Study on the Reasonable Alignment of the Steel Truss Concrete Composite Continuous Rigid Frame Bridge
Bibliographic record
Abstract
The alignment of steel truss concrete composite continuous rigid frame bridge (STCR in short) has a significant influence on the distribution of internal bending force, the research concerning the reasonable alignment, however, is very limited. In the negative bending moment section of STCR, the bottom chords are in compression state, in addition, the secondary moment caused by the joint stiffness of STCR is too significant to be neglected, therefore, these bottom chords are compression and bending members, which mechanical characteristics are somehow similar to that of the main arch of the open spandrel arch bridge, with the difference lies in that the bottom chord of STCR in the mid-span is in tension while the arch in the mid-span of open spandrel arch bridge is still in compression. On the basis of the method that determine the reasonable alignment of open spandrel arch bridge, a segmental pressure line method is proposed to determine the reasonable alignment of STCR: (1) Select an initial approximate reasonable axis for the bottom chord and a specific load condition, and calculate the internal force of members through finite element model analysis; (2) Select the compressed bottom chord as the research object, make the internal force of the adjacent members as external force to the research object and apply them to the corresponding position, repeat the iterative calculation, and get the discrete node coordinates that approximate the real pressure line; (3) Take the discrete nodes and the bottom chord node in the mid-span as the controlling nodes, using the curve fitting method to get the parabola alignment that can be applied to practical engineering. To validate this method, a detailed engineering application was introduced. Results of the example show that the proposed method is simple and efficient, it can significantly reduce the internal bending force of each member, and improve the internal force distribution state of STCR.
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 | 0.000 |
| 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".