A strip-wise curvature integration deformation reconstruction framework using strain measurement from fiber optic sensors

Distributed fiber optic sensing (DFOS) provides spatially continuous strain measurements and is increasingly used in infrastructure monitoring applications. However, structural deformation, a key response parameter for performance assessment, is not directly available from measured strain data alone. To enable deformation evaluation from distributed strain measurements, this study develops a curvature integration deformation reconstruction framework based on Kirchhoff-Love plate theory specialized for DFOS applications in thin plate structures. This study is designed to accommodate the intrinsic measurement characteristics of DFOS, in which strain data are obtained along discrete fiber paths. Given the number restriction of DFOS, the reconstruction formulation is modified to enable strip-based calculations compatible with DFOS layouts. The reconstruction is formulated as an inverse problem grounded in Kirchhoff-Love plate theory. DFOS measured surface strains are converted into bending curvatures and subsequently integrated along Kirchhoff-Love strips to recover full field displacement. Spline interpolation and boundary correction are incorporated to enhance numerical stability and mitigate measurement uncertainty. To verify the reliability of the method, multiple representative loading conditions on thin plates are conducted, including bending, impact, and buckling. The results are validated through comparison with finite element analysis (FEA) and experimental measurements. In the bending test, the predicted transverse deflection closely matches the imposed displacement, with a relative error of less than 9%. For the impact and buckling conditions, the reconstructed peak transverse deflections also show strong alignment with FEA outputs. These results indicate that the Kirchhoff-Love strips method (KLSM) framework, leveraging DFOS measurement data, provides a reliable and efficient solution for deformation reconstruction in thin-plate structures with limited sensor coverage, supporting potential applications in real-time structural health monitoring and digital twin systems.