عنوان مقاله [English]
One of the most important issues in damage detection is determining the severity of damages. Speed and accuracy in determining the severity of damages depend on the measurement noise. Using the proposed method, damage severity of damaged connections has been calculated in the presence of noise in a faster and more accurate fashion. In the proposed method, instead of using several natural mode shapes, only first proper orthogonal mode shape is used and the damage severity of the rigid connections of moment frames is calculated with acceptable accuracy. Several numerical examples and a laboratory model have been studied to prove the performance of this procedure. The results show that this method has acceptable accuracy and speed to estimate the damage severity in the rigid connections of steel moment frames.
The proposed method is based on the finite element model updating method and the objective function is based on the difference between the proper orthogonal decomposition mode shapes of the intact and damaged structures. In the proposed procedure, the wavelet transform is used to reduce the noise in the displacement history of the structure. An optimized image processing method has been used to record structural displacements in the laboratory sample. To optimize the objective function, the particle swarm optimization method has been used. Two numerical scenarios have been studied using the finite element model of a three-story, three-span steel moment frame structure. Also, three laboratory scenarios have been studied on the three-story one-span steel moment frame laboratory model. In this paper, the exact location of the damaged connections is determined using the optimized mode shape curvature difference method. The effects of noise on the accuracy and efficiency of the proposed method are considered. Numerical scenarios have been studied at three noise levels. The proper orthogonal decomposition mode shapes are calculated using the free vibration of the structure under initial displacement.