Subsurface damage detection and structural health monitoring using digital image correlation and topology optimization

Detecting subsurface defects in structural members is a challenging yet important part of condition assessment of structures. Existing methods in this regard are either based on nondestructive evaluation and testing (NDE/T), or structural health monitoring (SHM) concepts. NDE/T methods require expensive equipment usually based on wave propagation or radiation imaging. Damage detection based on SHM usually relies on global vibration response which has proven successful in informing the existence and sometimes coarse-grained location information about damage, but is fairly limited in reconstructing the 3D shape of internal damage. This paper pro poses to leverage full-field response data obtained by digital image correlation (DIC) in a topology optimization framework to reconstruct the internal damage in members. In other words, this paper shows how perturbations in observable full-field surface measurements can be used as a proxy to detect unobservable internal abnormalities. An initial finite element model of the structure is first created to discretize the member into elements whose constitutive properties are treated as unknowns in the optimization problem. The goal of the optimization is to minimize the discrepancies between the observed full-field response measured experimentally using DIC, and that computed numerically using the model. To that end, an objective function is first computed as the sum of residuals by mapping both responses onto a common grid, which is then pushed to a minimum via the method of moving asymptotes (MMA) as the optimization algorithm. The framework was evaluated on a series of simulated and real-world experiments using steel coupon specimens with artificially manufactured defects. Results show that the proposed method is capable of detecting and reconstructing the location, extent, and shape of the damage with average F1-scores of 82.8% and 69.6% on simulated and real experiments, respectively. Furthermore, a detailed sensitivity analysis demonstrated the effect of various factors on the performance of the proposed approach, including different optimization starting points, defect severity, sensing density, and discretization density. Results from this study have demonstrated that the proposed method is able to successfully extract detailed internal damage information that is otherwise expensive and difficult to achieve with state of-the-art methods and can therefore be used as a promising subsurface damage detection method.