Bearing capacity under cyclic loading — offshore, along the coast, and on land. The 21st Bjerrum Lecture presented in Oslo, 23 November 2007This paper represents the written version of the 21st Bjerrum Lecture. While it has been edited for the present publication, it retains the general structure of the original lecture, which was intended for a general geotechnical audience. The Bjerrum Lecture is presented in Oslo in alternate years by the Norwegian Geotechnical Society with the support of the Bjerrum Memorial Fund (Laurits Bjerrums Minnefond).
Bibliographic record
Abstract
Cyclic loading can be important for the foundation design of structures, both offshore, along the coast, and on land, and for the stability of slopes. This is illustrated by several examples. The paper discusses how soil behaves under cyclic loading, both for structures and for slopes, and shows that the cyclic shear strength and the failure mode under cyclic loading depend strongly on the stress path and the combination of average and cyclic shear stresses. Diagrams with the cyclic shear strength of clay, sand, and silt that can be used in practical design are presented. Comparisons between calculations and model tests indicate that foundation capacity under cyclic loading can be determined on the basis of cyclic shear strength determined in laboratory tests.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".