Flexural and split tensile strength of concrete with basalt fiber: An experimental and computational analysis

• Published in: Construction & building materials 2024-02, Vol.414, p.134936, Article 134936
• Main Authors: Almohammed, Fadi, Thakur, M.S., Lee, Daeho, Kumar, Raj, Singh, Tej
• Format: Article
• Language: English
• Subjects: Artificial neural network; Bagging; Basalt fiber; Flexural strength; Random forest; Random tree; Split tensile strength; Stochastic
• ISSN: 0950-0618
• DOI: 10.1016/j.conbuildmat.2024.134936

The objective of this study is to create comprehensive multiscale models for predicting the split tensile strength (STS) and flexural strength (FS) of basalt fiber-reinforced concrete, with the intention of its use in the construction industry. To meet this objective, STS and FS data were collected from various academic research investigations. The collected data was further expanded by experimentally analyzing the FS and STS of basalt fiber (0.1 %, 0.2 %, 0.3 %, and 0.4 % by volume)-loaded concrete. The experimental investigation shows FS and STS increased with basalt fiber loading. The highest FS was 5.11 MPa (0.4 % basalt fiber), and the highest STS was 2.24 MPa (0.3 % basalt fiber). The collected STS (114 data) and FS (103 data) datasets were statistically examined and modelled using soft computing techniques. Seven soft computing techniques, namely Random Forest (RF), Stochastic Random Forest (Stochastic-RF), Random Tree (RT), Stochastic Random Tree (Stochastic-RT), Bagging Random Forest (Bagging-RF), Bagging Random Tree (Bagging-RT) and Artificial Neural Network (ANN) were used to predict the FS and STS of basalt fiber-reinforced concrete using nine inputs and two outputs. The accuracy of the models was evaluated using comprehensive measure (COM), correlation coefficient (CC), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE), Nash-Sutcliffe efficiency (NSE), and root mean square error (RMSE). The Stochastic-RT model had better predictive ability for FS, as seen by its lowest COM value of 0.121, and optimal values for CC, MAE, RRSE, NSE, RAE, and RMSE. Conversely, with a minimal COM value of 0.0279, the Bagging-RT model showed better accuracy in predicting the STS of concrete. Furthermore, sensitivity analysis revealed that basalt fiber length has the most significant effect on FS prediction, whereas curing time is the most crucial element influencing the STS of the concrete. [Display omitted] •Prediction of FS and STS of basalt fiber reinforced concrete using machine learning models.•FS and STS databases were collected from literature and experiments (@0.1%-0.4% basalt fiber loading).•Seven models (ANN, RF, Stochastic-RF, Bagging-RF, RT, Stochastic-RT, Bagging-RT) were compared.•Stochastic-RT and bagging-RT models were best for FS and STS predictions, respectively.