An intelligent design of distributed sensor networks for digital twin-based infrastructure monitoring

Efficient deployment of distributed fiber optic sensors (DFOS) is critical for capturing complex strain behavior in structural health monitoring, especially in scenarios involving non-uniform deformation such as buckling. This study presents an intelligent design framework that integrates experimental testing, finite element analysis (FEA), and advanced data-driven techniques to improve strain field reconstruction and guide sensor deployment. A buckling test on a steel plate instrumented with DFOS was conducted to generate high-resolution strain data, which was used to validate a corresponding FEA model. Four interpolation methods, including bilinear, cubic, inverse distance weighting, and radial basis function (RBF), were selected as benchmarks across various sensor counts. Results indicate that the RBF method provides the most accurate reconstruction performance, particularly under sparse sensor configurations. Moreover, multiple placement strategies were investigated to enhance sensor layout efficiency, including weighted K -means clustering, high-gradient hotspot selection, and density-based spatial clustering of applications with noise. An integrated strategy combining these methods was developed to achieve balanced spatial coverage while capturing key deformation zones. The findings demonstrate that data-informed deployment strategies can significantly improve monitoring accuracy while reducing sensor redundancy. Finally, several deployment methods for the DFOS were evaluated by six metrics, offering quantitative insights into trade-offs between sensing accuracy and deployment feasibility. While the case study is based on a specific buckling scenario, the proposed methodology is generalizable to other structural conditions. Specifically, for large-scale infrastructure, the framework can be employed to capture strains at critical nodes and to reconstruct full-field strain distributions for digital-twin-based infrastructure monitoring. Overall, this framework provides a flexible and scalable reference for optimizing DFOS installation in high-strain-gradient environments.