This study proposes a spatial compressive sensing approach to reconstruct full-field responses of beams and plates from measurements recorded by only a few sensors. To sparsely represent structural full-field responses, analytical dictionaries composed of sine series and orthogonal basis functions tailored to beam and plate structures are constructed. The required number of sensors is determined using the amplitude distribution of mode shape expansion coefficients, and their optimal locations are selected through the Latin hypercube sampling method. The sparse representation vector is reconstructed using the orthogonal matching pursuit algorithm. A comprehensive parametric study is conducted to investigate the effects of response complexity, boundary conditions, and sensor placement on reconstruction accuracy, followed by laboratory test validation. The results demonstrate that the proposed approach enables robust and efficient reconstruction of full-field responses from sparse measurements, offering a potential strategy for enhancing the reliability of vibration-based structural condition assessment.
He et al. (Tue,) studied this question.