Experimental Investigation of Steel Fiber Reinforced Concrete as Supplemental Reinforcement in Bridge Decks
Bibliographic record
Abstract
Research on fiber reinforced concrete has been ongoing since the 1960’s. The current state of\nknowledge of SFRC demonstrates the benefits of the fibers in regards to the ductility, energy\ndissipation, and strength improvements of the reinforced concrete matrix. However, further\nvalidation of SFRC as supplemental or alternative reinforcement needs to be conducted before\nbridge deck designs using SFRC may be utilized. The interaction between FRC and steel\nreinforcement needs to be identified as current practice in the United States and Canada require\npositive reinforcement as an anti-progressive collapse mechanism regardless of the\nreinforcement or concrete matrix detailing. Low dosages of fibers could also be used to satisfy\nservice requirement while steel reinforcement satisfies the strength requirements. As a result,\nthe behavior of a SFRC deck under service conditions must also be investigated. The lack of\npractical, large scale experiments and a simple and reliable method for accurately determining\nthe tensile residual stress for SFRC has prevented the adoption of building codes regarding SFRC\nin the United States.\n\nThis research identified the possible use of steel fiber reinforced concrete (SFRC) in bridge decks\nas supplemental reinforcement. A case study analysis was conducted to identify how the\nperformance of SFRC was influenced by the bridge deck geometry, steel reinforcement ratio, and\nSFRC residual strength. The analysis shows that the addition of fibers permits a reduction of\ntraditional steel reinforcement while achieving design requirements. Findings of the theoretical\nstudy was verified in an experimental parameter study of individual SFRC slab-strips subjected to\nfour point bending tests. The slab-strips investigated the depth of the bridge deck, the ratio of\nsteel reinforcement, and its location in the cross-section. The load-deformation and crack\npatterning of the slab-strips describe the strength and failure behavior of SFRC. A full-scale bridge\ndeck was designed and tested using AASHTO LRFD design procedures and the results of the slab-strip tests. A yield line analysis was conducted on the bridge deck results to show the benefits of SFRC with respect to theoretical predictions. Based on the results of each phase of testing, design recommendations are provided to predict the capacity of SFRC slabs.
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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".