Spatial Compressed Sensing for Field Reconstruction in Full-Scale Structural Systems

High dimensional systems, such as large civil infrastructure, exhibit fundamental patterns in space and time which can be exploited for efficient data acquisition, reconstruction, identification and damage detection. This study numerically investigates the applicability of compressed sensing (CS) theory to reconstruct the static displacement field of a multi-storey building using a small number of displacement samples. A full-scale Finite Element (FE) model of the building, developed using Opensees software, is used to capture its static displacement field and vibratory mode shapes, which serve as a tailored physics-guided basis. A sample of displacement data was then randomly selected, aiming to reconstruct the entire displacement field. The results demonstrate that achieving a reliable full-scale reconstruction is feasible with only approximately one percent of the total degrees of freedom in the original model. This highlights the effectiveness of the CS paradigm in accurately reconstructing various measurement fields within buildings, emphasizing its potential to enhance the efficiency of information extraction from spatially distributed sensor networks.