Towards Autonomous Bridge Scour Monitoring Using an Unmanned Surface Vehicle and Multibeam Sonar
Bibliographic record
Abstract
Scour is among the most important underwater changes to monitor, as it is the leading cause of bridge failures worldwide. The most common method of assessing bridge scour is with visual inspections carried out by commercial divers, which are subjective and pose safety risks. Given the limitations of existing scour monitoring approaches, there is an opportunity to use autonomous robotic unmanned surface vehicles (USVs) equipped with multibeam echosounders to supplement current scour as well as other underwater monitoring approaches. In the first phase of this research, calibration testing was undertaken with a multibeam sonar in a laboratory facility using a model bridge pier as a ground truth. Precision was found to be the limiting factor when mapping target objects. The results indicated that the sensor was sufficiently accurate to capture a 300 mm deep erosion void with differences up to 18% for individual measurements. The sonar was integrated into a USV platform equipped with an inertial measurement unit (IMU) and a 3D light detection and ranging (LiDAR) scanner. A LiDAR-based simultaneous localization and mapping (SLAM) package was configured and tested in a wave basin under varying wave heights and periods to assess the accuracy of the system for applications in GPS-denied environments. Taller waves with shorter periods were found to have a greater impact on the measurement accuracy and precision of the proposed system. In the second phase, the USV system was used for field monitoring at two local bridges in Kingston, Ontario to map bathymetric features and to evaluate the system performance in both varying environmental conditions and in GPS-denied environments. The system was used to map 0.5 m tall bathymetric features at five locations, confirming previous findings using conventional measurement techniques. Wave conditions had a minimal impact on measurement accuracy and decreased precision by 6% over the 500 mm scale of interest. Wind and surface waves both degraded the USV’s ability to follow a planned path. The SLAM positioning method was able to localize a USV beneath a bridge with sufficient accuracy to conduct a bathymetric survey, provided that sufficient above-water features were mapped when calibrating the system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".