Uncertainty propagation via sample-based deterministic model updating for structural damage identification

Structural health monitoring aims for the identification of damage. In order to identify damage using model updating, vibration measurement data is recorded, damage-sensitive features are identified and a numerical model is updated to match the structural behavior observed. However, uncertainty exists in every model updating application in structural dynamics. As established approaches for uncertainty quantification and propagation are dependent on previous assumptions regarding the input data (e.g., modal parameters), in this work, an alternative model updating procedure is proposed, namely the sample-based deterministic model updating (SDMU) approach. The key idea is to decouple the uncertainty incorporation from the actual model updating procedure by combining a sample provision with a numerical optimization algorithm. Using this approach, the uncertainty is incorporated indirectly through the performance of multiple deterministic model updating procedures based on each discrete input sample. The type of sample provision, just as the optimization algorithm, is freely selectable. The performance and adaptability of the proposed SDMU approach are demonstrated using a simple analytical application example and a laboratory steel cantilever beam with the aim of damage identification. Results are compared to the results of a benchmark Bayesian model updating (BMU) method, namely the transitional Markov chain Monte Carlo method. Findings show that the BMU and SDMU outcomes directly reflect their input data, whereby the considered SDMU realizations show a clear and more distinct convergence behavior. This highlights the advantages of the SDMU approach: Adaptability to the problem at hand, independence from assumptions about the input data and correct reproduction of its characteristics.