A variable velocity strategy particle swarm optimization algorithm (VVS-PSO) for damage assessment in structures

In this paper, for the first time, a variable velocity strategy particle swarm optimization (VVS-PSO) is presented to solve the optimization problems ranging from numerical functions to real-world problems. VVS-PSO introduces a new term added in the velocity updating process at each iteration. This new term is controlled by a reduction linear function, which allows VVS-PSO to reach a faster convergence rate. At the same time, it also leads to enhance the accuracy level. In this way, the strategy of position updating in VVS-PSO is more flexible than that of the original PSO. This strategy will support VVS-PSO to improve the distance between the current step and the previous step and to expand the feasible search space around each particle. To illustrate the convergence rate and level of accuracy of VVS-PSO, the original PSO and 4 well-known optimization algorithms are employed to solve 23 classical benchmark functions. Then, an engineering design problem and experimental validation using a four-storey steel frame are also presented to examine the reliability of VVS-PSO for solving particular real applications. VVS-PSO finally is applied to a real 3D reinforced concrete structure for the purpose of damage assessment. First, the modal assurance criterion (MAC) method, which considers the differences between the mode shapes, is combined with the Root-Mean-Square-Error (RMSE) that registers the differences between frequencies at two states, e.g., damaged and undamaged structures, to determine the objective function. Then, VVS-PSO is used to minimize the objective function, which accounts for variables related to stiffness reduction in elements. The presented results illustrate that VVS-PSO can solve the optimization and structural damage assessment problems with very high accuracy and reliability.