Stress–Strain Prediction for Steam-Cured Steel Slag Fine Aggregate Concrete Based on Machine Learning Algorithms
The utilization of steam-cured steel slag fine aggregate concrete (SC) faces challenges in accurately predicting its stress–strain relationship. The mechanical properties of steam-cured SC and its stress–strain relationship have been systematically investigated through combined tests and machine lea...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/11/1817 |
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| Summary: | The utilization of steam-cured steel slag fine aggregate concrete (SC) faces challenges in accurately predicting its stress–strain relationship. The mechanical properties of steam-cured SC and its stress–strain relationship have been systematically investigated through combined tests and machine learning (ML) approaches. The results showed that steam curing at 50 °C greatly increased the peak stress and ductility of SC. Specimens, the steel slag fine aggregate (SA) content of which was 40% by volume, and which were subjected to steam curing at 50 °C, exhibited superior mechanical and deformation properties. The prediction performance of three ML models—random forest (RF), backpropagation neural network (BPNN), and support vector regression (SVR)—was compared based on the test data. The analysis results revealed that the RF model achieved optimal performance (R<sup>2</sup> = 1.00), whereas the SVR model underperformed overall. Through the transfer validation method, it was found that the BPNN model, after parameter optimization, demonstrated a superior generalization ability in cross-mix-proportion predictions. It exhibited satisfactory prediction stability for steam-cured SC with an untrained mix proportion. In contrast, the RF model tended to overestimate peak stress. The theoretical reference for realizing the comprehensive utilization of steel slag in precast concrete components has been provided. |
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| ISSN: | 2075-5309 |