New Developments in Multi-Sensor Condition Assessment Using LiDAR, Sonar, and CCTV
Bibliographic record
Abstract
This paper describes the development and successful applications of a closed circuit television (CCTV), LiDAR (Light + radar) and sonar based pipe inspection system that is robust to gather quantitative data for critical underground pipe condition assessment. The system that can be deployed on a ROV or on a float produces accurate cross-sectional analysis and sediment volume. This capacity is increasingly critical in large diameter pipes with high level of flow. The system employs a time of flight LIDAR that is sub cm accurate. Results from recent projects are discussed in detail. The North Surrey Interceptor in Surrey, British Columbia, Canada, is a critical line in the municipality’s wastewater system. This reinforced concrete box culvert is 1500 mm × 1750 mm, and often operates at full capacity. The owner has experienced failures on this pipe, and it was recently rehabilitated. The sonar results provided accurate sediment volumes and cross sectional restrictions. This information was used to infer the location of defects and gather the necessary information for a subsequent pipe penetrating radar (PPR) deployment. The TEES Tunnel in Tolo Harbor, Hong Kong, is a 7-km long, 3.18-m diameter reinforced concrete sewer tunnel. It has been in service for 15 years and was in need of a quantitative condition assessment. Due to safety concerns man entry was not an option. A long-range multi-sensor robot was deployed to traverse 1 km from both access portals and to gather CCTV and LiDAR data. Due to limits to flow diversion the project had to be completed in a 24 hr time frame. The CCTV and LiDAR data revealed quantitative information on the condition of this critical tunnel. Advanced pipe condition assessment technologies, such as the CCTV, LiDAR and sonar system described in this paper are cost-effective, non-destructive methods that are able to help better refine estimated remaining life of an interceptor, accurately determine overall severity of pipe degradation, as well as provide a basis for improved cost allocation and timing of rehabilitation efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".