Prediction models for the hybrid effect of nano materials on radiation shielding properties of concrete exposed to elevated temperatures

Abstract

In modern construction, nanomaterials can be added to concrete to improve its radiation shielding properties. A prediction model for the gamma-ray radiation shielding properties, such as linear attenuation coefficient (LAC) of nanomaterial-based concrete remains necessary. This study aims to develop prediction models for LAC of nano modified concrete (NMC) using nano alumina (NA) and carbon nano tubes (CNTs) subjected to high temperatures. These models are based on linear regression and three machine learning algorithms: M5 Prime (M5P), random forest (RF), and extreme gradient boosting (XGBoost). To build the models, results of 117 concrete cubic specimens (100 mm size) was utilized as a dataset with varying amounts of NA and CNTs. Relevant variables including temperature, exposure time, and NA and CNTs dosages were considered. To further validate the predicted results, various statistical metrics and K-fold cross-validation were employed to compare and validate the models' output. The results showed that the XGBoost model outperformed other models, with the highest R2 of 0.9975 and the lowest error of 0.4365 %. In addition, the impact of each parameter on LAC of NMC was assessed using Shapley Additive Explanations (SHAP) analysis. Sensitivity and SHAP analyses reveal that CNTs has the highest influence on the radiation shielding properties of NMC. In comparison with the previously developed ANN model, the XGBoost and ANN models showed approximately the same prediction power for predicting LAC of NMC.

Keywords

• Machine learning
• Nanoparticle
• Radiation shielding properties
• Linear attenuation coefficient
• Concrete
• Elevated temperature