Framework for long-term structural health monitoring by computer vision and vibration-based model updating

This study aims at developing a framework for assessing and tracking structural conditions by combining computer vision-based three-dimensional (3D) reconstruction and vibration measurement. Starting from a finite element (FE) model with unknown geometric and material properties, the framework seeks to obtain the accurate representation of the structure by applying a sequence of two types of operations: (1) image collection, 3D reconstruction, and the extraction of localized geometric properties, and (2) vibration measurement, operational modal analysis, and extraction of global stiffness properties. The first step, termed the computer vision step, is based on the alignment of the approximate initial finite element model to the 3D reconstruction, from which the area and second moment of area associated with each element of the model are retrieved by slicing the reconstructed mesh. The second step, termed the vibration measurement step, performs system identification using vibration measurement data, followed by the Bayesian model updating to calibrate the element stiffness. Every time one of those operations is performed, the new information is merged into all the information obtained previously. The framework is validated using a laboratory-scale bamboo cantilever beam. The experimental validation showed the potential of the proposed framework for effectively assessing and tracking localized structural conditions by heterogeneous sources of information.