Dynamic bridge-vehicle interactions
Bibliographic record
Abstract
The interaction between vehicles and bridges remains a complex yet important concept in the assessment of dynamic loading on existing structures. The dynamic impact of vehicular loading on a structure is typically accounted for in the assessment procedure by the application of a dynamic load allowance (DLA) factor to the assessment load, with a factor of 0.4 specified in the Australian bridge design code AS 5100. This factor is historically based on empirical dynamic load test data that underpins the Canadian bridge design codes. The Queensland Department of Transport and Main Roads (TMR) has adopted the AS 5100 DLA factor in its base level, Tier 1 Bridge Heavy Load Assessment Brief (2013). However it is looking to develop a better understanding of a family of bridges for higher-order bridge assessments when adopting dynamic load factors, taking into account various vehicle and structure types and dynamic influences. This in turn may lead to a review of vehicle access and ensure efficient use of resources. To address these issues and to improve understanding on bridge-vehicle interactions, TMR has initiated a three-year research program in conjunction with ARRB Group. This paper presents the findings from the first year of the program. In summary, a detailed literature review has yielded valuable information pertaining to various factors influencing the assessment of bridge-vehicle dynamic interactions and the background to the adoption of the current DLA factors. A gap analysis has shown that very little practical information has been published regarding the dynamic impact of hydro-pneumatic cranes and road trains on bridges. A significant review of previous load test reports from national and international jurisdictions revealed that various structure types, vehicle types, and materials can influence the dynamic response of a structure to dynamic loads. Frequency matching between vehicles and structures can also result in significant load amplification. A recent load test on Canal Creek Bridge in Cloncurry, Queensland, using various vehicle types and suspensions supports these observations. Finally, a review of the viability and applicability of the development of a Vehicle-Bridge Interaction model for TMR use has been conducted. Additional field tests are scheduled as part of the program, with validation and calibration of models and experimental findings and recommendations to be completed in the final year.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".