Abstract
Structural model updating can be treated as the process to extract information from measurement for modifying the structural model such that the model calculated responses fit the measured data. The updated model is very important for structural response prediction, structural damage detection and structural control. The location for sensor installation has significant effect on the amount of information that can be extracted from the measured data. This paper presents a methodology for identifying the “optimal” locations to install a given number of sensors on a structure so as to find useful information for structural model updating. The proposed method relies on the information entropy as a measure of the uncertainties associated with the identified model parameters for a given sensor configuration. The larger the value of information entropy, the higher the uncertainty of the identified model parameters will be. As a result, the problem of optimal sensor placement can be transformed to a discrete optimization problem with the information entropy as the objective function and the sensor configuration as the minimization variable. However, the corresponding numerical minimization problem is computational demanding for real structures with many degrees of freedom (DOFs). One of the contributions of this paper is to propose a computational efficient optimization method based on genetic algorithm for solving this minimization problem. A model of typical transmission tower with 40 nodes, 160 elements and 216 DOFs is used as a numerical example to illustrate the proposed methodology. The computational time of the proposed optimization method can be future reduced by making use of parallel computing technologies.
Published Version
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