Live Load Distribution Factor for Highway Bridges Based on AASHTO-LRFD and Finite Element Analysis
Bibliographic record
Abstract
The AASHTO-LRFD live 2load distribution factors for highway bridges show significant change compared to the standard AASHTO that have been used for the last 50 years. This paper presents a comparison between the moments distribution factors of concrete bridges due to live load calculated in accordance with the current AASHTO-LRFD (2004) formulas and finite-element analysis. Several three-dimensional linear elastic models were built using the structural analysis program SAP2000 to obtain the most accurate method to model the bridge superstructure. The bridge deck was modeled as quadrilateral shell elements and the girders as space frame elements. The live load used in the analysis is the vehicular load plus the standard uniform lane load as specified by AASHTO-LRFD. The live load is positioned at the longitudinal location that produces the maximum moments, then the load is moved transversely across the bridge width in order to investigate all possibilities of bridge loading (one, two and three lanes loaded). In this comparison the range of applicability specified by the AASHTO-LRFD is fully covered in terms of span length, slab thickness, girder spacing and longitudinal stiffness. One parameter is considered at a time while the remaining parameters are fixed. All the AASHTO-PCI girders (Type I to VI) are considered to cover the complete range of longitudinal stiffness specified in the AASHTO-LRFD specifications. The results of this study are presented in format of graphs and some recommendations for specific bridge geometries are presented.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".