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With a goal to improve our understanding of the relationships between microstructural features and ultrasonic signal behavior, concrete specimens of varying water-to-cement ratio were imaged using X-ray computed tomography (CT), and subsequently subjected to ultrasonic testing. From the CT scans, measurements were made of cement paste density and number of interfaces. Ultrasonic signals produced using a through-transmission configuration were analyzed and fit to a diffusion model to separate absorption from scattering attenuation. The results showed that at the frequencies tested, ultrasonic dissipation rate correlated weakly with paste density, while diffusivity correlated well with number of interfaces, but only if entrained air is considered separately. Cement paste density was found to be very well predicted by diffusivity, leading to a clear power-law relationship between diffusivity and compressive strength.
Irrespective of the experimental care used to acquire Computed Tomography Data, certain artifacts might still exist such as: Noise, Blurring, Ring Artifacts etc. To tackle this problem, a complete multi-level framework employing AI (Deep Artificial Neural Nets), targeting specific artifacts individually, is presented. The goal is to render the data suitable for subsequent unproblematic segmentation without any loss of information, compared to manual conditioning with traditional filters.
The strategy can therefore be used to acquire faster CT data (e.g. in-situ investigations) and ensure legacy with existing data obtained, perhaps, on older instruments.