Abstract
We address the problem of damage identification in complex civil infrastructure with an integrative modular Bayesian framework. The proposed approach uses multiple response Gaussian processes to build an informative yet computationally affordable probabilistic model, which detects damage through inverse updating. Performance of structural components associated with parameters of the developed model was quantified with a damage metric. Particular emphasis is given to environmental and operational effects, parametric uncertainty and model discrepancy. Additional difficulties due to usage of costly physics-based models and noisy observations are also taken into account. The framework has been used to identify a reduction of a simulated cantilever beam elastic modulus, and anomalous features in main/stay cables and bearings of the Tamar bridge. In the latter case study, displacements, natural frequencies, temperature and traffic monitored throughout one year were used to form a reference baseline, which was compared against a current state, based on one month worth of data. Results suggest that the proposed approach can identify damage with a small error margin, even under the presence of model discrepancy. However, if parameters are sensitive to environmental/operational effects, as observed for the Tamar bridge stay cables, false alarms might occur. Validation with monitored data is also highlighted and supports the feasibility of the proposed approach.
Highlights
A structural health monitoring (SHM) system must be able to identify the performance of complex physical systems
Based on the above remarks, the current paper proposes a hybrid modular Bayesian approach (MBA) to address the damage identification problem
It is recalled that the module 2 of the MBA, approximation of the discrepancy function, requires marginalisation of the computer model multiple response Gaussian processes (mrGp) with respect to the parameters prior
Summary
A structural health monitoring (SHM) system must be able to identify the performance of complex physical systems. One is Journal of Civil Structural Health Monitoring (2019) 9:201–215 the environmental/operational effects— known as confounding influences—which mask the presence and extent of damage This is a well known and extensively documented problem [11, 16, 17, 35]. Unless the effects of model discrepancy are small, assuming them as a zero-mean uncorrelated Gaussian [37, 43] (as in the traditional Bayesian methods) results in biased identifications. For these reasons, the broad influence of environmental variations, and in particular temperature, lead to the development of several temperature-based damage identification methods.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
