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基于模型试验的悬索桥结构损伤识别研究 被引量:8

MODEL TEST STUDY ON DAMAGE IDENTIFICATION FOR SUSPENSION BRIDGES
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摘要 通过整桥模型试验,探讨了悬索桥结构损伤识别方法。首先面向损伤识别研究设计制作了长10m的悬索桥试验模型,并通过模型误差分析建立了相应的高精度有限元模型。基于悬索桥结构健康监测和试验检测的主要常用参数以及这些参数对结构损伤的灵敏性和相关性研究,确定损伤识别策略。采用有限元模型模拟可能的损伤工况,从而生成BP网络的训练样本数据。再将试验模型作为"实际结构"通过损伤模拟试验生成网络测试数据。就试验模拟的损伤情况而言,对损伤位置的识别准确率达到了86%,相应的损伤程度识别精度也达到可接受程度。显示了该方法较好的应用前景,对基于监测系统的悬索桥健康状态识别与评价具有参考意义。 Through model testing, damage identification techniques for suspension bridges are investigated. A10m long suspension bridge model was designed and manufactured for the purpose of damage identification testing. Using the physical model for error analysis, a corresponding high accuracy finite element model was developed for the test model. The damage identification strategy is determined using the parameters provided by the monitoring system and/or tests on the suspension bridge, and considering the sensitivity and correlation of these parameters with damages. The FEM was used to develop possible damage cases which provided the training sample data for the BP network, and then the testing data were physically modeled on the test model. For the damage cases simulated, damage locations were identified correctly 86% of the time, with acceptable accuracy in identifying the degree of damage. The method has good prospects for application. It also has significant implications for health status evaluation of suspension bridges using structural monitoring.
出处 《工程力学》 EI CSCD 北大核心 2014年第6期132-137,共6页 Engineering Mechanics
基金 国家自然科学基金项目(51178070) 中央高校基本科研业务费项目(2012TD015)
关键词 悬索桥 损伤识别 健康监测 模型试验 神经网络 suspension bridge damage identification health monitoring model test neural network
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