Structural Damage Detection Using FRF Data, 2D-PCA, Artificial Neural Networks and Imperialist Competitive Algorithm Simultaneously

作     者：Khoshnoudian, Faramarz Talaei, Saeid Fallahian, Milad 

作者机构：Amirkabir Univ Technol Fac Civil Engn Tehran Iran Islamic Azad Univ Young Researchers & Elite Club Cent Tehran Branch Tehran Iran 

出 版 物：《INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS》 (国际结构稳定性与动力学杂志)

年 卷 期：2017年第17卷第7期

页      面：1750073-1750073页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0814[工学-土木工程] 0801[工学-力学（可授工学、理学学位）] 

主　　题：Damage detection damage index frequency response function artificial neural network imperialist competitive algorithm 

摘      要：In this study, a promising pattern recognition based approach is introduced for structural damage identification using the measured dynamic data. The frequency response function (FRF) is preferably employed as the input of the proposed algorithm since it contains the most information of structural dynamic characteristics. The 2D principal component analysis (2D-PCA) is used to reduce the large size of FRFs data. The output data generated by the 2D-PCA are used to extract the damage indexes for each of the damage scenarios. A dataset of all probable damage indexes is provided;of which 30% are selected to form the train dataset and to be compared with the unknown damage index for an unidentified state of the structure. The sum of absolute errors (SAE) are calculated between the unknown damage index and the selected indexes from the dataset;of which the minimum refers to the most similar damage condition to the unknown one. The artificial neural networks (ANNs) are used to form a smooth function of the SAEs and the imperialist competitive algorithm (ICA) is utilized to minimize this function in order to find the location and severity of the damages of the unknown state of the structure. To validate the proposed method, the damage identification of a truss bridge structure and a two-story frame structure is conducted by considering all the single damage cases as well as multi damage scenarios. In addition, the robustness of the proposed method to measurement noise up to 20% is thoroughly investigated.