Bayesian Model Updating Based on Structural Health Monitoring using Metamodeling Techniques

Bayesian modeling techniques are widely used in structural health monitoring to manage uncertainty in real-time data. These techniques integrate a probabilistic framework into model updating procedures, making Bayesian finite element model updating a preferred method. However, Bayesian updating can cause very high computational cost in solving complex structures. To overcome this, researchers are investigating metamodeling techniques that replace a useful computational model with a more expensive model by using a functional approximation of the original model. However, the model calibration process can be accelerated and can also facilitate faster evaluation. However, despite its advantages, there are still areas for improvement. In this study, well-known metamodeling techniques in the literature are used and an experimental study is examined to evaluate their performance.