Browsing by Subject "Nondestructive Testing"

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    Advancements in Imaging of Concrete Members Using Shear Waves
    (2018-12) Asadollahi, Aziz
    The emergence of linear array devices employing dry point contact transducers emitting horizontal shear waves significantly increased the efficiency of data acquisition and enabled using imaging techniques for nondestructive evaluation of concrete members. Reverse time migration (RTM) is a mechanics-based imaging technique that has gained the attention of researchers in the context of nondestructive testing (NDT) in recent years. RTM offers a better focusing over synthetic aperture focusing technique (SAFT), a well-established real-time imaging method for NDT of concrete members, and enables locating reflectors with steep slopes and the bottom boundaries of embedded objects. Despite all advantages, RTM suffers from some limitations. It is computationally costly and demands a massive memory. In addition, RTM algorithm generates images with high-amplitude artifacts and assigns amplitudes to the points of a reconstructed image that are not a true representative of the reflectivity of the scanned medium at those points. This dissertation develops an analytical approach to resolve the computational cost and memory demand bottlenecks of the RTM when dry point contact transducers emitting horizontal shear waves are used for data acquisition. Horizontal shear waves preserve more energy than longitudinal waves after emission allowing inspection of concrete members in deeper depths. However, the lower wavelength of shear waves increases the potential of scattering by aggregates and air voids that affects the quality of the reconstructed images. This dissertation develops a 3D numerical tool to study the scattering attenuation of shear waves in concrete. An efficient algorithm is introduced to generate non-overlapping aggregates and air voids to study the effect of size, shape, and material properties of aggregates as well as the porosity of concrete on the scattering attenuation of shear waves. Moreover, it develops novel techniques to mitigate the high-amplitude artifacts of RTM images and to adjust the amplitudes assigned to the points of an image reconstructed by RTM for homogeneous and concrete members.
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    Nondestructive Evaluation Advancements for Damage Detection in Concrete
    (2016-06) Freeseman, Katelyn
    While concrete is the most widely used civil engineering material, damage detection and progression in concrete structures have still proven to be difficult to address, especially when only one-sided access is available. New technological advances in nondestructive testing technology have created the opportunity to better utilize ultrasonic waves to aid in this damage detection process. However, interpretation of the signal data is a challenging task which often requires subjective assessments. This thesis addresses these limitations via the utilization of ultrasonic array technology for nondestructive damage detection purposes. The ultrasonic shear velocity array system used for this research is particularly advantageous because it can obtain measurements on virtually any concrete specimen, from columns and beams to concrete pavements, and provides a wealth of data from a single measurement. Novel signal interpretation methods were developed for several important concrete applications. Detection of load-induced damage in laboratory beams and a full-scale reinforced concrete column, as well as standard life-cycle damage in concrete pavements caused by freeze thaw or alkali-silica reaction degradation were considered. These investigations culminated in the development of successful and efficient quantitative damage detection methods. Finally, the development and refinement of a simulation program allowed for verification of the experimental investigation and a greater understanding of signal results.
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    Recommendations for Data Collection and Analysis During Field Testing of Ground Penetrating Radar Used to Measure Bituminous Asphalt Compaction
    (2021-07) Cacek, Twain
    MnDOT, as part of a multistate pooled fund project, is developing procedures that use ground penetrating radar for QA/QC in the placement of bituminous asphalt pavement. In July 2020, over 400,000 measurements were collected (using the GSSI PaveScan RDM 2.0) from a 5.5-mile stretch of US Highway 2 near Bena, Minnesota. In this thesis, we analyzed that data to develop conceptual models and identify statistical measures, methods, and tests to characterize the results. Graphical representations of our results are presented that will help guide the next phase of field testing with the ultimate goal of developing a national standard. Spatial correlation was characterized using the experimental semivariogram. This analysis revealed that the correlation length of the dielectric (and therefore air voids) ranges from 3[ft] to 45[ft] near the centerline joint, and is less than or equal to 13[ft] in the middle of the lane. These relatively small correlation lengths demonstrate a lack of significant spatial correlation in asphalt density measurements. Lateral variations in dielectric were revealed by partitioning measurements in the road into 1[ft] wide segments. Summary statistics were then computed using the measurements from each segment. The mean of the dielectric values from each segment varied throughout the road. This variation of the mean led to the development of a conjecture that splits the road into three unique zones: the joint zone (i.e., the centerline joint), the transition zone (i.e., the transition between the joint and the mat), and the mat zone (i.e., the middle of the road). This development is important, because current practices in transportation engineering only acknowledge the presence of two zones in the road (joint and mat). The possibility of sensor bias was examined through the use of histograms, probability plots, and statistical tests. Sensor bias was characterized by the prevalence of questionable measurements and dissimilar distributions for each sensor. In this case, the questionable measurements were identified as such because they were either not physically possible, or unlikely to represent an accurate road measurement. The dissimilar distributions demonstrate that, despite sampling the same population, each sensor makes significantly different measurements than the other sensors. Because it is likely present, sensor bias during data collection must be identified and addressed in real time. Recommendations for future data collection and analysis are also provided. These recommendations primarily pertain to sensor bias, data coverage, collection goals, and future experimentation. These recommendations allow MnDOT to move forward in the project knowing that testing procedures are statistically justified.