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Impacts of Epistemic Uncertainty in Operational Modal Analysis
Field experimentation on constructed systems demands consideration of many mechanisms of epistemic and aleatory uncertainties as well as human errors and subjectivity. This is especially true in operational modal analysis (OMA) applications that aim to identify the dynamic properties of a structure. Although statistics and probability theory are sufficient for quantifying aleatory uncertainty and bounding the resulting errors in OMA results, there is much debate as to whether the same tools may also be used to quantify epistemic uncertainty. This study explored a framework for better understanding the distinctions and impacts of these two types of uncertainties in OMA and how human errors and subjectivity may be classified. A physical laboratory model was designed to simulate four key sources of epistemic uncertainty that represented the primary test variables: structural complexity (changing boundary conditions, nonlinearity), ambient excitation characteristics (magnitude, directionality, and bandwidth), preprocessing approaches, and modal parameter identification algorithms. The experimental program employed these variables within a full-factorial design and was carried out independently by two experts. To quantify the impacts of epistemic uncertainty, an error function termed the uncertainty evaluation index (UEI) was formulated based on comparing the uniform load surfaces derived from OMA (using pseudomodal flexibility) and the ground truth flexibility obtained from both forced vibration and static testing. The advantage of the UEI is that it provides a physically meaningful approach to distinguish the importance of capturing various modes based on their contribution to the flexibility of the structure. The results demonstrated that proven and accepted data preprocessing techniques and modal parameter identification algorithms can significantly bias OMA results when used in certain combinations under different structural and excitation conditions. Although caution must be used when generalizing the results of this study, they do indicate that epistemic (or bias) uncertainty can be far more significant that aleatory (or random) uncertainty in the case of OMA.
Impacts of Epistemic Uncertainty in Operational Modal Analysis
Field experimentation on constructed systems demands consideration of many mechanisms of epistemic and aleatory uncertainties as well as human errors and subjectivity. This is especially true in operational modal analysis (OMA) applications that aim to identify the dynamic properties of a structure. Although statistics and probability theory are sufficient for quantifying aleatory uncertainty and bounding the resulting errors in OMA results, there is much debate as to whether the same tools may also be used to quantify epistemic uncertainty. This study explored a framework for better understanding the distinctions and impacts of these two types of uncertainties in OMA and how human errors and subjectivity may be classified. A physical laboratory model was designed to simulate four key sources of epistemic uncertainty that represented the primary test variables: structural complexity (changing boundary conditions, nonlinearity), ambient excitation characteristics (magnitude, directionality, and bandwidth), preprocessing approaches, and modal parameter identification algorithms. The experimental program employed these variables within a full-factorial design and was carried out independently by two experts. To quantify the impacts of epistemic uncertainty, an error function termed the uncertainty evaluation index (UEI) was formulated based on comparing the uniform load surfaces derived from OMA (using pseudomodal flexibility) and the ground truth flexibility obtained from both forced vibration and static testing. The advantage of the UEI is that it provides a physically meaningful approach to distinguish the importance of capturing various modes based on their contribution to the flexibility of the structure. The results demonstrated that proven and accepted data preprocessing techniques and modal parameter identification algorithms can significantly bias OMA results when used in certain combinations under different structural and excitation conditions. Although caution must be used when generalizing the results of this study, they do indicate that epistemic (or bias) uncertainty can be far more significant that aleatory (or random) uncertainty in the case of OMA.
Impacts of Epistemic Uncertainty in Operational Modal Analysis
Ciloglu, Korhan (author) / Zhou, Yun (author) / Moon, Franklin (author) / Aktan, A. Emin (author)
Journal of Engineering Mechanics ; 138 ; 1059-1070
2012-02-13
122012-01-01 pages
Article (Journal)
Electronic Resource
English
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