3DGPR for the Non-Destructive Monitoring of Subsurface Weathering of Sandstone Masonry
Bibliographic record
Abstract
Remote sensing techniques, such as LiDAR and photogrammetry, are used by researchers exploring the spatial distribution of weathering features in historic masonry. These well-established tools provide users with a perspective of the processes affecting the surface of masonry blocks; however, they cannot provide information on the alteration occurring subsurface. Geophysical tools are being explored as a potential approach to observe the variation in material properties beneath masonry block surfaces and to examine the patterns of deterioration across wall sections. Applying such techniques inform the development of conceptual models of weathering at the block to building wall scale. In this study, ground-penetrating radar (GPR) was selected to inspect the subsurface condition of the wall section of an historic church wall, where areas of granular disintegration and flaking can be observed. 3DGPR was selected for this task, as its use of regular grids during data collection make it better suited for detecting features within an area. Three high-frequency antennas, 1.2 Ghz, 1.6 Ghz and 2.3 Ghz, were run across the study area in a series of 80 cm by 80 cm grids. The data were collated within GIS, where observed features were annotated onto a schematic of the wall surface. The 3DGPR outputs identified anomalies within this structure that could not have been as easily interpreted using a 2DGPR transect. However, as 3DGPR relies upon interpolative techniques to estimate the returns between observation transects, the validity of features detected in these locations need to be tested. The results of this application of 3DGPR identified variable weathering response across the wall section, relative to elevation. These observations were used to develop a conceptual model linking these findings to seasonal variation in the capillary rise of groundwater, upward from the base of the church wall. Through these findings it is possible to see how GPR can assist in developing our understanding of the processes threatening heritage buildings.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".