The Use of Fuzzy Linear Regression with Trapezoidal Fuzzy Numbers to Predict the Compressive Strength of Lightweight Foamed Concrete
Bibliographic record
Abstract
Lightweight foamed concrete is defined as one of the most broadly implemented sustainable material in the construction of buildings.Due to its properties, it has been commonly applied in structural design providing energy conservation and excellent durability and functional properties.This paper describes the characteristics of lightweight foamed concrete and its properties for application in constructions.Also presents the prediction of its compressive strength by using Fuzzy Linear Regression (FLR) method with trapezoidal fuzzy numbers.Particularly, many approaches were applied in calculating the compressive strength of foamed concrete, such as multivariable nonlinear regression method, single or hybrid machine learning models and FLR method with trapezoidal fuzzy numbers.By applying them and analyzing the calculated values, it was concluded that although the last method did not have the smallest predictive accuracy criteria among the other methods, it provides a specific relation to calculate the compressive strength.In contrast to the other black box methods, FLR method with trapezoidal fuzzy numbers can be proposed as an efficient modelling tool in construction industry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".