Bio-Inspired Bridge Scour Countermeasures: Streamlining and Biocementation
Bibliographic record
Abstract
Bridge scour has long been identified as the major cause to bridge failures. This paper presents the results of an experimental study on the effectiveness of two newly-proposed scour countermeasures, namely streamlining and biocementation, which are inspired by nature. On one hand, borrowing ideas from the streamlined body shape of boxfish and blue shark, this study introduces streamlining features to bridge piers in order to reduce the flow intensities in the vicinity of bridge piers. Based on the numerical results of a pier streamlining optimization study previously conducted, four small-scale pier models with different streamlining levels were constructed using 3D printing techniques and flume tests were conducted to characterize the scour process around these models. On the other hand, microbial induced carbonate precipitation (MICP) is an emerging technique in geotechnical engineering. It precipitates carbonate that binds soil particles together and thus improves soil properties. In this study, a standard soil, Ottawa graded sand, was treated with bacteria (Sporosarcina pasteurii) in a full-contact reactor where the soil in a fabric mold was fully immersed in bacteria and cementation solution. The treated sample was tested in a flume to investigate the effectiveness of MICP on bridge scour control. The experimental results reveal that both streamlining and biocementation can significantly reduce or even fully prevent the scour around the model bridge piers under the laboratory testing conditions.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".