Detecting and Reconstructing the 3D Geometry of Subsurface Structural Damages Using Full-Field Image-Based Sensing and Topology Optimization

Most of the critical defects in structural components are invisible at the surface, mainly throughout early stages of deterioration, causing their timely detection to be a challenge. Assessing the actual and accurate 3D form and extent of interior defects is a complicated and also cumbersome task, unexpectedly with the developments in NDE techniques. Unlike the majority of traditional methods based on specialized forms of surface-penetrating waves or radiation imaging, this research uses optical cameras for full-field sensing of surface strains and deformations using the 3D-DIC technique as the basis for damage identification. This data-rich representation of behavior of the structural component is then leveraged in an inverse mechanical problem to reconstruct the underlying subsurface abnormalities. The inverse problem is solved through a topology optimization formulation that iteratively adjusts a fine-tuned FEM of the structure to infer abnormalities within the structure. Recently illustrated the feasibility of detecting and reconstructing the existence of 3D defects within small-scale structural components such as coupons using the proposed idea, this work focuses on expanding on the work by the authors to reveal that the proposed idea can be employed on large-scale structural components using the rich data from full-field image-based measurements to enable the identification of a more detailed picture of the internal defects. Thus, the goal of this research is to demonstrate the practicability and investigate the performance of the previously proposed method through an experimental program in which a set of large-scale structural components such as steel beams with and without buried defects are tested with full-field DIC sensing. A corresponding set of research steps with an increasing level of sophistication are designed to assess the capability of the approach to estimate steel material properties then to extent to infer the 3D shape of embedded defects. Upon completion, this research is expected to demonstrate the feasibility and practicality of the proposed subsurface structural component condition assessment technique and pave the way for its future implementation in existing structures.