The Magdalena Ridge Observatory Interferometer has been conceived to be the most ambitious optical/near-infrared long-baseline imaging interferometer in the world today. We anticipate receiving the second telescope mount and enclosure and associated beamline infrastructure to enable us to attempt first fringes measurements early in 2023. Having reached this important milestone, we anticipate receiving the third copy of all beamline components about one year later and attempting closure phase measurements thereafter. We will present a status update and plans under the new Cooperative Agreement with AFRL for the next phases of the project.
This paper discusses the established and potential applications of high-dimensional data analysis in the fields of structural health monitoring and non-destructive evaluation. Despite the significant potential of high dimensional data analytic methods, a few applications have been implemented in structural health monitoring and non-destructive evaluation. Further, as measuring technologies improve, the requirement of applying these approaches grows. This paper uses thermal videos as an example of high-dimensional data in the non-destructive evaluation field. These thermal videos are used to detect and localize delamination in composite plates, typically found in aircraft wings. Using traditional statistical approaches to analyze videos presents theoretical and practical challenges due to their high dimensionality. Tensor analysis methods help to overcome these issues. To locate the damage, two tensor factorization algorithms are used. For a rectangular damage zone, two vectors are enough to localize the extent of the damage. For more sophisticated cases like a damage in the shape of a circle, higher order of core tensors with larger projection tensors are needed. The results demonstrate these methods are accurate and efficient in terms of computing cost.
3D-printed one-way valves were designed and fabricated to relieve the corrosion-induced internal pressure on concrete structures. These valves were post-installed onto concrete to increase corrosion resistance in the concrete structure and extended the service life. This study investigated an Internet-of-Things device to continuously monitor corrosion in steel-reinforced concrete in order to determine the effectiveness of the valves in preventing corrosion. The IoT device monitors acoustic emission to determine the corrosion stage of reinforced concrete. The ongoing results show that the current valve design is an effective one-way check valve that will allow the internal pressure of the concrete to be released. This type of valve will prevent reinforced concrete surface cracking and extend the life of concrete structures by only releasing internal pressure without allowing for external materials to further corrode the steel reinforcement in concrete.
Surface crack patterns are one of the earliest damage signs in concrete structures. Existing procedures to visually evaluate the damage rely on experts' judgment to interpret the existing cracks. The initial necessary step to quantify and automate this procedure is crack detection. Precise crack detection provides a reliable basis to update the structural parameters and to predict future behavior. Several methods have been investigated to detect cracks based on image processing methods; but, there are several limitations and inaccuracies in these methods. In a number of cases, recordings during damage occurrence are available. The videos comprise not only spatial information but also temporal information. The videos provide a set of images for a unique damage situation. In this study, using video processing methods, a methodology is developed to track crack formation. In this regard, robust principal component analysis is employed to detect new crack propagation. The experimental test data of RC shear walls are used to assess the implemented methodology. The quasi-static cyclic load is applied to these walls, and several cameras captured the video of walls' behavior. Taking advantage of the phase-based motion processing method, a video stabilization is implemented to enhance the accuracy of the crack detection step. Propagation of cracks is monitored by calculating Gini coefficients for each frame. The results show that monitoring this coefficient can indicate new crack formations.
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