Digital twin-oriented generation of structural data and models with LiDAR scan point clouds

Accurate geometric and structural data are essential for the development of digital twins for existing infrastructure systems, particularly in the context of building resilience. Many older or poorly documented structures either lack as-built plans or exhibit deviations from the original design due to construction inconsistencies, undocumented modifications, or repairs. As a result, capturing current structural conditions becomes a necessary first step for generating reliable analytical models, conducting assessments, and enabling long-term infrastructure monitoring. In this study, we evaluate the effectiveness of LiDAR-derived point clouds in generating finite element models (FEM) of a infrastructure, using a stadium as a case study. We compare models built from structural plans and point cloud data against on-site measurements in terms of geometry, dimensions, joint displacements, and support reactions. Our results show that point cloud-based models consistently outperform plan-based models in replicating actual geometric and structural behavior. These findings demonstrate that point cloud scanning can serve as a reliable, efficient, and scalable method for generating digital structural models. This capability is especially valuable for building resilient infrastructure systems, where timely and accurate digital representations are needed for performance tracking, diagnostics, and model updating over time. The study highlights the practical application of point cloud data for digital twin development in large-scale, load-bearing public structures.