Response of multistory steel structure subjected to differential settlements of its foundation
Bibliographic record
Abstract
Purpose This article aims to address an outstanding problem dealing with the structure and its foundation. Design/methodology/approach Differential settlement between foundation units of a multistory structure has been responsible for serious damage to buildings and often catastrophic failure and loss of life. The dynamic changes in the loading conditions of the structure, and the variability of the underlying ground due to environmental changes, are causing the undesirable differential settlement, which is manifested in the form of additional stresses in beams, columns and distortion of the structure elements. The structural response to the differential settlements depends on the type of the structure (concrete or steel), type of beam-to-column connections (rigid or semi-rigid), number of floors and the spans of the beams in the building. This paper presents the results of a numerical model, which was developed using the finite element technique and the software “ABAQUS” to analyze a nine-floor steel structure. The model was capable to capture the stresses and the strains developed in beams and columns and the relationships of moment–settlement and rotation–settlement for the structural during the differential settlement of its foundation. After validating of the model, data were produced for a wide range of governing parameters for rigid and semi-rigid connections and accordingly the mode of failure. The results can be used as a guideline for the design of steel structures. Findings Results are useful for those design steel structures. Research limitations/implications This study is based on the experimental and numerical data of the authors. Practical implications This study provides a guideline for the design of steel structures. Originality/value This is the original research developed by the authors.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".