Predicting residual strength of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) exposed to elevated temperatures using machine learning
Bibliographic record
Abstract
Hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) offers significant advantages over conventional concrete like increased ductility, crack resistance, and eliminating the need for compaction etc. However, it is very difficult to determine residual strength properties of HFR-SCC after a fire event since it requires rigorous experimental work and resources. Thus, this research presents innovative ways for reliable prediction of compressive strength (cs), flexural strength (fs), and tensile strength (ts) of HFR-SCC using different machine learning (ML) algorithms including gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), extreme gradient boosting (XGB), AdaBoost, and random forest regression (RFR). The data to be used for this purpose was obtained from internationally published literature having nine inputs including cement, fly ash, temperature, fibre content etc. and three output parameters i.e., cs, ts, and fs. The collected dataset was split into two sets named training and testing sets to be used for training the algorithms and testing their accuracy respectively. The developed predictive models were validated by error metrices including coefficient of determination ( R 2 ) , performance index (PI), and a20-index, etc. The comparison of the algorithms revealed that XGB surpassed its counterparts having testing R 2 values equal to 0.998, 0.997, and 0.999 for cs, ts, and fs prediction respectively. Also, the PI values were the lowest for XGB-based predictive model in both phases of training and testing. Thus, Shapely Additive Analysis (SHAP) was performed on the XGB model which revealed that temperature, fibre content, and cement are some of the main contributors to predict the three outputs. The developed predictive models presented in this study can be utilized effectively by the professionals to estimate the residual strength of HFR-SCC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".