Bio-Engineered Concrete: A Critical Review on The Next Generation of Durable Concrete
Bibliographic record
Abstract
Concrete is a prerequisite material for infrastructural development, which is required to be sufficiently strong and durable. It consists of fine, coarse, and aggregate particles bonded with a fluid cement that hardens over time. However, micro cracks development in concrete is a significant threat to its durability. To overcome this issue, several treatments and maintenance methods are adopted after construction, to ensure the durability of the structure. These include the use of bio-engineered concrete, which involved the biochemical reaction of non-reacted limestone and a calcium-based nutrient with the help of bacteria. These bio-cultures (bacteria) act as spores, which have the ability to survive up to 200 years, as they are also found to start the mineralization process and the filling of cracks or pores when in contact with moisture. Previous research proved that bio-engineered concrete is a self-healing technology, which developed the mechanical strength properties of the composite materials. The mechanism and healing process of the concrete is also natural and eco-friendly. Therefore, this study aims to critically analyze bio-engineered concrete and its future potentials in the Structural Engineering field, through the use of literature review. The data analysis was conducted in order to provide gradual and informative ideas on the historical background, present situation, and main mechanism process of the materials. According to the literature review, bio-engineered concrete has a promising outcome in the case of strength increment and crack healing. However, the only disadvantage was its less application in the practical fields. The results concluded that bio-engineered concrete is a new method for ensuring sustainable infrastructural development. And also, it indicated that more practical outcome-based analysis with extensive application in various aspects should be conducted, in order to assess the overall durability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".