The Use of Wireless Geotechnical Instrumentation to Remotely Monitor Isolated Infrastructure and Manage Geohazard Risks
Bibliographic record
Abstract
After identifying active landslides within a geohazard-prone corridor, Duquesne Light Company (DLC), an electric utility serving southwestern Pennsylvania, received engineering recommendations to relocate four at-risk power transmission towers. These towers, vital for supplying power via two 138,000-volt transmission lines, were adjacent to, and potentially within, active landslides. Unable to relocate the towers immediately, DLC’s asset management group collaborated with a local geotechnical engineering firm that implemented an investigation program to monitor the towers for signs of unanticipated movement. The local firm collected subsurface data near the at-risk towers and installed instrumentation to detect ground and structural movement. The instrumentation included wireless in-place inclinometers (IPIs) in boreholes for ground monitoring and biaxial tilt meters (TMs) attached to the towers for structural assessment. Over a two-year period, DLC utilized an online dashboard for near real-time data monitoring while planning tower relocation. The system demonstrated its effectiveness during a heavy rain event caused by Hurricane Ida. While IPIs registered slope movement below a crucial tower, TMs indicated no structural displacement, allaying immediate concern that ground movement had impacted the tower. This case study examines the original tower engineering design philosophy, the current geological challenges, the risk assessment during the geotechnical investigation, and the lessons learned from the monitoring system’s design, implementation, and outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".