Maintenance and repair of deteriorating civil infrastructure are global problems requiring significant attention and resources. Accurate measurements of civil infrastructure enable lower repair and rehabilitation costs if mitigation techniques are deployed at earlier stages of deterioration. This research describes an infrastructure inspection solution to scan concrete bridge decks for internal cracking at high speeds. Internal cracking within bridge decks, known as delamination, is a particularly difficult defect to identify because it is often not detectable through visual inspection. State-of-the practice testing approaches involve the use of slow and subjective manual sounding techniques and costly lane closures. The need for an improved testing approach has led to decades of research investigating the use of acoustic impact-echo testing to detect bridge deck delaminations. The research presented here consists of a study of the acoustic radiation patterns of delamination defects when they are impacted. Acoustic data were collected on an in-service bridge deck and compared to acoustic data collected on defects in decommissioned bridge deck slabs and on simulated delaminations. This study examined cases of ideal and non-ideal delaminations on the in-service bridge deck and identified characteristics of non-ideal delaminations. An apparatus consisting of a high-speed impact-echo platform and recording suite was designed and constructed. Using this towed apparatus, an order-of-magnitude increase in scanning speed was obtained over other reported methods. Significant design effort was employed to achieve synchronization between different sensing devices using networked computer systems. Analysis was also developed to process and automatically classify acoustic responses to determine the presence and location of delaminations. Demonstrated performance against ground truth data obtained on an in-service bridge deck includes an achievement of approximately 90% probability of detection with only a 2% false alarm rate within 0.30 m. Because of the need to classify acoustic data when ground truth may not be obtainable, a new outlier rejection algorithm, which robustly removes outliers for classification on both simulated and field test data, was also developed. These contributions advance state-of-the-art bridge inspection and also lay the groundwork for additional studies of bridge deck deterioration processes. The framework also demonstrates how a tedious, subjective, and manual inspection process can be automated using advanced excitation tools, signal processing, and machine learning.
| Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-9980 |
| Date | 10 April 2020 |
| Creators | Hendricks, Lorin James |
| Publisher | BYU ScholarsArchive |
| Source Sets | Brigham Young University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | https://lib.byu.edu/about/copyright/ |
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