Experimental Damage Identification of a Model Reticulated Shell
School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China
Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Electronics and Information School, Yangtze University, Jingzhou 434023, China
Author to whom correspondence should be addressed.
Academic Editor: Stefano Invernizzi
Appl. Sci. 2017, 7(4), 362; https://doi.org/10.3390/app7040362
Received: 17 February 2017 / Revised: 29 March 2017 / Accepted: 29 March 2017 / Published: 6 April 2017
(This article belongs to the Special Issue Structural Health Monitoring (SHM) of Civil Structures)
The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs), the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR) time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs) were then established and were trained using the Levenberg–Marquardt algorithm (L–M algorithm). The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.