Structural Identification of a Concrete-Filled Steel Tubular Arch Bridge via Ambient Vibration Test Data

Abstract

Structural identification (St-Id) is an effective structural evaluation approach for health monitoring and performance-based engineering. However, various uncertainties may significantly influence the reliability of St-Id. This paper presents ambient vibration measurements to develop a baseline model for a newly constructed arch bridge over Hongshui River in Guangxi, China. In this study, modal parameter identification was performed using the random decrement (RD) technique together with the complex mode indicator function (CMIF) algorithm, and the results were compared with those from stochastic subspace identification (SSI). First, a three-dimensional (3D) finite-element (FE) model was constructed to obtain the analytical frequencies and mode shapes. Then, the FE model of the arch bridge was tuned to minimize the difference between the analytical and experimental modal properties. Three artificial intelligence algorithms were used to calibrate uncertain parameters: the simple genetic algorithm (SGA), the simulated annealing algorithm (SAA), and the genetic annealing hybrid algorithm (GAHA). The simulation results showed that GAHA exhibited the best performance in mathematic function tests among the three methods and that the large-scale arch bridge could be efficiently calibrated using a hybrid strategy that combines SGA and SAA. To verify the admissibility of the calibration procedure, a sensitivity analysis was performed for the Young's modulus of the steel members, and the relative error for the static deformation of the bridge deck was determined. Finally, to verify the accuracy of the results, a multimodel updating method based on Bayesian statistical detection was analyzed for further validation. Through a detailed St-Id study using precise modeling, operational modal analysis (OMA), and the artificial intelligence algorithms, the authors confirmed the accuracy of the updated FE model for further structural performance prediction.

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

“…Particularly, in the work done by Zhou et al. ( 2017b ), a GA is also applied to minimize the difference between numerical and experimental data related to the static response of the bridge. Useful insights about the application of a GA for model updating based on static or dynamic tests are reported by Chisari et al.…”

Experimental and Numerical Study on Dynamics of Two Footbridges with Different Shapes of Girders

“…A description of the basic steps to identify dynamic parameters of the structure could be found in [32]. They included the creation of a priori models, vibration testing, post-processing of received data, validation and calibration, and also the utilization of a numerical model in some cases [33]. During the experimental study, two basic methods could be distinguished to find out the modal parameters.…”

Output-only structural parameter identification with evolutionary algorithms and correlation functions

“…With the purpose of improve the computational efficiency, some hybrid methods were proposed with the approach of roughly search with swarm intelligence algorithms till close to the solution, then fine search with guided methods to the optimal. The genetic annealing hybrid algorithm, combining simple GA and simulated annealing algorithm, was proposed for structural identification in large-scale arch bridge [34]. The results showed that the hybrid identification method exhibited the best performance in accuracy and efficiency of results in comparison with simple GA and simulated annealing algorithm.…”