Bibliographic record
Abstract
Micropiles were first used to repair the damaged structures of “Scuola Angiulli” in Naples after World War II. They are known as small versions of regular piles, with a diameter of less than 30 cm, and are made of high-strength, steel casing and/or threaded bars, produce minimal noise and vibration during installation, and use lightweight machinery. They are capable to withstand axial loads and moderate lateral loads. They are used for underpinning existing foundations and to restore historical buildings and to support moderate structures. In the literature, several reports can be found dealing with micropiles, yet little has been reported on Micropiled-Raft Foundations (MPR). This technology did not receive the recognition it deserved until the 1970s when its technical and economic benefits were noted. A series of laboratory tests and numerical modeling were developed to examine the parameters governing the performance of MPR, including the relative density of the sand, the micropile spacing, and the rigidity of the raft. The numerical model, after being validated with the present experimental results, was used to generate data for a wide range of governing parameters. The theory developed by Poulos (2001) (PDR) to predict the capacity of pile-raft foundations was adopted for the design of MPR. The PDR method is widely used by geotechnical engineers because of its simplicity.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".