cost of analysis time is reduced by 27.8%. For this test model, the most unfavorable temperature effect condition is a continuous eight-span temperature rise of 37 ℃. Appropriately increasing the prestress level is the best compensation measure to improve the working performance of the structure under the temperature effect.

关键词

索桁架结构 / 温度作用 / 力学响应 / 影响因素 / 补偿措施

Key words

cable truss structure / temperature effect / mechanical response / influencing factor / compensation measure

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基金资助

国家土建结构预制装配化工程技术研究中心资助项目(2023CPCCE-K01)

国家自然科学基金资助项目(52178095)