Structural Health Monitoring of Beam Model Based On Swarm Intelligence-Based Algorithms And Neural Networks Employing FRF

In recent decades, swarm optimization methods have been employed to address various optimization problems in structural health monitoring (SHM). One of the widely recognized swarm-based algorithms, particle swarm optimization (PSO), has gained significant popularity and found extensive applications across diverse fields. However, it presents some limitations, such as the low convergence rate in the iterative process. The butterfly optimization algorithm (BOA) is a recently developed algorithm that has demonstrated its performance in solving a variety of optimization problems. In this research, a novel hybrid swarm optimization algorithm is introduced, integrating PSO and BOA, with the aim of enhancing its effectiveness. To overcome the limitations of the traditional Artificial Neural Network (ANN) technique and enhance its training performance, this new hybrid algorithm is integrated with ANN. The study offers valuable insights into the creation of a predictive model, known as PSO-BOA-ANN, for detecting structural damage. Input parameters for the model include natural frequencies, while the output parameter is the severity of the damage. To test the efficiency of the proposed technique, data were collected from a finite element model using a simulation tool, and from frequency response function (FRF) after experimental modal analysis for single and double cracked aluminum beams considering different crack depths. A comparative analysis was conducted between the results obtained from PSO, BOA, GA, and their respective combinations with ANN. The findings indicate that the novel PSO-BOA-ANN approach outperforms the other approaches in terms of accuracy when it comes to damage prediction