Modal

Algorithm

Engineering

Bayesian probability

Polymer chemistry

Approximate Bayesian computation

Probabilistic logic

Computation

Weighting

Estimation theory

Computer science

Mathematical optimization

A-weighting

Radiology

Likelihood function

Mathematics

Inference

Operations management

Artificial intelligence

Medicine

Chemistry

Metric (unit)

Sensors

A novel probabilistic approach for model updating based on approximate Bayesian computation with subset simulation (ABC-SubSim) is proposed for damage assessment of structures using modal data. The ABC-SubSim is a likelihood-free Bayesian approach in which the explicit expression of likelihood function is avoided and the posterior samples of model parameters are obtained using the technique of subset simulation. The novel contributions of this paper are on three fronts: one is the introduction of some new stopping criteria to find an appropriate tolerance level for the metric used in the ABC-SubSim; the second one is the employment of a hybrid optimization scheme to find finer optimal values for the model parameters; and the last one is the adoption of an iterative approach to determine the optimal weighting factors related to the residuals of modal frequency and mode shape in the metric. The effectiveness of this approach is demonstrated using three illustrative examples.

Probabilistic Updating of Structural Models for Damage Assessment Using Approximate Bayesian Computation