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Record W7002500810

New Truck Loading Model For Rural Bridges In Saskatchewan

2021· dissertation· en· W7002500810 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2021
Typedissertation
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsnot available
Fundersnot available
KeywordsTruckWork (physics)Bridge (graph theory)Tower
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT In rural municipalities in Saskatchewan, there are approximately 1700 bridges, many of which were constructed in the 1950s to 1960s and are therefore nearing the end of their expected service life. Evaluations for those bridges are therefore needed to make cost-effective decisions on rehabilitation or replacement to maintain the transportation network for the local regions. However, the lack of existing information on estimating the critical load effects occurring on short span bridges, on low traffic roadways, and over specific reference periods makes it difficult to perform the bridge evaluations with an adequate degree of confidence. This study focuses on aspects of the estimation of traffic load effects based on the use of a truck loading model to generate the nominal critical load effects in bridges. In order to be truly representative of actual traffic, the selected truck model must reproduce the critical static, dynamic and total load effects that may occur on rural bridges in Saskatchewan over specific reference periods. Since the traffic conditions in rural Saskatchewan differ from more populated regions, data used in the analysis for this study were all collected in the local regions. A probabilistic and statistical approach was used to introduce a new design truck model designated as SRL-895. Weigh-in-motion (WIM) data from six locations across Saskatchewan (SK) were evaluated and used to determine the most critical WIM station, which was then used in combination with traffic count data in rural areas in SK to simulate extreme truck loading configurations that caused extreme static load effects on bridges over a specific reference period. This new truck loading model provides bias factors that are much more consistent for various load effects, span lengths, and reference periods compared to those provided by the truck model specified in the Canadian Highway Bridge Design Code. The dynamic component of critical load effects measured from rural bridge tests was considered as a random variable in an estimation of the critical dynamic load effect over specific reference periods. This method improves the randomness of sampling from a bridge test. The inclusion of specific reference periods in the method used for the estimation of the critical dynamic load effects makes it consistent with that used for the estimation of the critical static load effects. Total critical load effects in bridge girders were estimated by combining simultaneously all relevant factors that have different contributions to the total load effect over a specific reference period, including static and dynamic effects, as well as the distribution of load effects across the width of the bridge. This method permits the determination of a live load factor that can be used for calculations of total load effect, instead of combining live load factor for calculation of critical static load effects and adding a separate dynamic load allowance as is specified in CSA S6-19. The emphasis of this study was on the development of a rational methodology for establishing a combination of site-specific traffic characteristics with field measurements in a rigorous and consistent statistical approach. The proposed method addressed several deficiencies that have been identified with methods currently found in the literature with the aim of having more accurate estimates for critical load effects in bridges based on available data sources for the local region under consideration.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.721

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0400.007

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.

Opus teacher head0.010
GPT teacher head0.203
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractno

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