Self-healing bacterial cementitious concrete composites: development and performance evaluation
Bibliographic record
Abstract
The principal objective of the research is to contribute towards attaining the goal of developing self-healing cementitious concrete composites by incorporating bacteria as healing agent. Since the root cause of the majority of structural failure is attributed to concrete cracking, there is a compelling economic incentive to develop a concrete that can treat and repair the damage all by itself. Even though some research has been carried out in this area, a major breakthrough in identifying the types of bacteria, modes to protect this bacteria from high pH concrete environment and nutrients for effective healing are yet to materialise. For the present study, three different bacteria namely, Sporosarcina ureae, Sporosarcina pasteurii and Bacillus subtilis subsp. spizizenii and two protective vehicles such as zeolite and pumice were selected to determine the best combination among them for self-healing. Normal and fibre reinforced mortar and engineered cementitious composite (ECC) specimens were employed for the study. In order to develop self-healing bacterial concrete based materials, it is crucial to understand whether the introduction of mineral producing bacteria and nutrients adversely affect the properties. Thus, various concentrations of bacteria and nutrients were tested to determine the best possible combinations without sacrificing concrete properies. Evaluation of healing effect was determined by comparing compressive strength, sorptivity and rapid chloride permeability (RCPT), four point bending and ultrasonic pulse velocity (UPV) properties of sound and damaged specimens at different ages. Healing associated with crack closure was visualised and analysed using scanning electronmicroscopy (SEM), Energy Dispersive Spectrum Energy (EDS) and X-ray diffraction (XRD) studies. Finally, an attempt was made to employ statistical models for parameter optimization of self-healing characteristics in terms of compressive strength, sorptivity, RCPT and UPV by design and analysis of experiments. Evaluation of results to determine self-healing efficiency indicated that a significant amount of self-healing was achieved by all three selected bacteria, out of which Sporosarcina pasteurii and Bacillus subtilis subsp. spizizenii found to be promising choices. Both zeolite and pumice turned out to be effective protective vehicles. Statistical modelling of the experiment proved to be the ideal choice for modelling self-healing characteristics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".