Ensemble Machine Learning approach for evaluating the material characterization of carbon nanotube-reinforced cementitious composites

Time and cost-efficient techniques are essential to avoid extra conventional experimental studies with large data-set for material characterization of composite materials. This study is aimed at providing a correlation between the structural performance and mechanical properties of carbon nano-tubes reinforced cementitious composites through efficient predictive Machine Learning (ML) models. The Flexural (FS) and Compressive (CS) Strength of Carbon Nanotube (CNT)-reinforced composites were predicted based on the data-rich framework provided in the litera-ture. Two different ensembled ML methods including Random Forest (RF) and Gradient Boosting Machine (GBM) were implemented on those data for predicting the CNT-reinforced cement-based composites. Data-set were utilized for training the proposed models through employing SciKit-Learn library in Python, followed by hyper-parameter tuning and k-fold cross-validation method for obtaining an optimum model to predict the target values. It was shown that the CS values predicted by the proposed models were more accurate than the FS counterparts and the developed model of GBM has less sensitivity to the alteration of test data than the proposed RF model. Finally, sensitivity analysis was conducted through Sobol algorithm and the parameters with highest contribution were identified.

Automated summary

This study provides a correlation between the structural performance and mechanical properties of carbon nano-tubes reinforced cementitious composites through efficient predictive Machine Learning models. Random Forest and Gradient Boosting Machine were implemented for predicting the properties, and the results show that the GBM model performs better.