In this study, the creation of a 3D digital model of a bridge structure by applying computer vision technology to LiDAR point cloud data acquired through laser scanning is proposed for determining structural damage. This method first identifies and labels the individual components of a bridge and then creates a detailed 3D digital model of its structure. An octree data structure is used to efficiently store and process the point cloud data to create the digital model.
A static loading test on an actual bridge structure demonstrated the effectiveness of the proposed method in identifying individual components and analyzing the structural deformation. Based on the results, a 3D digital model can be used to identify structural damage to bridges during bridge inspection, monitoring, and maintenance.
Tendon damage is a major risk of prestressed structures. Ground anchors are structural elements that introduce high levels of prestress, typically over 1000 kN. Tendon damage can threaten the stability of the structure they support. Particularly, visual inspection of buried ground anchor tendons is impossible. Thus, assessing tendon damage is essential during its service life. This study proposes an embedded tendon damage detection method based on the magnetic hysteresis change. Different damage degrees in tendons were measured at room temperature using the fabricated electromagnetic induction (EMI) sensor. Subsequently, the induced electromotive force (EMF) and magnetic flux density were obtained. The finite element simulation results showed a quadratic relationship between the effective cross-sectional area reduction ratio of the specimen due to damage and the peak of magnetic flux density. The experiment results were compared with the simulation results. This study introduces a promising nondestructive evaluation (NDE) method for detecting damage in the embedded tendon of ground anchors.
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