Effects of Pavement Roughness and Dynamic Tank Load on the Bridge Response
Bibliographic record
Abstract
This study aims to investigate the dynamic Load allowance variation (DLA) of dynamic tank Loads and compare it with Iraq's standard specifications for road bridges. DLA is considered a simple measurement of the dynamic variation magnitude of the tank load for a specific combination of road roughness and speed. In addition to determining the stochastic dynamic response of the bridge. Al-Awsej bridge in Iraq, with a span of 33.2 m and a principal road with four pavement roughness classes (very good, good, average, and poor), was proposed as a case study in this analysis. A spectral closed-form solution was used for evaluating the dynamic tank load due to the passage of a tank type-72A at a constant speed of 40,50,60 and 70 km/hr along a bridge with different types of rough pavement surface. The results show the less value of DLA for very good pavement at 40 km/hr is about 0.032, and the largest value is about 0.293 at 70 km/hr for poor pavement. Also, road surface roughness greatly influences vehicle-bridge interactions and bridge responses. Where at 40 km/hr, the Root mean square of bridge deflection range from 0.31 to 2.75 mm and 0.39 to 3.14 mm at 70 km/hr.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".