Use of Artificial Neural Networks for Prediction of Properties of Self-Sensing Concrete
Bibliographic record
Abstract
Nanomaterials such as carbon nanotubes and carbon nanofibers are used as reinforcement for concrete, enhancing its compressive and flexural strength, and durability, and providing additional properties such as electrical conductivity.Hence, CNT/CNF reinforced concrete composite material is multifunctional material which may be used in structural capacity as well as structural health monitoring purposes.Although this material can be superior to traditional concrete, extensive and costly procedures of fabrication are hindering its practical potential.Concrete mix design methods are commonly used during the design of such composites, however, since these methods cannot give direct connection between the recipe and the end-product, every composite must be put through testing and iteratively adjusted until the appearance of wanted results.This paper proposes application of artificial neural networks for predicting properties of CNT/CNF concrete composite materials.Artificial neural networks in mix design have been developed for various types of concrete, commonly to predict only compressive strength as the primary property of concrete.However, self-sensing concrete is used primarily for its piezoresistivity and enhanced strength is only the consequence of the existence of nanofillers.Hence, the paper investigates prediction of compressive and flexural strength as well as electrical resistivity of 468 concrete mixtures by developing 3 different datasets comprehended by 6 ANN models.The models show some interesting results and point toward the necessity of further investigations on this topic and possible improvements.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".