Neural predictional of Mechanical Properties of Fiber-Reinforced Lightweight Concrete Containing Metakaolin at High Temperatures
Bibliographic record
Abstract
The fire hazard is a permanent threat to structures.Given the use of concrete in many structures, fire constitutes a considerable risk since it leads to a sudden collapse in these structures.If concrete made using Portland cement is subjected to heat, it experiences a number of transformations and reactions even in the case of a moderate temperature rise.Since aggregate occupies usually 65%-75% of concrete volume, the behavior of concrete at high temperatures is strongly dependent on the aggregate type.Therefore, the mix design of the fire-resistant concrete is of great importance.Obtaining a mix design capable of handling high temperatures requires the preparation of various concrete samples with different mix designs, which is both costly and time-consuming.Instead, simulating such tests in numerous iterations and performing the involved computations via simulation software results in cost savings and high accuracy.The present research used MATLAB modeling to obtain the compressive and tensile strengths of fiber-reinforced lightweight concrete containing metakaolin for various percentages of cement, gravel, sand, superplasticizer, and polypropylene fibers at high temperatures.The multilayer perceptron artificial neural network was employed for this purpose.The neural network was trained with 7 input layers, 2 output layers, and 1 hidden layer.As a result, it reached estimation accuracies of 99.06% and 97.16% for the training and testing data, respectively, and 99.05% for all the data, indicating the efficiency of the selected network.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".