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Optimal sensor placement methodology for uncertainty reduction in the assessment of structural condition
Author(s) -
Yang Wei,
Sun Limin,
Yu Gang
Publication year - 2017
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.1927
Subject(s) - truss , reduction (mathematics) , uncertainty quantification , uncertainty reduction theory , finite element method , vulnerability (computing) , bayesian probability , measurement uncertainty , mathematical optimization , structural health monitoring , vulnerability index , computer science , mathematics , engineering , structural engineering , statistics , ecology , geometry , computer security , communication , climate change , sociology , biology
Summary This paper introduces a novel approach for selecting sensor positions for uncertainty reduction in the assessment of structural condition, where the main difficulty is how to quantify the uncertainty. In order to tackle this problem, a condition index, which is a linear combination of the finite‐element model parameters, is defined. By taking the multiplying coefficient equal to the vulnerability index corresponding to each model parameter, the linearized condition index is able to reflect the influence of local damage on the global damage condition. The uncertainty in the estimate of this linearized condition index can be readily quantified from the uncertainty in the updated model parameters. Bayesian finite‐element model updating is applied for uncertainty quantification in the model parameters. The procedure of the proposed method is illustrated by designing the optimal sensor configuration for a truss structure model. The simulated damage and condition assessment of the truss structure shows that the proposed method is effective in reducing the uncertainty in the condition assessment. Furthermore, it is demonstrated that the proposed method is useful for a more important reason: it can reduce the uncertainty in the damage assessment of vulnerable substructure. Copyright © 2016 John Wiley & Sons, Ltd.