Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method

The inverse problem of structural system identification is prone to ill-conditioning issues; thus, uniqueness and stability cannot be guaranteed. This issue tends to amplify the error propagation of both the epistemic and aleatory uncertainties, where aleatory uncertainty is related to the accuracy and the quality of sensors. The analysis of uncertainty quantification (UQ) is necessary to assess the effect of uncertainties on the estimated parameters. A literature review is conducted in this paper to check the state of existing approaches for efficient UQ in the parameter identification field. It is identified that the proposed dynamic constrained observability method (COM) can make up for some of the shortcomings of existing methods. After that, the COM is used to analyze a real bridge. The result is compared with the existing method, demonstrating its applicability and correct performance by a reinforced concrete beam. In addition, during the bridge system identification by COM, it is found that the best measurement set in terms of the range will depend on whether the epistemic uncertainty involved or not. It is concluded that, because the epistemic uncertainty will be removed as the knowledge of the structure increases, the optimum sensor placement should be achieved considering not only the accuracy of sensors, but also the unknown structural part.

Automated summary

The inverse problem of structural system identification is prone to ill-conditioning issues, and uncertainty quantification analysis is necessary to evaluate its impact on estimated parameters. The dynamic constrained observability method can compensate for the shortcomings of existing methods, and its correct performance and applicability are demonstrated through the analysis of a real bridge. The optimal sensor placement should consider not only the accuracy of sensors, but also the unknown structural part as epistemic uncertainty is removed with increasing knowledge of the structure.