Nature-Inspired Bridge Scour Countermeasures: Streamlining and Biocementation
Bibliographic record
Abstract
Abstract Bridge scour has long been identified as the major cause of bridge failures. Bridge scour refers to the loss of sediment around bridge foundations, and it occurs when the erosive force from the flow exceeds the resistance from the soil. This article presents an experimental study on the effectiveness of two nature-inspired countermeasures for scour control and prevention, namely, streamlining and biocementation. On one hand, inspired by the streamlined form of the boxfish and the blue shark, this study introduced streamlining features (i.e., sloped nose and concaved sidewalls) for bridge piers in order to reduce the erosive forces in the vicinity of the piers. On the other hand, inspired by the natural process of microbial-induced carbonate precipitation (MICP) in soil, a polymer-modified MICP method is developed to “cement” the coarse-grained sand in order to increase the erosion resistance. Accordingly, two series of experimental tests were conducted to evaluate the performance of these two countermeasures: (1) based on the numerical results of a pier streamlining optimization study, 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; (2) Ottawa graded sand treated via the polymer-modified MCIP method was tested in the flume to investigate its effectiveness on bridge scour control. The experimental results revealed that both streamlining and biocementation could significantly reduce or even fully prevent the scour around the model bridge piers under the laboratory testing conditions.
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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) | 0.001 | 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".