Reconstruction of full-field dynamic responses for large-scale structures using optimal sensor placement

This study explores an optimal sensor placement approach for reconstructing full-field dynamic responses using a set of basic vectors obtained from high-fidelity data. The novelty is the combination of the reduced-order model with sparse promotion, which makes the sensor optimization algorithm effective. Pivoted QR decomposition was performed to accelerate the full-field reconstruction of large-scale structures. To improve the reconstruction precision, an online–offline paradigm was used to create the reduced-order model offline and estimate the sparse coefficient online. The proposed method was verified using a high-rise building case study. The influential factors of reconstruction precision, such as measurement noise, different mode orders, number of sensors, and various types of dynamic responses, were investigated. The results show that the proposed method is accurate and reliable for reconstructing full-field responses, providing a potential alternative for structural health monitoring of large-scale structures.