###### Abstract
These methods are often time-consuming to test the parameters and consume many computer resources gradually. Nevertheless, their accuracy is still not as good, requiring an optimization method to improve the technical goals. Additionally, the updated structure is mostly those simulated by a fixed material without the participation of composites. Based on input parameters from experiments (can be static or dynamic behavior of the structure), various model update solutions have been researched and developed. The solution commonly used in structural model updates is to change the uncertainty parameters so that the analysis results are similar to the experimental results. These methods rely on trial and error in the selection of structural parameters such as geometry, material properties, and boundary conditions. With a small number of parameters for simple, less detailed structures, updating the model is quite simple. However, the usual solutions are relatively tricky when the number of parameters increases. Many researchers have started using optimization algorithms to solve this problem. They allow searching for results in a large space, while ensuring resource savings for the execution environment. Optimizing algorithms with the FE methods also aids the engineers in fast calculation and improvement of the FE modeling, achieving the highest accuracy. For example, an optimization algorithm was used to update the FE model of the bridge structures based on sensitivity analysis [16]; an approach was proposed to update the model by combining simulated annealing and genetic algorithms (GA) [17]; the response surface method and GA were used to update a model for a simple concrete bridge [18]; and GA was also applied for updating the Canonica Bridge FE model [19]. Furthermore, various research has proposed and applied the optimization of algorithms to improve the used cuckoo search algorithm in the search mechanism of FE model parameters [20; 21; 22; 23; 24]. It is worth saying that optimization algorithms are a powerful mathematical tool for solving technical problems, especially in the engineering fields. They are practical and widely applicable in updating the FE model. Optimization algorithms fall into three main categories: heuristic search methods, mathematical methods, and metaheuristic methods. In particular, the metaheuristic method is a random search method that simulates the biological evolution in nature or the social behavior of species. This method is considered to have more advantages than the other two methods. The most commonly used algorithms are genetic algorithms (GA), ant colony optimization (ACO) and particle swarm optimization (PSO). The published results [25] show that the PSO algorithm has faster convergence time in large-scale problems, especially in the problem of updating large structural models. The PSO algorithm does not require detailed mathematical description and finds the best possible value by optimizing an objective function. This helps to perform fast model updates in large structures, saving computational resources while achieving accuracy. In this research, FE model updating regarding the central part from the core structures of the bridge, a large composite plate, is presented. The composite plate is composed of several materials working together. An initial FE model is created based on uncertain material parameters and boundary conditions. With the data processed by actual vibration measurements, the dynamic characteristics of the composites are determined, including frequency and mode shapes. These properties are the outputs of the FE model. The correlation index between the model and experimental results is minimal using the PSO optimization algorithm combined with the objective function. The results obtain the corresponding parameters of the model after being updated. These properties are needed to correct the model, creating a temporary digital replica of the structure at a specified time.