Neural network modelling of properties of cement-based materials demystified
Bibliographic record
Abstract
Engineers often have to deal with materials of ill-defined behaviour such as cement-based materials in order to perform special design tasks. There is usually great difficulty in predicting the engineering properties of such materials due to various factors, including their non-homogeneous nature, their composite behaviour with dissimilar ingredients and sometimes the dual and/or contradictory effects of some components on the overall performance. Until recently, the methods used to predict the engineering properties of cement-based materials have been based mainly on statistical and mathematical models, which in turn are derived from human observation, empirical relationships and assumptions with limited ability to account for the effects of and interaction between all variables involved. An alternative approach, termed artificial neural networks (ANNs), has recently emerged in different engineering fields as a popular tool to predict the behaviour of materials. Due to the relatively recent adoption of ANNs for modelling the behaviour of cement-based materials, a good understanding of its fundamental basis and a critical assessment of its performance are essential. This paper examines the most widely used ANNs in materials modelling (the feed-forward, back-propagation (FFBP) neural networks). Guidelines for building, training, and validating such networks are provided. A critical assessment is presented of the effects of various parameters on the training and performance of FFBP networks and their use as an alternative approach to traditional modelling methods is evaluated through a case study. Recommendations are made to optimise the performance of ANNs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".