SummaryNavigation locks are critical infrastructure components, and their closure for maintenance and repair can have significant impacts on the global economy. The current state of inspection and monitoring of lock components is generally to close the lock and perform a visual inspection. Whereas structural health monitoring of navigation locks is gaining acceptance, automation of the structural health monitoring process is lacking. This paper reports on efforts to develop an automated damage detection system for miter gates of navigation locks. The study focuses on using strain gage measurements to identify the redistribution of load throughout lock gates in the presence of damage. To eliminate the environmental variability in the data, a new damage-sensitive feature is introduced, termed here as "slope" and defined as the derivative of the strain with respect to the water levels in the lock chamber. The slopes form a new, stationary time series effectively purged of environmental effects. A principal component analysis, a method of analyzing multivariate, stationary time series, is then used to detect significant changes in the statistics of slopes as an indication of damage. To validate the approach, damage is simulated in a finite element model, and the resulting changes in strain from the finite element model are superimposed on the measured data. The results demonstrate the potential of the proposed approach for detecting damage in navigational lock gates.
The U.S. Army Corps of Engineers (USACE) operates and maintains 236 lock chambers at 191 lock sites on 41 waterways throughout the contiguous United States. Waterway navigational locks are important parts of the nation's infrastructure. Locks enable the flow of billions of dollars of commerce and support efforts for flood control. Proper maintenance of the locks and early detection of damage is crucial; however, due to shrinking budgets, adequate funding to apply traditional scheduled maintenance and visual inspection is not available. Structural health monitoring (SHM) systems have been considered to assist in establishing more efficient maintenance, repair, and replacement priorities for navigational locks. This work was undertaken to develop and implement a real-time methodology that provides lock operators with a robust, accurate warning system of gap(s) at the gate-to-wall interface. This initial effort, which focused on horizontally framed miter gates and on damage that is assumed to take the form of a gap at the gate/wall interface (quoin), developed a methodology to identify the occurrence of damage in miter gate structures using data from strain and water level gages that is collected continuously from the SHM system deployed by USACE. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.
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Research has been continually growing toward the development of image-based structural health monitoring tools that can leverage deep learning models to automate damage detection in civil infrastructure. However, these tools are typically based on RGB images, which work well under ideal lighting conditions, but often have degrading performance in poor and low-light scenes. On the other hand, thermal images, while lacking in crispness of details, do not show the same degradation of performance in changing lighting conditions. The potential to enhance automated damage detection by fusing RGB and thermal images together within a deep learning network has yet to be explored. In this paper, RGB and thermal images are fused in a ResNET-based semantic segmentation model for vision-based inspections. A convolutional neural network is then employed to automatically identify damage defects in concrete. The model uses a thermal and RGB encoder to combine the features detected from both spectrums to improve its performance of the model, and a single decoder to predict the classes. The results suggest that this RGB-thermal fusion network outperforms the RGB-only network in the detection of cracks using the Intersection Over Union (IOU) performance metric. The RGB-thermal fusion model not only detected damage at a higher performance rate, but it also performed much better in differentiating the types of damage.
The purpose of the effort described herein is to verify the reliability of the FLIR One Pro Gen 3 (FLIR One) unit through systematic experiments that compare the temperature perceived by the unit to the temperature measured by contact sensors on different materials through a range of temperatures.
in the Brown School of Engineering. Canek's research interests broadly relate to efforts to broaden participation in engineering. Currently, he is working on a project to improve mathematics education for visually impaired students.
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