Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm

Abstract

Basalt fiber is a green non-polluting material with strong tensile mechanical properties. In this paper, the strength prediction model of basalt fiber concrete is constructed by random forest method based on experiments to study the strengthening effect of basalt fiber in concrete composites and explore the impact of different fiber sizes and fiber content on the strength of basalt fiber concrete. First, the compressive strength tests of BFRC under different stress states were performed at fiber volume fractions of 0.2%, 0.4%, 0.6% and fiber lengths of 6 mm, 12 mm and 18 mm to obtain stress–strain curves and peak compressive strength. Then, 70% of the original test data is used to establish a random forest training sample set, and the remaining 30% is used as a test set. According to the error outside the bag, the appropriate number of decision trees and leaf nodes are selected, and the influencing factors are ranked by importance. Then use the Pearson correlation diagram to analyze the correlation of each influencing factor, establish a random forest training model, and output the fitting prediction results of the model training set and the prediction set. Finally, the prediction results of random forest with BP neural network and support vector regression are measured by MSE, RMSE, and R2 evaluation metrics for performance. The results show that the random forest prediction model has a good model fitting ability. Compared with other algorithms, the accuracy of the RF model in the MSE index is increased by 8% and 18.8%, which further verifies the accuracy and reliability of the model.