Bayesian Probabilistic Approach to Structural Health Monitoring

TLDR

The study builds on using changes in modal‑parameter‑derived model stiffness to detect damage, addressing uncertainties from parameter variation and model error that obscure health assessment. The authors propose a Bayesian probabilistic methodology for structural health monitoring. The method computes, from successive modal‑parameter data sets, the probability that updated stiffness parameters fall below a fraction of their initial values, using a high probability of reduction as a damage proxy, and demonstrates this through simulated online monitoring with continual updates.

Abstract

A Bayesian probabilistic methodology for structural health monitoring is presented. The method uses a sequence of identified modal parameter data sets to compute the probability that continually updated model stiffness parameters are less than a specified fraction of the corresponding initial model stiffness parameters. In this approach, a high likelihood of reduction in model stiffness at a location is taken as a proxy for damage at the corresponding structural location. The concept extends the idea of using as indicators of damage the changes in structural model parameters that are identified from modal parameter data sets when the structure is initially in an undamaged state and then later in a possibly damaged state. The extension is needed, since effects such as variation in the identified modal parameters in the absence of damage, as well as unavoidable model error, lead to uncertainties in the updated model parameters that in practice obscure health assessment. The method is illustrated by simulating on-line monitoring, wherein specified modal parameters are identified on a regular basis and the probability of damage for each substructure is continually updated.