Temperature Effects on Concrete Slab on Steel Girder Bridges with Malfunctioning Expansion Joints
Bibliographic record
Abstract
For both the design of new bridges and the evaluation of existing bridges, the quantification of the effects of thermal loads on structural behaviour is an important factor that needs to be taken into account. With the projected increase in average temperatures in Canada due to climate change, combined with deterioration, the demand on bridge infrastructure is expected to increase. Among the various deterioration issues in bridges, one of the most common is the malfunctioning of expansion joints. Due to the accumulation of debris and dirt in the joint, the axial movement of the bridge superstructure may be restrained, leading to axial forces at levels that the structural members are not originally designed for. It was also reported in the literature that the increasing temperature due to climate change may accelerate the overall deterioration rates and that of the expansion joints. This paper outlines a preliminary study on impacts of malfunctioning expansion joints on the structural behaviour and moment resistance of concrete slabs on steel girder (CSSG) bridges. A summary of temperature loads per the Canadian Highway Bridge Design Code (CHBDC) for various Canadian cities with different climates is first presented. A Finite Element Analysis (FEA) of a three-span continuous CSSG bridges is introduced and the results for axial load levels on this bridge with malfunctioning expansion joints are presented assuming that the bridge is situated at the location with the largest temperature loads among the locations considered. A brief literature review on the quantification of the moment resistance of CSSG bridges subject to interactions of axial force and shear is provided. Based on the FEA and literature review presented, future research considerations are proposed for temperature distribution and moment-shear-axial load interaction relationships on multi-span continuous CSSG bridges.
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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".