The distribution of pressure differed greatly between breasts. In a large proportion of breasts the compression plate did not provide optimal compression of the breast, the compression force being absorbed in juxtathoracic structures.
This study contributes to the establishment of frequency recommendations for use in coda wave interferometry structural health monitoring (SHM) systems for concrete structures. To this end, codas with widely different central frequencies were used to detect boreholes with different diameters in a large concrete floor slab, and to track increasing damage in a small concrete beam subjected to bending loads. SHM results were obtained for damage that can be simulated by drilled holes on the scale of a few mm or microcracks due to bending. These results suggest that signals in the range of 50-150kHz are suitable in large concrete structures where it is necessary to account for the high attenuation of high-frequency signals.
Diffuse ultrasonic wave measurements used in structural health monitoring applications can detect damage in concrete. However, the accuracy is very susceptible to environmental variations. In this study, a large concrete floor slab was monitored using diffuse wave fields that were generated by continuous-wave transmissions between ultrasonic transducers. The slab was monitored for several weeks while being subjected to changes in environmental conditions. Subsequently, it was damaged using impact hits, resulting in centimeter-scale cracking. The variations caused by the environment masked the effects of the damage in the measurements. To address this issue, the Mahalanobis distance was used to distinguish between the influence of the damage and the influence of the environmental variations. The Mahalanobis model uses amplitude and phase measurements of continuous waves at a set of different frequencies as inputs. A moving window approach was applied to the baseline data set to account for slow trends. This study shows that this technique greatly suppresses most of the variations caused by environmental conditions. All damage events in our data set have been detected.
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