Application of Artificial Intelligence Technologies in Concrete and Nanomaterials
Bibliographic record
Abstract
With the continuous adjustment of contemporary technology, artificial intelligence has gradually become irreplaceable. The main goal of the paper is to discuss the core of artificial intelligence - machine learning and its application in basic concrete materials and nanomaterials. Concrete is a kind of mixed material with high hardness, high compressive strength, low cost and easy production; Nanomaterials, whose properties are mainly determined by quantum mechanics, consist of a powdery or agglomerated natural or artificial material composed of basic particles. With the continuous advancement of modern technology, machine learning algorithms are gradually replacing previous technical algorithms such as manual algorithms, and becoming a very important tool in the engineering field. However, with the increasing complexity of various new materials, some materials such as nanomaterials have still not fully mastered the application methods of machine learning data analysis, therefore they have some limitations on their use. In this work, the impact and role of machine learning on material applications are discussed. Data patterns learned by humans are entered into machine learning, and high-dimensional data are easier to analyze. Machine learning has brought the development of concrete and nanotechnology in the field of materials to a new level, and a new milestone has come for technology to replace labor.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".