Automated inspection and monitoring of member deformation in grid structures

In large-span grid structures with thousands of members involved, bending deformation of members is one of the most commonly observed damages affecting the normal service, even the safety of structures. Traditional testing and monitoring methods show weakness in the accurate judgment of crooked members and precision of deformation measurements. In this paper, a new deformation inspection and monitoring method of grid structures using image-based 3D reconstruction is proposed, wherein a new method is put forward to automatically recognize and extract the shape deformation of the structural member for the first time. First, the key area with multiple members is modeled as a three-dimensional mesh model using image-based 3D reconstruction. Then, a new automated recognition and extraction algorithm of shape deformation (AREAS) is carried out, and crooked members, together with their deformed shapes, are extracted from the mesh model through AREAS. In this study, a load experiment of a quadrangular pyramid grid structure containing artificial crooked members and speckled members is designed to compare the deformation measurements using image-based 3D reconstruction with those using laser scanning. The comparison of deformation and deformation increment shows an average error within 1 mm for image-based 3D reconstruction, which validates the proposed method in on-site inspection and monitoring.