Automated bridge analysis based on computer vision and improved finite cell method

Finite element method (FEM) is one of the essential means of structural analysis. However, the existing finite element modelling relies on manual and design drawings. Therefore, this study proposes an automated method for the numerical analysis of in-service bridges represented by point clouds. The proposed method includes two main innovations: first, an improved finite cell method (FCM) is introduced to generate finite element meshes from point clouds directly. This method eliminates the need for intricate computations involving uniformly distributed grid points as division criteria, significantly reducing the modelling time. Second, to overcome FCM’s limitations in handling structures with multiple material properties, this paper introduces a combination of a three-way topological relationship determination method (TRDM) and RandLA-Net. This approach automatically classifies material properties at integration points within the bridge structure’s physical domain. A model of an arch bridge is subjected to indoor experiments. Through comparative experimentation and ANSYS outcomes, proposed method demonstrates a level of precision akin to that of conventional modelling approaches.