Using Public Well Data Banks to Improve Field Investigations for Excavations
Bibliographic record
Abstract
ABSTRACT ASTM D420-18, Standard Guide for Site Characterization for Engineering Design and Construction Purposes, states the need “to identify and locate, both horizontally and vertically, significant soil and rock types and groundwater conditions.” Knowing the hydraulic properties of soils and rock is vital for excavations. The project engineers and contractors should have information about expected pumping rate (Q), hydraulic conductivity (K), drawdown, and risks of instabilities. This information is often limited. Experience shows that many engineers and contractors do not consult public data banks for wells, which contain useful but frequently unused information, such as Q values at existing wells near the project. For soils, there are reliable methods to predict the K value. For rocks, the K value is difficult to predict and field results are often highly variable and poorly related to field conditions in excavations. However, a mean K value may be estimated from the specific capacity (SC) value at each tested pumping well (PW). This article presents new practical findings for local correlations between transmissivity and SC, after making a synthesis of over 100 publications. It explains how to derive useful statistics for the Q values distribution and the relative performance of drilling methods, which is rock-specific. This information is a key addition to a field investigation for all professionals involved in a project, especially contractors who have to install dewatering systems for temporary and permanent excavations.
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How this classification was reachedexpand
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 | 0.000 | 0.005 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".