Prediction of Soil Erosion Using 3D Point Scans and Acoustic Emissions

Abstract

Over half of the approximately 12,000 earthen watershed dams sponsored by the USDA have exceeded their planned 50-year service life. Age, land use changes, extreme weather events, structural deterioration, and sedimentation filling flood pools pose increased risks of dam incidents and potential failures. Among various mechanisms leading to integrity issues, soil erosion is of particular concern due to its potential to occur with little warning. The objective of this research is to determine if soil erosion can be predicted using acoustic emissions. A simulated dam overtopping experiment was replicated in a test flume with dimensions of 0.61 m by 4.27 m (2 ft. by 14 ft.) with a 13.7% slope and a 0.15 m (6 in) layer of inorganic clay (USCS CL) compacted at 17.4% moisture content. A constant flow discharge of 0.07 m3/s (2.37 cfs) was applied to induce erosion. The test was performed until complete failure of the test section occurred. Throughout the experiment, a sonar radar, a 3D scanning total station, and an accelerometer were used to monitor the water level, erosion levels, and vibrations, respectively. The frequency analysis of the water-induced vibrations was compared to measured erosion volumes to determine if in situ vibrations can predict erosion. The results revealed a linear relationship between erosion volume and time, with noticeable changes in the frequency domains as erosion progressed. The outcomes of this research have the potential to provide real-time insights into the integrity of earthen dams concerning erosion, offering a valuable tool for monitoring and maintenance.

Soil Modeling and Prediction Methods in Dredging Construction Areas

“…Ghaderi et al [3] proposed a new and optimized multi-output generalized feedforward neural network (GFNN) structure to generate a digital map of the soil types of southwest Sweden using 58 piezoelectric cone penetration test points (CPTus). Moreover, Cao et al [4] established a Bayesian framework for probabilistic soil stratification to identify soil, while Wise et al [5] proposed the use of a combination of 3D points and acoustics to predict soil erosion, aimed at three-dimensional point clouds. For noisy points in point cloud models, Zeng et al [6] used a point cloud adaptive weighted guided filtering algorithm to smooth out noise based on its characteristics, which can effectively preserve the key points of the point cloud.…”