A two-step approach for damage detection in a real 3D tower using the reduced-order finite element model updating and atom search algorithm (ASO)

In this paper, an effective approach is presented for solving damage identification problems. The key factor of this approach is based on the model updating and inverse method. First, Root-Mean-Square-Error (RMSE), which registers the differences between frequencies at two states; damaged stage and undamaged stage, is used to establish the objective function. Then, an effective optimization algorithm named the Atom search algorithm (ASO) is employed to minimize the objective function, which accounts for variables related to stiffness reduction in the structures. The process of calculating the objective function during the course of iterations is based on the reduced-order finite element model of a real 3D tower named Guangzhou New TV Tower (GNTT) to extract the frequencies at each iteration. This process will implement continuously until finding the damaged values that agree with the objective function with the minimum error. The results obtained in this paper prove that the proposed approach can predict damaged stories with high reliability and acceptance error.In this paper, an effective approach is presented for solving damage identification problems. The key factor of this approach is based on the model updating and inverse method. First, Root-Mean-Square-Error (RMSE), which registers the differences between frequencies at two states; damaged stage and undamaged stage, is used to establish the objective function. Then, an effective optimization algorithm named the Atom search algorithm (ASO) is employed to minimize the objective function, which accounts for variables related to stiffness reduction in the structures. The process of calculating the objective function during the course of iterations is based on the reduced-order finite element model of a real 3D tower named Guangzhou New TV Tower (GNTT) to extract the frequencies at each iteration. This process will implement continuously until finding the damaged values that agree with the objective function with the minimum error. The results obtained in this paper prove that the proposed approach can predict damaged stories with high reliability and acceptance error.C