Practical model updating of the Ting Kau Bridge through the MCMC-based Bayesian algorithm utilizing measured modal parameters. (1st March 2022)

• Record Type: Journal Article
• Authors: Fang, Chen; Liu, Hong-Jun; Lam, Heung-Fai; Adeagbo, Mujib Olamide; Peng, Hua-Yi
• Is Part Of: Engineering structures. Volume 254(2022)
• Publication Date: 2022-03-01
• Subjects: Bayesian model updating -- Markov chain Monte Carlo -- Cable-stayed bridge -- Influence line; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.113839
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A novel Bayesian model updating method was developed using Python–Abaqus interface. The new method was employed for the model updating of Ting Kau Bridge. The effect of model class on the results of model updating is studied. The full-scale vehicular load test was designed and implemented. The updated FEM is verified using the vehicular load test results. Abstract: Bayesian model updating framework provides a reliable method for building high-fidelity finite element models (FEMs). To realize the efficient model updating of large-scale civil engineering structures, a practical Bayesian inference framework based on software interaction is proposed. The newly developed framework was applied to update the FEM of a long-span cable-stayed bridge, Ting Kau Bridge in Hong Kong, utilizing measured modal parameters from the literature. The model updating results are found to be highly sensitive to the selection of model classes. Furthermore, the area of the main girder of the bridge deck is a key parameter influencing the lower modes of the cable-stayed bridge. A full-scale vehicular load test is conducted on the Ting Kau Bridge to obtain the displacement influence line through the data recorded by GPS sensors on the bridge. The set of measured influence lines is employed to verify the accuracy of the updated FEM. The results demonstrate that the characteristics of the FEM updated using the proposed Bayesian model updating framework based on measured dynamic properties are consistent with the structural characteristics of the bridge. The proposed framework can facilitate the structural health monitoring of large-scale civil engineering structures.