Limit States Design (LSD) for Shallow and Deep Foundations
Bibliographic record
Abstract
Load and Resistance Factor Design (LRFD) shows promise as a viable alternative to the present working stress design (WSD) approach to foundation design. The key improvements of LRFD over the traditional Working Stress Design (WSD) are the ability to provide a more consistent level of reliability and the possibility of accounting for load and resistance uncertainties separately. In order for foundation design to be consistent with current structural design practice, the use of the same loads, load factors and load combinations would be required. In this study, we review the load factors presented in various LRFD Codes from the US, Canada and Europe. A simple firstorder second moment (FOSM) reliability analysis is presented to determine appropriate ranges for the values of the load factors. These values are compared with those proposed in the Codes. The comparisons between the analysis and the Codes show that the values of load factors given in the Codes generally fall within ranges consistent with the results of the FOSM analysis. For LRFD to gain acceptance in geotechnical engineering, a framework for the objective assessment of resistance factors is needed. Such a framework, based on reliability analysis is proposed in this study. Probability Density Functions (PDFs), representing design variable uncertainties, are required for analysis. A systematic approach to the selection of PDFs is presented. Such a procedure is a critical prerequisite to a rational probabilistic analysis in the development of LRFD methods in geotechnical engineering. Additionally, in order for LRFD to fulfill its promise for designs with more consistent reliability, the methods used to execute a design must be consistent with the methods assumed in the development of the LRFD factors. In this study, a methodology for the estimation of soil parameters for use in design equations is proposed that should allow for more statistical consistency in design inputs than is possible in traditional methods. Resistance factor values are dependent upon the values of load factors used. Thus, a method to adjust the resistance factors to account for code-specified load factors is also presented. Resistance factors for ultimate bearing capacity are computed using reliability analysis for shallow and deep foundations both in sand and in clay, for use with both ASCE-7 (1996) and AASHTO (1998) load factors. The various considered methods obtain their input parameters from the CPT, the SPT, or laboratory testing. Designers may wish to use design methods that are not considered in this
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".