On the Bayesian sensor placement for two-stage structural model updating and its validation

A common approach to update a linear structural model with ambient vibration data or unmeasured excitation is to adopt a two-stage approach. The first stage involves identifying the modal parameters and the second stage involves updating the model parameters using the identified modal parameters. In this study, an optimal Bayesian sensor placement approach is proposed for such two-stage Bayesian model updating. The Bayesian sensor placement problem is formulated as an optimization problem in which the sensor configuration that maximizes the expected information gain in the model parameters is selected as the optimal one. Expressions to estimate the expected information gain in the model parameters are derived assuming a Gaussian posterior and small uncertainty in the model parameters. To illustrate the effectiveness of the proposed approach, two examples involving a simple 10-Degrees of Freedom (DOF) shear building model of a structure, and a 120-DOF space truss structure are considered. The effectiveness and applicability of the proposed approach is validated using the experimental data from a real 3-story frame structure.

Automated summary

This study proposes an optimal Bayesian sensor placement approach for updating linear structural models, involving two stages of identifying modal parameters and updating model parameters, selecting the sensor configuration that maximizes the expected information gain.