Fatigue cracks can develop in mechanical, aerospace, and civil engineering structures over time due to repetitive loads. Growing fatigue cracks could reduce the lifespan of the structure and lead to catastrophic collapse. Distortion-induced fatigue cracks are specifically concerning in steel bridges. Computer vision-based crack detection have shown great potential in crack detection for being robust and easy-to-deploy. A vision-based feature point tracking method measures the changes in surface motion to detect fatigue crack and performs well in the presence of other crack like features like corrosion marks, boundaries, etc. When the video is recorded using a moving camera like a handheld camera, unmanned aerial vehicle, and mixed reality headset worn by an inspector, feature point movement contains camera motion as well as the true object motion. To accurately detect cracks, feature point displacement needs to be free from camera motion. Distortion induced fatigue cracks occur in regions with complex geometries like web-gap regions in girder bridges. Due to parallax effects, a single geometric transformation is not enough to compensate the camera motion accurately in videos with such complex geometry. The bundled camera paths approach divides a video into multiple mesh grid cells and estimates motion in each cell individually. These camera paths are then optimized to remove camera jitters and rolling shutter effects producing stable video. However, the global camera motion is still present in the smoothed video. We have extended the bundled camera paths method to remove the global motion from the smoothed video. The proposed approach was successfully tested in a laboratory experiment to compensate camera motion and detect distortion induced fatigue cracks.
Integrating the interest of dancers in monitoring dance quality with the interest of engineers in monitoring loads and vibrations, this research studies floor vibrations induced by human activity while classifying and characterizing dances simultaneously. This research uses smart sensing capabilities with readily available low-cost Arduino sensors equipped with accelerometers. The paper describes the procedures to extract step features from the signal to categorically classify different dance steps. The major contribution of this work is to demonstrate that structural vibration can be used to classify dance steps and provide meaningful information about the harmony of the dances. The conclusion of this research accomplishes that using this new Cyber-Physical Systems (CPS), dancers’ performance can be objectively classified using floor vibration data.
New federal regulations now mandate North American railroad bridge owners to closely assess the structural capacity of
their bridges. Consequently, railroad companies are currently looking into developing and exploring monitoring systems
for specific bridges, to help them improve and develop bridge safety in order to help comply with this new rule. The first
part of this paper explains the significance of the new federal law. The new rule comes from the Federal Railroad
Administration (FRA), Department of Transportation (DOT), and it falls under the 49 Code of Federal Regulations
(CFR), Parts 213 and 2371. It requires railroad track owners to know the safe capacity of their bridges and to additionally
conduct special inspections if either weather or other exceptional conditions make them necessary to ensure safe railroad
bridge operations. The second part of this paper will cover past and current studies about the viability of bridge health
monitoring, and actual structural monitoring experiences for railroad bridges. Finally, lessons learned from these
monitoring examples, as well as recommendations for future applications, are suggested, including wireless monitoring
strategies for railroad bridges such as: campaign sensing inspections (periodic monitoring); bridge replacement
observations (short term monitoring); and permanent bridge instrumentation (long term monitoring).
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