Illinois-Specific LRFR Live-Load Factors Based on Truck Data
Bibliographic record
Abstract
This research project has a focus on the load and resistance factored rating (LRFR) live-load factors for load rating bridges in \nIllinois. The study’s objectives were to examine the adequacy of available Illinois weigh-in-motion (WIM) data and to develop \nrefined live-load factors for Illinois LRFR practice, based on recorded truck loads in Illinois. \nThere are currently 20 operating WIM sites in Illinois, each next to a weigh station. Initially, only one WIM site was providing two \nlanes of truck-weight data simultaneously recorded, while the remaining 19 were collecting data for the driving lane only. Twolane WIM data are important for live-load factor refinement because it is the cluster events involving trucks in different lanes \nthat induce maximum load effects in primary bridge components such as girders. Thus, such data are critical to live-load factors. \nUpon recommendation from this project, the capability of passing-lane recording was promptly added to two more of the 20 \nsites. An additional effort was made in this study to simulate the passing lane’s data for the remaining 17 sites, to maximize the \nuse of Illinois-relevant WIM data for covering the entire state. This simulation used the probability of multiple trucks in a cluster, \nbased on WIM data from eight states including Illinois. It also used truck-weight-demography information and headway distances \nof trucks in cluster from all available Illinois sites. This simulation method was tested and proven in the present project to be \nreliable for calibration here for Illinois. \nThe resulting truck records of these 17 sites and those recorded at the other 3 sites capable of providing two lanes of truckweight data from 2013 to 2017 were then used to develop refined live-load factors for LRFR in Illinois. Illinois trucks are seen in \nthese WIM data to be less severe than those weighed in Canada, which were used in calibrating the current AASHTO LRFD \nBridge Design Specifications (BDS) (2017). Illinois trucks recorded in the WIM data were also found to have behaved with little or \nno influence from the nearby weigh station. Four load-rating cases are addressed in this project in calibrating LRFR live-load \nfactors for Illinois: design load, legal load, routine-permit load, and special-permit load. Based on calibration using Illinois truckweight records, no change for the design load rating is recommended. Lower live-load factors are recommended for the other \nthree cases for Illinois than those prescribed in the current MBE, by about 8% to 14%, depending on average daily truck traffic \n(ADTT). Illustrative examples using the recommended live-load factors have been prepared and presented in this report. \nIt is also recommended that Illinois Department of Transportation (IDOT) continue to keep the WIM stations well-maintained, \nincluding periodical calibration of the weight sensors and systems; gather more truck-weight-data; review them at least \nbiennially; and focus on possible growth of truck load in both magnitude and volume. When funding becomes available, passinglane recording is recommended to be added to those WIM sites that currently do not have this capability. Truck-data gathering is \nalso recommended for sites where congested truck traffic is often observed, given adequate funding for such facilities.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".