基于神经网络算法和自振频率测试的连续梁桥模型修正
PDF下载 (487)张增辉,揭志羽,朱建朝,王维国.基于神经网络算法和自振频率测试的连续梁桥模型修正[J].宁波大学学报(理工版),2024,37(5):1-11.DOI:10.20098/j.cnki.1001-5132.2023.1116
ZHANG Zenghui,JIE Zhiyu,ZHU Jianchao,WANG Weiguo.Model correction of continuous girder bridges based on neural network algorithm and natural frequency testing[J].Journal of Ningbo University(Natural Science & Engineering Edition),2024,37(5):1-11.DOI:10.20098/j.cnki.1001-5132.2023.1116
| Title: | Model correction of continuous girder bridges based on neural network algorithm and natural frequency testing |
| 作者: | 张增辉, 揭志羽, 朱建朝, 王维国 |
| Author(s): | ZHANG Zenghui, JIE Zhiyu, ZHU Jianchao, WANG Weiguo |
| 关键词: | 连续梁桥; 神经网络; 自振频率; 灵敏度分析; 模型修正 |
| Keywords: | continuous girder bridge; neural network; natural frequency; sensitivity analysis; model correction |
| 分类号: | U448.21; U441+.3 |
| DOI: | 10.20098/j.cnki.1001-5132.2023.1116 |
| 文献标识码: | A |
| 摘要: | 针对有限元模型计算结果往往与实际结构存在偏差的问题, 本文以连续梁桥为研究对象, 提出了基于神经网络和自振频率的有限元模型修正方法. 首先介绍了反向传播神经网络(BPNN)、径向基神经网络(RBFNN)以及粒子群优化算法-反向传播神经网络(PSO-BPNN)的基本原理. 其次, 建立空心板梁、小箱梁、T梁3种不同截面类型连续梁桥有限元模型并计算得到其动力特性, 同时对实桥进行振动测试. 通过灵敏度分析确定待修正参数, 对比分析了3种不同神经网络的修正效果, 并将修正前后计算值对照实测数据进行验证. 结果表明: (1)混凝土弹性模量和重力密度对于桥梁自振频率的灵敏度远大于泊松比, 选取弹性模量和重力密度为待修正参数. (2)修正后的有限元模型误差均降低到了7%以内, 并且通过模态置信准则验证了修正后的模态置信度MAC值均提高到了94%以上. (3)PSO-BPNN相比于BPNN和RBFNN具有更高的精度和可靠性, 基于PSO-BPNN修正后的有限元模型更能反映实际桥梁结构在真实条件下的力学响应情况 |
| Abstract: | In order to solve the problem of deviation between the finite element model results and the actual structure, this paper takes continuous girder bridges as research object and proposes a corrective approach for finite element models based on neural networks and natural frequency testing. Firstly, the fundamental principles governing backpropagation neural networks (BPNN), radial basis function neural networks (RBFNN), and particle swarm optimization-backpropagation neural networks (PSO-BPNN) are introduced. The finite element models for three types of continuous girder bridges featuring distinct cross-sections: hollow slabs, box girders, and T-beams are then established and analyzed. This involves computing the corresponding dynamic characteristics and conducting vibration tests on real bridges. Sensitivity analysis is employed to identify parameters requiring correction. A comparative assessment of the corrective impacts of these three neural networks ensues, validated against empirical data. Notably, the elastic modulus and density exert a more pronounced influence on natural frequencies for bridges compared to Poisson’s ratio, prompting their selection for correction. The corrected finite element models exhibit errors reduced to within 7%, accompanied by all corrected modal assurance criterion (MAC) values surpassing 94%. PSO-BPNN demonstrates superior accuracy and reliability when juxtaposed with BPNN and RBFNN, yielding finite element models that more accurately depict the mechanical response of actual bridge structures under realistic conditions |
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| 备注/Memo: | 收稿日期: 2023−11−13. 宁波大学学报（理工版）网址: http://journallg.nbu.edu.cn/ 基金项目: 国家自然科学基金（51708305）; 浙江省自然科学基金（LY24E080001）; 宁波市自然科学基金（2021J094）. 第一作者: 张增辉, 硕士研究生, 主要研究方向: 钢结构和钢-混组合结构桥梁. E-mail: 821899146@qq.com *通信作者: 揭志羽, 博士/副教授, 主要研究方向: 钢结构和钢-混组合结构桥梁. E-mail: jiezhiyu@nbu.edu.cn 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ |
