Removing Environmental Influences in Health Monitoring for Steel Bridges Through Copula Approaches
Removing Environmental Influences in Health Monitoring for Steel Bridges Through Copula Approaches
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DOI：
10.1007/s13296-018-0170-3
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发表时间：
2018-10
影响因子：
1.5
通讯作者：
Yi Zhang;Chul-Woo Kim;Jiamin Lin
中科院分区：
文献类型：
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作者：
Yi Zhang;Chul-Woo Kim;Jiamin Lin
民用基础设施的维护已成为当今城市面临的一个尖锐的技术问题。对于潜在风险较大的桥梁结构而言，这一点尤为重要。对于大多数桥梁结构来说，它们可能会经历许多环境和运营条件。在桥梁监测中，真实准确地了解这些外部条件的影响是必要的。然而，以往的许多研究仍然不能很好地刻画结构安全与环境影响之间的关系。需要合适的多元模型来提取桥梁与其关联环境之间的依赖关系信息。本文提出了一种基于Copula的特征敏感指标，用于桥梁结构健康监测，以排除运营和环境条件。该方法首先利用Copula统计特性识别出模态参数随时间的变化，然后通过合适的Copula模型将其去除。基于一座七跨钢板Gerber大桥的振动监测数据，进行了实例分析。利用近十年的温度、加速度等数据对该方法的适用性进行了检验。在此基础上，讨论了如何选择最优的Copula模型来刻画监测时间序列。在桥梁结构健康监测方面也将有一定的改进。
Maintenance of civil infrastructure has become a keen technical issue nowadays for modern cities. This is particular important for the bridge structures which contains large potential risks. For most bridge structures, they may experience lots of environmental and operational conditions. A real and accurate understanding of the influences from these external conditions is necessary in the monitoring of bridges. However, many previous study still cannot depict the relations between the structural safety and environmental influences very well. Suitable multivariate models are demanded to extract the information of dependences between the bridges and its associated environment. In this study, a copula-based feature sensitive indicator is proposed for the bridge structural health monitoring for removing the operational and environmental conditions. Changes in the modal parameters with the time are firstly identified by the copula statistical properties and then removed through an appropriate copula model. A case study is performed based on the observed vibration data collected from a seven-span plate steel Gerber bridge. The data including the temperature and acceleration for the past ten years is utilized to test the applicability of the proposed approach. Based on the results, the selection of the best copula model in characterizing the monitored time series will be discussed. Certain improvement in the bridge structural health monitoring will also be investigated.