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Bridge live load effects based on statistics of extremes using on-site load monitoring

机译:使用现场负载监控,基于极限统计数据来桥接桥梁的活荷载效果

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The initial (i.e., at time t = 0) reliability index, the time-variant live load, and the resistance deterioration processes are some of the most important factors in conducting a reliability-based life-cycle analysis of a highway bridge structure. In such an analysis, at least for a newer structure, there is likely more confidence in the geometry and material properties of the structure that determine its capacity than there is in the various loading conditions and scenarios that will place demand upon the structure. Structural health monitoring (SHM) offers a potentially powerful means to obtain site-specific data. However, questions that must be addressed are: what information to collect? how often? and how it should be processed? This paper examines the potential of utilizing the statistics of extremes to answer these questions. By using on-site SHM and observing only the maximum daily peak strain values over time, it is determined that one can successfully modify an initial estimate of truck weight and volume to determine the actual distribution and volume observed at the site. Although a newly developed idea in this work and specific to an initial assumed Gumbel distribution, the method shows interesting potential in the monitoring and assessment of structural systems.
机译:在对公路桥梁结构进行基于可靠性的生命周期分析时,初始(即,在时间t = 0时)可靠性指标,时变活荷载和电阻劣化过程是一些最重要的因素。在这种分析中,至少对于较新的结构而言,确定结构容量的结构的几何形状和材料特性可能比对结构施加需求的各种加载条件和方案更有信心。结构健康监视(SHM)提供了一种潜在的强大手段来获取特定于站点的数据。但是,必须解决的问题是:要收集哪些信息?多常?以及应该如何处理?本文研究了利用极端统计信息回答这些问题的潜力。通过使用现场SHM并随时间仅观察最大日峰值应变值,可以确定可以成功修改卡车重量和体积的初始估计值,以确定在现场观察到的实际分布和体积。尽管这项工作中出现了新的想法,并且特别针对初始假定的Gumbel分布,但是该方法在监视和评估结构系统中显示出有趣的潜力。

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