Data compression of very large-scale structural seismic and typhoon responses by low-rank representation with matrix reshape

The intrinsic low-dimensional structure, which is implicit in the large-scale data sets of structural seismic and typhoon responses, is exploited for efficient data compression. Such a low-dimensional structure, empirically, stems from few modes that are active in the structural dynamic responses. Originally, limited to the sensor and time-history dimension, the structural seismic and typhoon response data set generally does not have an explicit low-rank representation (e.g., by singular value decomposition or principal component analysis), which is critical in multi-channel data compression. By the proposed matrix reshape scheme, the low-rank structure of the large-scale data set stands out, regardless of the original data dimension. Examples demonstrate that the developed method can significantly compress the large-scale structural seismic and typhoon response data sets, which were recorded by the structural health monitoring system of the super high-rise Canton Tower.