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Uncertainty quantification in ultrasonic guided-waves based damage localization

Cantero-Chinchilla, Sergio; Chiachío, Juan; Chiachío, Manuel; Chronopoulos, Dimitrios; Jones, Arthur; Essa, Yasser; Martín De La Escalera, Federico


Sergio Cantero-Chinchilla

Juan Chiachío

Manuel Chiachío

Dimitrios Chronopoulos

Arthur Jones

Yasser Essa

Federico Martín De La Escalera


Bayesian methods for inverse problems offer higher robustness to noise and uncertainty than deterministic, yet accurate, inference methods. Both types of techniques typically focus on finding optimal model parameters that minimize an objective function, which compares model output with some acquired data. However, uncertainties coming from different sources, such as: (1) the material manufacturing process, (2) material's mechanical properties , (3) measurement errors, or (4) the model and its parameters, may cause inference errors and loss of information should they are not properly taken into account. These uncertainties might have important safety and economic consequences in damage-related applications, such as in structural health monitoring of aerospace structures. This paper aims at illustrating the benefits of using probability based methods instead of deterministic approaches. A case study is presented, which illustrates the use of a hyper-robust Bayesian damage localization method when compared to a deterministic one. The results show that Bayesian inverse problem is more robust to data noise and uncertainties stemming from the model parameters than deterministic methods.


Cantero-Chinchilla, S., Chiachío, J., Chiachío, M., Chronopoulos, D., Jones, A., Essa, Y., & Martín De La Escalera, F. (2019). Uncertainty quantification in ultrasonic guided-waves based damage localization

Conference Name 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2019)
Conference Location Crete, Greece
Start Date Jun 24, 2019
End Date Jun 26, 2019
Acceptance Date Jun 3, 2019
Publication Date Jun 26, 2019
Deposit Date Aug 7, 2019
Publicly Available Date Aug 8, 2019
Keywords Bayesian inverse problem; SHM; Ultrasound; Guided waves; Damage localization; Hyper-robust model; Uncertainty quantification
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