Identification of member connectivity and mass changes on a two-storey framed structure using frequency response functions and artificial neural networks

This paper presents a structural health monitoring (SHM) technique that utilises pattern changes in frequency response functions (FRFs) as input parameters for a system of artificial neural networks (ANNs) to assess the structural condition of a structure. To verify the proposed method, it is applied to numerical and experimental models of a two-storey framed structure, on which structural damage is induced by member connectivity and mass changes, respectively. For the numerical structure, simulated time-history data are polluted with various levels of white Gaussian noise in order to realistically represent field-testing conditions. As a damage indicator, residual FRFs are used, which are derived by calculating the differences in FRF data between the undamaged/baseline structure and the structure with changed joint conditions or added mass. To obtain suitable patterns for neural network training, principal component analysis (PCA) techniques are adopted to reduce the size of the residual FRF data and to filter noise. A hierarchical system of individual ANNs, termed network ensemble, is then trained to map changes in PCA-reduced residual FRFs to damage conditions. The results obtained for both damage investigations, namely joint damage and mass changes, demonstrate that the proposed SHM technique is accurate and reliable in assessing the condition of the test structure numerically and experimentally based on direct FRF measurements and network ensemble analysis. From the outcomes of the individual networks, it is found that the proposed hierarchical network ensemble approach is highly efficient in filtering poor results of underperforming networks obtained from measurement locations with low damage sensitivity.