Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures

作者：Hany A. Dahish, Ahmed D. Almutairi · 发表于：Results in Engineering · 年份：2025 · DOI：10.1016/j.rineng.2025.103975 · 被引用次数：23 · 研究领域：Fire effects on concrete materials、Innovative concrete reinforcement materials、Concrete and Cement Materials Research

• M5P, RF, and LR are used to predict the strength of NMC under elevated temperatures. • RF-developed model outperformed LR and M5P models in terms of predictive accuracy. • Predicted results validated through k-fold-cross-validation using statistical metrics. • SHAP analysis reveals the correlation between input parameters and NMC strength. The addition of nanomaterials to concrete is widely employed in modern construction to improve its durability and mechanical properties. In the present study, two machine learning algorithms, random forest (RF) and M5P decision tree, and linear regression were used for developing prediction models for the compressive strength (CS) of concrete containing nano alumina (NA) and carbon nanotubes (CNT) and being subjected to elevated temperatures. Datasets of 169 tested concrete specimens of 100 × 100 × 100 mm were gathered from the literature. Four variables were considered in the development of the prediction models, including temperature, exposure duration, and NA and CNT ratios. K-fold cross-validation was used to confirm the predicted output. The performance of the created models was compared to experimental data and earlier developed models: fuzzy logic models, artificial neural networks, genetic algorithms, and water cycle algorithms, using several evaluation metrics. The response surface methodology (RSM) was employed for optimizing the input parameters in order to achieve the maximum compressive strength of concrete containing NA and ...