Over the past five decades, the U.S. Army Corps of Engineers has been upgrading its projects by installing high-capacity, post-tensioned foundation anchors, typically with seven-wire strand cables. The purpose of these anchors has been to achieve structural stability for Corps hydraulic concrete structures (e.g., locks, dams, approach walls) and/or to remediate cracked concrete monoliths. Substantial improvements to protect multistrand anchor systems from corrosion have been made in the past five decades, but the corrosion of older multistrand anchorage systems is still a major concern.This report discusses a laboratory-testing program for the estimation of post-tensioning (PT), seven-wire strand cable strength as a function of corroded cross-sectional material loss. Pull tests were performed to gather reduced cable strength measurements. An innovative morphological procedure using digital photography was developed by U.S. Army Engineer Research and Development Center (ERDC) researchers for quantifying the cross-section geometrical properties of cables near their failure locations. The laboratory-testing program also included a successful series pull test to failure on pristine specimens for a control set of data, and the issues encountered are detailed. A statistical assessment of pull-test data to failure of pristine and corroded cables is used to establish a correlation between cross-section properties, corroded and pristine, and the cable strength.An overview of the corrosion process and the variables, ranked by contribution in Corps structures, which determine corrosion rate at each of the multistrand cables, is provided. Further, methods for estimating cable capacity under load were developed using the provided best-fit curves from the laboratory pull tests.

The increasing use of unmanned aerial vehicles (UAV) and high spatial resolution imagery from associated sensors necessitates the continued advancement of efficient means of image processing to ensure these tools are utilized effectively. This is exemplified in the field of forest management, where the extraction of individual tree crown information stands to benefit operational budgets. We explored training a region-based convolutional neural network (Mask R-CNN) to automatically delineate individual tree crown (ITC) polygons in regenerating forests (14 years after harvest) using true colour red-green-blue (RGB) imagery with an average ground sampling distance (GSD) of 3 cm. We predicted ITC polygons to extract height information using canopy height models generated from digital aerial photogrammetric (DAP) point clouds. Our approach yielded an average precision of 0.98, an average recall of 0.85, and an average F1 score of 0.91 for the delineation of ITC. Remote height measurements were strongly correlated with field height measurements (r2 = 0.93, RMSE = 0.34 m). The mean difference between DAP-derived and field-collected height measurements was −0.37 m and −0.24 m for white spruce (Picea glauca) and lodgepole pine (Pinus contorta), respectively. Our results show that accurate ITC delineation in young, regenerating stands is possible with fine-spatial resolution RGB imagery and that predicted ITC can be used in combination with DAP to estimate tree height.

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