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

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. (C) 2017 American Society of Civil Engineers.