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.
This paper presents vibration-based damage detection (VBDD) for testing a steel-concrete composite bridge deck in a laboratory using both model-based and non-model-based methods. Damage that appears on a composite bridge deck may occur either in the service condition or in the loading condition. To verify the efficiency of the dynamic test methods for assessing different damage scenarios, two defect cases were designed in the service condition by removing the connection bolts along half of a steel girder and replacing the boundary conditions, while three damage cases were introduced in the loading condition by increasing the applied load. A static test and a multiple reference impact test (MRIT) were conducted in each case to obtain the corresponding deflection and modal data. For the non-model-based method, modal flexibility and modal flexibility displacement (MFD) were used to detect the location and extent of the damage. The test results showed that the appearance and location of the damage in defect cases and loading conditions can be successfully identified by the MFD values. A finite element (FE) model was rationally selected to represent the dynamic characteristics of the physical model, while four highly sensitive physical parameters were rationally selected using sensitivity analysis. The model updating technique was used to assess the condition of the whole deck in the service condition, including the boundary conditions, connectors, and slab. Using damage functions, Strand7 software was used to conduct FE analysis coupled with the MATLAB application programming interface to update multiple physical parameters. Of the three different FE models used to simulate the behavior of the composite slab, the calculated MFD of the shell-solid FE model was almost identical to the test results, indicating that the performance of the tested composite structure could be accurately predicted by this type of FE model.
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