Application Potential of Ultra-Wide Band Radar for Detecting Buried Obstructions in Construction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The need of sustainable urbanization drives construction engineers to explore the underground space to improve quality of life, meet with the challenges of population growth, and satisfy increasing infrastructure demands for utility pipelines and subways. However, unexpected obstructions or heterogeneous ground conditions make underground construction a risky operation, increasing construction cost while presenting additional safety hazards. Examples include (1) high pressure underground water stops a tunnel boring machine (TBM); (2) breaking an existing water main leads to flooding the downtown area; and (3) hitting an unexpected gas line causes an explosion. To a certain extent, all of those accidents can be attributable to the lack of cost-effective technology for detecting buried underground obstacles. The current practices such as geotechnical test holes and ground penetration radars (GPR) have their limitations in revealing underground situations. Meanwhile, the emerging technology of ultra-wide band (UWB) radar holds the potential to provide a cost-effective, non-destructive detection method. In this paper, a critical review of current practices and established methodologies is given. The functionality, working mechanism and application potential of UWB radar technologies in underground construction are described. Preliminary lab testing results are presented. Research findings are summarized and further research plans are discussed in conclusions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it