Impact of loaded trucks on highway infrastructure deterioration
Bibliographic record
Abstract
Impact of loaded trucks on our transponation infrastructure system is becoming a growing concern for many State Departments of Transportation (DOTs) in the U.S. An increasing volume of loaded trucks due to the implememation of the NAFTA is likely to make the situation worse for the NAFTA corridor states including Oklahoma, because the axle loads as well as gross vehicular weight limits for the Mexican and the Canadian trucks are much higher than the corresponding U.S. limits. This report attempts to document the effeet of truck axle load, gross vehicular weight, and traffic volume on major damage to pavements and bridges. The information was mostly assembled through a comprehensive literature search and contacts with several state agencies. Due to limited resources, scope, and time, no laboratory and field study and material testing were performed. The truck user fees and taxes in Oklahoma are not equitable with the damage these heavy vehicles cause to the transportation infrastructure. Over 92% of the total equivalent single axle loads (ESALs) on rural lnterstate highways are contributed by truck traffic. The corresponding average ESALs for rural and urban highways are over 80%. Fatigue damage is one of the most common distresses in both flexible and rigid pavements, although it is more predominant in rigid pavements. Fatigue damage in pavement is highly sensitive to the axle load (proportional to the fourth power). Due to a 10% increase in axle load, from the current limit of 20 kips to 22 kips, the fatigue damage is increased by 46% , thus significantly reducmg the remaining life of pavements. High axle loads in asphalt pavements drastically inciuse the rutting potential. The axle load magnitudes and frequency of truck traffic are largely responsible for faulting and pumping-induced deterioration in concrete pavemems. Heavy vehicles (over 7, 700 lb) are believed to be responsible for about 99 % of the total traffic-related damages in pavements. An 80 kips truck bas the same damaging potential as 9,600 automobiles. Also, the serviceability of pavements is shortened significantly by the action of heavy trucks. Overstress in bridge members due to the passage of heavy Canadian trucks was analyzed in this srudy. About 70 percent of the Oklahoma interstate bridges were found safe with respect m overstressing. Almost all concrete culvert and concrete girder bridges are not likely to be susceptible to overstress, while the majority of steel bridges may undergo significant overstressing due to the passage of the Canadian trucks. Increase of truck load and volume can also result significant fatigue damage to steel bridges and thus reduce the life.
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) | 0.002 | 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".