Measuring the Risk of Geotechnical Site Investigations
Bibliographic record
Abstract
The site investigation phase of any geotechnical design plays a vital role, where inadequate characterization of the subsurface conditions may contribute to either a significantly over designed solution that is not cost-effective, or an under design, which may lead to potential failures. Although it is intuitive to expect that the financial risk of a design will reduce as the site investigation scope increases (i.e. additional sampling), it is not known to what degree the risk is reduced, nor whether other uncertainties have an impact on this relationship. As such, this paper discusses research to measure the impact of varying the scope of a site investigation, on the financial risk of a foundation design project. The financial risk is defined as the total cost, which includes costs associated with undertaking the site investigation, constructing the foundation and superstructure, and any works required to rehabilitate a foundation failure. The analysis is numerically based, where a foundation design simulation model is incorporated into a Monte Carlo framework, in order to generate expected costs, and a measure of the financial risk. Results indicate that the risk of a foundation design is considerably reduced as the scope of a site investigation increases. However, results also indicate that there is an optimal site investigation expenditure, which leads to the least financial risk, and where additional sampling becomes redundant.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".