Implementation of Hyperbolic Load-Deformation Model in Reliability-Based Design (RBD) of Shallow Foundations Using Some In Situ Test Results
Bibliographic record
Abstract
Random variability of strength and deformation parameters in soil mechanics is an indispensable attribute of naturally occurred soil deposits. Substantiation of the spatial and random variability of the parameters in soil mechanics is often carried out using results of some in situ tests. The implication of such randomness of parameters on design of shallow foundation has always been under scrutiny by experts in geotechnical engineering discipline. Plate load tests provide semi-continuous load-displacement profiles, which, when represented holistically, reap dividends that go far beyond the bearing capacity estimations. Different load-deformation models have been discussed in literature; however, provide a more realistic model, one needs to consider the physical concepts governing the limit load bearing capacity nature of soils. This study unfolds some uncharted territories of shallow foundation design in the realm of load and resistance factor design (LRFD). A hyperbolic load-deformation model, instead of the well-established power series model, is corroborated to represent the actual load-deformation behavior of natural soil deposits. To this end, some plate load tests, coupled with cone penetration tests (CPT), are invoked to establish a reliability based design (RBD) model, which enables serviceability limit state design of shallow foundations using Monte Carlo simulations. Finally, a load factor is proposed being predicated on the assumption of deterministic design approach. Results presented in this study can be directly implemented to shallow foundation design for both serviceability and ultimate limit states.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".