Using Random Forest and Random Tree model to Predict the splitting tensile strength for the concrete with basalt fiber reinforced concrete

Abstract

The best model for predicting concrete splitting tensile strength using Basalt Fiber Reinforced Concrete (BFRC) was found utilising Random Forest (RF) and Random Tree (RT) implemented in this study. A total of 74 datasets were collected for this investigation from various academic papers. The entire data set is split into 51 training data sets and 23 testing data sets. The software which used for analysis in WEKA software. Cement, fine aggregate/crushed sand, coarse aggregate, water, superplasticizer, fly ash, Basalt Fiber Reinforced Concrete BFRC, diameter, length, and curing time are the input factors, while the compressive strength of the concrete containing BFRC is the output variable. Three performance assessment indices are used to assess the performance of the created models: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC). Containing CC (0.9889, 0.9579) and lower MAE (0.0886, 0.1842), RMSE (0.1430, 0.2406) for the training and testing data sets, the Random Forest was shown to be the best model for predicting the splitting tensile strength of concrete with BFRC. And by analysing the sensitivity it shows that the curing time is the most sensitive input among all the inputs.

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

“…Zhang et al [46] also predicted the mechanical properties of composite laminate using a random forest model, and the results showed that the model was able to arrive at an accurate prediction in a shorter time. Similarly, researchers have used random forest regression to predict the mechanical properties of composites [13,47].…”

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach

“…According to statistical metrics, LSTM-CHOA outperformed the other three models in predicting the kerf quality characteristics of cut composites. Almohammed and Soni [ 57 ] used Random Forest (RF) and Random Tree (RT) for predicting concrete splitting tensile strength using basalt fiber-reinforced concrete. Random Forest showed the best accuracy for predicting the splitting tensile strength according to performance metrics.…”