Road Failure Investigation Using Augmented 2-D Resistivity Survey
Bibliographic record
Abstract
Summary Electrical Resistivity Imaging, ERI, is the most often used geophysical method for engineering applications due to the direct relation between electrical resistivity and shear strength of subsurface materials. However, the cost of resistivity imaging equipment is limiting the applications of ERI in developing countries. This limitation is overcome in this study where an augment 2D resistivity array and imaging technique is achieved using traditional 1D Earth resistivity equipment to investigate the principal cause of road failure and potholes development on the University of Ilorin Teaching Hospital Road in Nigeria. The augmented array produced 2D models of the subsurface along the road, revealed low resistivity breakouts in the top 2 m. saturated weathered rock, elongated vertical structures, and competent basement rocks were interpreted in the deeper section. The lateral positions of the low resistivity breakouts correspond to the potholes on the road. The elongated vertical structures were interpreted as weak-zones/brittle structures. High moisture content in some sections of the topsoil/subgrade layer is the principal cause of road failure. The study showed that the augmented 2D survey is appropriate for road investigation and it therefore removes the barrier created by the unaffordability of the equipment and its use in underdeveloped/developing countries.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".