Influence of underground structures and infrastructures on the groundwater level in the urban area of milan, italy
Bibliographic record
Abstract
Underground structures and infrastructures (i.e. metro tunnels and stations, deep foundations, etc.) locally affect the groundwater level of the aquifers in urban area, and they can bring about hydrogeological hazards especially in areas also interested by a regional raising trend of the water table. In this paper, the case of Milan (Italy) is discussed by means of the following steps: (1) monitoring data analysis for reconstructing the regional increasing trend of the water table ; (2) numerical modelling of the groundwater flow system in steady state; (3) numerical modelling of the interference of underground structures and infrastructures with the aquifer system. More in detail, the study started from a numerical model of the aquifer system of Milan, calibrated in steady state with reference to the maximum water table observed in May 2014. Afterwards, a pilot sub-model of about 9 km 2 was refined to simulate the major intersections between the metro lines and the stations. Modelling results pointed out a local increasing of the groundwater levels of about 10-15 cm over wide areas located nearby the tunnels intersections. Unfortunately, these areas coincide with those areas interested by the highest increase in water table due to the regional trend. Therefore, even if the local changes are quite low, they can negatively affect underground structures and buildings because of the superimposition of effects.
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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 | 0.001 |
| 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 | 0.001 |
| 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".