Adjustment of Load and Resistance Factor Design Live Load Factors Using Recent Weigh-in-Motion Data
Bibliographic record
Abstract
Traffic loads on bridges exhibit significant variations regionally, from state to state, and from site to site. Accounting for actual live loads in the bridge design process is important to improving the overall reliability and safety of bridges. In some cases, the code-specified live loads may underestimate traffic loading on a bridge. The current load and resistance factor design (LRFD) live load calibration is based on a biased sample of truck weights collected as part of an Ontario, Canada, truck weight survey conducted in 1975. In the past 35 years, truck traffic has significantly increased in volume and weight, which may necessitate adjusting the LRFD live load factors in certain cases on the basis of current truck traffic conditions. Although the quality and quantity of traffic data being collected by highway agencies has improved since 1975, it has not been used to update the bridge design loads. NCHRP Project 12–76 was initiated in 2006 to develop a set of protocols and methodologies using recent truck traffic data to update live loads for LRFD bridge design. Various levels of complexity are available using the site-specific truck weight and traffic data to calibrate live load models. One simplified calibration approach focuses on the lifetime maximum live load for updating the live load model or the load factor for current traffic conditions. Another, more robust, reliability-based approach for calibration is proposed in the protocols. The models are applicable for the design of bridge members, for both ultimate capacity and cyclic fatigue, and are implementable for both main structural members and the design of bridge decks.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".