文章摘要
引用本文:宗周红,WANG T L,HUANG D Z,郑振飞.桥梁健康监测应用与研究现状[J].福州大学学报(自然科学版),2002,30(2):127~152
桥梁健康监测应用与研究现状
State-of-the-art report of bridge health monitoring
  
DOI:10.7631/issn.1000-2243.2002.2.127
中文关键词: 健康监测系统,损伤检测,状态评估,模型修正,系统识别,传感器优化布置,神经网络方法,桥梁结构
英文关键词: health monitoring system,damage detection,condition assessment,model updating,system identification,sensors optimum placement,neural networks approaches,bridge structures
基金项目:美国佛罗里达州交通部科技发展项目 (BC818)
作者单位
宗周红,WANG T L,HUANG D Z,郑振飞 1. 福州大学土木建筑工程学院,福建,福州,350002,中国
2. 佛罗里达国际大学土木与环境工程系
Miami,FL,33199,美国 
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中文摘要:
      桥梁损伤诊断与健康监测是近年来国际上的研究热点 ,在实践方面 ,土木工程和航空航天工程、机械工程有明显的差别 ,比如桥梁结构以及其他大多数土木结构 ,尺寸大、质量重 ,具有较低的自然频率和振动水平 ,桥梁结构的动力响应极容易受到不可预见的环境状态、非结构构件等的影响 ,这些变化往往被误解为结构的损伤 ,这使得桥梁这类复杂结构的损伤评估具有极大的挑战性 .本文首先给出了结构健康监测系统的定义和基本构成 ,然后集中回顾和分析了如下几个方面的问题 :①损伤评估的室内实验和现场测试 ;②损伤检测方法的发展 ,包括 :(a)动力指纹分析和模式识别方法 ,(b)模型修正和系统识别方法 ,(c)神经网络方法 ;③传感器及其优化布置等 ,并比较和分析了各自方法的优点和不足 .文中还总结了健康监测和损伤识别在桥梁工程中的应用 ,指出桥梁健康监测的关键问题在于损伤的自动检测和诊断 ,这也是最困难的问题 ;最后展望了桥梁健康监测系统的研究和发展方向
英文摘要:
      The damage diagnosis and health monitoring of bridge structures are active areas of research in recent years. Comparing with the aerospace engineering and mechanical engineering, civil engineering has the specialities of its own in practice. For example, because bridges, as well as most civil engineering structures, are large in size, and have quite low natural frequencies and vibration levels, at low amplitudes, the dynamic responses of bridge structure are substantially affected by the nonstructural components, unforeseen environmental conditions, and changes in these components can easily to be confused with structural damage. All these give the damage assessment of complex structures such as bridges a still challenging task for bridge engineers. This paper firstly presents the definition of structural health monitoring system and its components. the laboratory and field testing research on the damage assessment; signature analysis and pattern recognition approaches, (b) model updating and system identification approaches, (c) neural networks approaches; and of each method are compared and analyzed. Recent examples of implementation of structural health monitoring and damage identification are summarized in this paper. The key problem of bridge health monitoring is damage automatic detection and diagnosis, and it is the most difficult problem. Lastly, research and development needs are addressed.
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