Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating
Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating
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DOI：
10.1002/stc.372
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发表时间：
2011-04-01
影响因子：
5.4
通讯作者：
Au, S. K.
中科院分区：
文献类型：
--
作者：
Chow, H. M.;Lam, H. F.;Au, S. K.
提出了一种确定在结构上安装给定数量传感器的最佳位置的方法，以便为结构模型更新提取尽可能多的信息。采用信息熵测度来量化模型参数的不确定性。将传感器最优配置问题转化为一个离散优化问题，以信息熵测度为最小，传感器配置为最小化变量。这种离散优化问题的计算要求很高，特别是对于大型结构。一个有效的基于遗传算法的优化方法来解决这个最小化问题，使基于熵的传感器优化配置方法适用于大型结构系统。一个典型的输电塔作为一个数值例子来说明所提出的方法。最优传感器布置技术和所提出的优化方法的性能进行了验证，使用测量的动态数据从2.6米高的输电塔模型在实验室条件下。通过利用并行计算技术，可以减少所提出的优化方法的计算时间。版权所有（C）2009约翰威利父子有限公司
A methodology is presented for the identification of the best locations to install a given number of sensors on a structure so as to extract as much information as possible for structural model updating. The information entropy measure is employed to quantify the uncertainties of the set of identified model parameters. The problem of optimal sensor placement is formulated as a discrete optimization problem in which the information entropy measure is minimized and the sensor configurations are taken as the minimization variables. This discrete optimization problem is computationally demanding especially for large-scale structures. An efficient genetic algorithm-based optimization method is developed to solve this minimization problem to make the entropy-based optimal sensor configuration approach applicable for large-scale structural systems. A typical transmission tower is first used as a numerical example to illustrate the proposed methodology. The performance of the optimal sensor placement technique and the proposed optimization method are then verified using the measured dynamic data from a 2.6 m high transmission tower model under laboratory conditions. The computational time of the proposed optimization method can be reduced in the future by making use of the parallel computing technology. Copyright (C) 2009 John Wiley & Sons, Ltd.