Lateral bending strength of ultra-high performance concrete decked I-beams reinforced with high-strength and conventional carbon steel rebars
Bibliographic record
Abstract
Precast ultra-high performance concrete (UHPC) decked I-beam (DIB) was designed and constructed to align with the objectives of accelerated bridge construction (ABC) approach. Precast UHPC decked I-beam (DIB) aimed to improve both the service life of bridges and the process of construction. However, since this DIB is new, evaluating its structural performance is essential. The lateral bending strength of the web of UHPC DIB is crucial for ensuring resistance to applied forces during shipping, erection, and construction activities. Hence, this study assesses the lateral bending strength of the web of UHPC DIB specimens through experimental testing, considering both unreinforced and vertically reinforced configurations using either high-strength A1035 or conventional 400 W steel reinforcements. Additionally, finite element method (FEM) and fiber technique analysis were employed to analyze DIB sections with different dimensions and UHPC properties. The results showed that A1035-reinforced web components had slightly higher lateral bending capacity and smaller crack widths than those reinforced with 400 W rebar. This study also showed that unreinforced UHPC specimen exhibited an acceptable service lateral bending capacity. Additionally, fiber technique analysis revealed that UHPC sections with A1035 rebar exhibited greater reserve tensile strain capacity, and ductility loss could be minimized by using UHPC with higher ultimate tensile strain. • Experimental tests on UHPC decked I-beam (DIB) for accelerated bridge construction. • Use of concrete damage plasticity model for finite element analysis of UHPC members. • UHPC materials with higher ultimate tensile strain result in higher ductility member. • UHPC sections reinforced with conventional rebars exhibit higher ductility. • Multiple cracks with narrower width in UHPC reinforced with high-strength rebars.
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) | 0.001 | 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".