High-resolution time-lapse DC-IP imaging of a green infrastructure’s response to a flooding test and a heavy rain event
Bibliographic record
Abstract
Stormwater management is an escalating challenge in urban areas worldwide. Green Infrastructures (GIs), such as vegetated roadside areas with lowered curbs, are gaining recognition in Quebec as an effective solution to reduce the burden on urban sewage systems. However, the widespread use of de-icing salt during winter raises concerns about increased contaminant infiltration into the ground, potentially leading to infrastructure deterioration. Despite the growing adoption of GIs, a research gap persists regarding their impact on water and chloride infiltration.In this study, a 50m-long GI was equipped with a large range of hydrogeological sensors such as water content and pore pressure sensors, thermistors, barometer and piezometers equipped with level, temperature and conductivity loggers. In addition, a time-lapse electrical resistivity tomography (TL-ERT) monitoring system was installed to extend spatially and temporally the coverage of the hydrogeological monitoring of the GI. In total, 113 electrodes were installed in boreholes and connected to an autonomous resistivity meter. In this study, 64 electrodes located within and around a 6m × 1.8m grid were used to recover the spatial and temporal distribution of electrical resistivity perpendicularly to the GI.A controlled flooding test (CFT) using bromide salt as a saline tracer was conducted to evaluate the GI’s response to infiltration. A multi-method surveying and sampling program was implemented, integrating ERT and induced polarization (IP) geophysical measurements, hydrogeological monitoring (piezometers), and geochemical analyses (continuous groundwater sampling). In addition, a heavy rainfall event (HRE) was monitored using DC-IP surveys conducted every two hours. In total, approximately 90 DC surveys were completed during the CFT, with a temporal resolution of approximately 10 minutes and 50 DC-IP surveys were performed during the HRE, with a temporal resolution varying from 2 to 4 hours. This study presents geophysical imaging results from these events, showcasing time-lapse imaging interpreted using laboratory analyses of the soil in-situ. Preliminary results suggest that the geophysical results are consistent with hydrogeological and geochemical data, offering valuable insights into the 2D distribution and temporal evolution of water and chloride movement in and around the GI. These findings contribute to understanding the performance and potential limitations of GIs in mitigating stormwater impacts under saline conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".