This work presents a surrogate model-based Bayesian model updating (BMU) approach for automated damage identification of large-scale structures, which outperforms methods currently available in the literature by effectively solving the real-time damage identification challenge. The computational difficulties involved in Bayesian inference using intensive numerical models are circumvented by implementing a high-fidelity surrogate model and an adaptive Markov Chain Monte Carlo (MCMC) algorithm. The developed surrogate model combines adaptive sparse polynomial chaos expansion (PCE) and Kriging meta-modelling. The optimal order of the polynomials in the PCE is automatically identified by a model selection technique for sparse linear models, the least-angle regression (LAR) algorithm. Then, the optimal PCE is inserted into a Kriging predictor as the trend term, while the stochastic term is fitted through a global optimization algorithm. Afterwards, the surrogate model bypassing the original numerical model is used for BMU exploiting monitoring data extracted from continuous ambient vibration measurements. The computational demands of the MCMC algorithm are kept minimal by implementing an adaptive Metropolis sampling with delayed rejection (DRAM). The effectiveness of the proposed methodology is demonstrated through three case studies: an analytical benchmark; a planar truss structure; and a real case study of an instrumented historical tower, the Sciri Tower in Italy. The presented results demonstrate that the proposed BMU approach is compatible with real-time Structural Health Monitoring (SHM), providing promising evidence for the development of digital twins with superior probabilistic damage identification capabilities.

Real-time Bayesian damage identification enabled by sparse PCE-Kriging meta-modelling for continuous SHM of large-scale civil engineering structures

Garcia Macias E.;Ubertini F.
2022

Abstract

This work presents a surrogate model-based Bayesian model updating (BMU) approach for automated damage identification of large-scale structures, which outperforms methods currently available in the literature by effectively solving the real-time damage identification challenge. The computational difficulties involved in Bayesian inference using intensive numerical models are circumvented by implementing a high-fidelity surrogate model and an adaptive Markov Chain Monte Carlo (MCMC) algorithm. The developed surrogate model combines adaptive sparse polynomial chaos expansion (PCE) and Kriging meta-modelling. The optimal order of the polynomials in the PCE is automatically identified by a model selection technique for sparse linear models, the least-angle regression (LAR) algorithm. Then, the optimal PCE is inserted into a Kriging predictor as the trend term, while the stochastic term is fitted through a global optimization algorithm. Afterwards, the surrogate model bypassing the original numerical model is used for BMU exploiting monitoring data extracted from continuous ambient vibration measurements. The computational demands of the MCMC algorithm are kept minimal by implementing an adaptive Metropolis sampling with delayed rejection (DRAM). The effectiveness of the proposed methodology is demonstrated through three case studies: an analytical benchmark; a planar truss structure; and a real case study of an instrumented historical tower, the Sciri Tower in Italy. The presented results demonstrate that the proposed BMU approach is compatible with real-time Structural Health Monitoring (SHM), providing promising evidence for the development of digital twins with superior probabilistic damage identification capabilities.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1533377
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